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  • 1.
    Abbas, Muhammad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalens University, Sweden.
    Ferrari, Alessio
    CNR-ISTI, Italy.
    Shatnawi, Anas
    Berget-Levrault, France.
    Enoiu, Eduard
    Mälardalens University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Is Requirements Similarity a Good Proxy for Software Similarity?: An Empirical Investigation in Industry2021In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 27th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2021, 12 April 2021 - 15 April 2021, Springer Science and Business Media Deutschland GmbH , 2021, Vol. 12685, p. 3-18Conference paper (Refereed)
    Abstract [en]

    [Context and Motivation] Content-based recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a new requirement is proposed by a stakeholder, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn identify previously developed code. [Question/problem] Several NLP approaches for similarity computation are available, and there is little empirical evidence on the adoption of an effective technique in recommender systems specifically oriented to requirements-based code reuse. [Principal ideas/results] This study compares different state-of-the-art NLP approaches and correlates the similarity among requirements with the similarity of their source code. The evaluation is conducted on real-world requirements from two industrial projects in the railway domain. Results show that requirements similarity computed with the traditional tf-idf approach has the highest correlation with the actual software similarity in the considered context. Furthermore, results indicate a moderate positive correlation with Spearman’s rank correlation coefficient of more than 0.5. [Contribution] Our work is among the first ones to explore the relationship between requirements similarity and software similarity. In addition, we also identify a suitable approach for computing requirements similarity that reflects software similarity well in an industrial context. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and categorization.

  • 2.
    Abbas, Muhammad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Ferrari, Alessio
    CNR-ISTI, Italy.
    Shatnawi, Anas
    Berger-Levrault, France.
    Enoiu, Eduard
    Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Sundmark, Daniel
    Mälardalen University, Sweden.
    On the relationship between similar requirements and similar software: A case study in the railway domain2023In: Requirements Engineering, ISSN 0947-3602, E-ISSN 1432-010X, Vol. 28, p. 23-47Article in journal (Refereed)
    Abstract [en]

    Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.

  • 3.
    Abbas, Muhammad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalens University, Sweden.
    Hamayouni, Ali
    Mälardalens University, Sweden.
    Helali Moghadam, Mahshid
    RISE Research Institutes of Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden.
    Strandberg, Per Erik
    Westermo Network Technologies AB, Sweden.
    Making Sense of Failure Logs in an Industrial DevOps Environment2023In: Advances in Intelligent Systems and Computing book series (AISC,volume 1445): 20th International Conference on Information Technology New Generations, Springer International Publishing , 2023, Vol. 1445, p. 217-226Conference paper (Refereed)
    Abstract [en]

    Processing and reviewing nightly test execution failure logs for large industrial systems is a tedious activity. Furthermore, multiple failures might share one root/common cause during test execution sessions, and the review might therefore require redundant efforts. This paper presents the LogGrouper approach for automated grouping of failure logs to aid root/common cause analysis and for enabling the processing of each log group as a batch. LogGrouper uses state-of-art natural language processing and clustering approaches to achieve meaningful log grouping. The approach is evaluated in an industrial setting in both a qualitative and quantitative manner. Results show that LogGrouper produces good quality groupings in terms of our two evaluation metrics (Silhouette Coefficient and Calinski-Harabasz Index) for clustering quality. The qualitative evaluation shows that experts perceive the groups as useful, and the groups are seen as an initial pointer for root cause analysis and failure assignment.

  • 4.
    Abbas, Muhammad
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Inayat, I.
    National University of Computer and Emerging Sciences, Pakistan.
    Jan, N.
    National University of Computer and Emerging Sciences, Pakistan.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Enoiu, E. Paul
    Mälardalen University, Sweden.
    Sundmark, D.
    Mälardalen University, Sweden.
    MBRP: Model-Based Requirements Prioritization Using PageRank Algorithm2019In: 2019 26th Asia-Pacific Software Engineering Conference (APSEC), 2019, p. 31-38Conference paper (Refereed)
    Abstract [en]

    Requirements prioritization plays an important role in driving project success during software development. Literature reveals that existing requirements prioritization approaches ignore vital factors such as interdependency between requirements. Existing requirements prioritization approaches are also generally time-consuming and involve substantial manual effort. Besides, these approaches show substantial limitations in terms of the number of requirements under consideration. There is some evidence suggesting that models could have a useful role in the analysis of requirements interdependency and their visualization, contributing towards the improvement of the overall requirements prioritization process. However, to date, just a handful of studies are focused on model-based strategies for requirements prioritization, considering only conflict-free functional requirements. This paper uses a meta-model-based approach to help the requirements analyst to model the requirements, stakeholders, and inter-dependencies between requirements. The model instance is then processed by our modified PageRank algorithm to prioritize the given requirements. An experiment was conducted, comparing our modified PageRank algorithm's efficiency and accuracy with five existing requirements prioritization methods. Besides, we also compared our results with a baseline prioritized list of 104 requirements prepared by 28 graduate students. Our results show that our modified PageRank algorithm was able to prioritize the requirements more effectively and efficiently than the other prioritization methods.

  • 5.
    Abbas, Muhammad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Inayat, Irum
    National University of Computer& Emerging Sciences, Pakistan.
    Jan, Naila
    National University of Computer& Emerging Sciences, Pakistan.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden.
    Enoiu, Eduard Paul
    Mälardalen University, Sweden.
    Sundmark, Daniel
    Mälardalen University, Sweden.
    MBRP: Model-based Requirements Prioritization Using PageRank Algorithm2019In: The 26th Asia-Pacific Software Engineering Conference, IEEE conference proceedings, 2019Conference paper (Refereed)
    Abstract [en]

    Requirements prioritization plays an important role in driving project success during software development. Literature reveals that existing requirements prioritization approaches ignore vital factors such as interdependency between requirements. Existing requirements prioritization approaches are also generally time-consuming and involve substantial manual effort. Besides, these approaches show substantial limitations in terms of the number of requirements under consideration. There is some evidence suggesting that models could have a useful role in the analysis of requirements interdependency and their visualization, contributing towards the improvement of the overall requirements prioritization process. However, to date, just a handful of studies are focused on model-based strategies for requirements prioritization, considering only conflict-free functional requirements. This paper uses a meta-model-based approach to help the requirements analyst to model the requirements, stakeholders, and inter-dependencies between requirements. The model instance is then processed by our modified PageRank algorithm to prioritize the given requirements. An experiment was conducted, comparing our modified PageRank algorithm’s efficiency and accuracy with five existing requirements prioritization methods. Besides, we also compared our results with a baseline prioritized list of 104 requirements prepared by 28 graduate students. Our results show that our modified PageRank algorithm was able to prioritize the requirements more effectively and efficiently than the other prioritization methods.

  • 6.
    Abbas, Muhammad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Inayat, Irum
    National University of Computer & Emerging Sciences, Pakistan.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden.
    Jan, Naila
    National University of Computer & Emerging Sciences, Pakistan.
    Requirements dependencies-based test case prioritization for extra-functional properties2019In: IEEE International Conference on Software Testing, Verification and Validation Workshops, IEEE , 2019Conference paper (Refereed)
    Abstract [en]

    The use of requirements’ information in testing is a well-recognized practice in the software development life cycle. Literature reveals that existing tests prioritization and selection approaches neglected vital factors affecting tests priorities, like interdependencies between requirement specifications. We believe that models may play a positive role in specifying these inter-dependencies and prioritizing tests based on these inter-dependencies. However, till date, few studies can be found that make use of requirements inter-dependencies for test case prioritization. This paper uses a meta-model to aid modeling requirements, their related tests, and inter-dependencies between them. The instance of this meta-model is then processed by our modified PageRank algorithm to prioritize the requirements. The requirement priorities are then propagated to related test cases in the test model and test cases are selected based on coverage of extra-functional properties. We have demonstrated the applicability of our proposed approach on a small example case.

  • 7.
    Abbas, Muhammad
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Inayat, Irum
    National University of Computer & Emerging Sciences, Pakistan.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Jan, Naila
    National University of Computer & Emerging Sciences, Pakistan.
    Requirements dependencies-based test case prioritization for extra-functional properties2019In: Proceedings - 2019 IEEE 12th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 159-163Conference paper (Refereed)
    Abstract [en]

    The use of requirements' information in testing is a well-recognized practice in the software development life cycle. Literature reveals that existing tests prioritization and selection approaches neglected vital factors affecting tests priorities, like interdependencies between requirement specifications. We believe that models may play a positive role in specifying these inter-dependencies and prioritizing tests based on these inter-dependencies. However, till date, few studies can be found that make use of requirements inter-dependencies for test case prioritization. This paper uses a meta-model to aid modeling requirements, their related tests, and inter-dependencies between them. The instance of this meta-model is then processed by our modified PageRank algorithm to prioritize the requirements. The requirement priorities are then propagated to related test cases in the test model and test cases are selected based on coverage of extra-functional properties. We have demonstrated the applicability of our proposed approach on a small example case.

  • 8.
    Abbas, Muhammad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Jongeling, Robbert
    Mälardalen University, Sweden.
    Lindskog, Claes
    Bombardier Transportation AB, Sweden.
    Enoiu, Eduard
    Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Sundmark, Daniel
    Mälardalen University, Sweden.
    Product line adoption in industry: an experience report from the railway domain2020In: SPLC '20: Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume A, Association for Computing Machinery , 2020, p. 130-141, article id 3Conference paper (Refereed)
    Abstract [en]

    The software system controlling a train is typically deployed on various hardware architectures and must process various signals across those deployments. The increase of such customization scenarios and the needed adherence of the software to various safety standards in different application domains has led to the adoption of product line engineering within the railway domain. This paper explores the current state-of-practice of software product line development within a team developing industrial embedded software for a train propulsion control system. Evidence is collected using a focus group session with several engineers and through inspection of archival data. We report several benefits and challenges experienced during product line adoption and deployment. Furthermore, we identify and discuss improvement opportunities, focusing mainly on product line evolution and test automation. 

  • 9.
    Abbas, Muhammad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Rauf, Abdel
    RISE Research Institutes of Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Enoiu, Eduard
    Mälardalen University, Sweden.
    Sundmark, Daniel
    Mälardalen University, Sweden.
    Keywords-based test categorization for Extra-Functional Properties2020In: 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 2020, p. 153-156Conference paper (Refereed)
    Abstract [en]

    Categorizing existing test specifications can provide insights on coverage of the test suite to extra-functional properties. Manual approaches for test categorization can be time-consuming and prone to error. In this short paper, we propose a semi-automated approach for semantic keywords-based textual test categorization for extra-functional properties. The approach is the first step towards coverage-based test case selection based on extra-functional properties. We report a preliminary evaluation of industrial data for test categorization for safety aspects. Results show that keyword-based approaches can be used to categorize tests for extra-functional properties and can be improved by considering contextual information of keywords.

  • 10.
    Abbas, Muhammad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden.
    Enoiu, Eduard Paul
    Mälardalens University, Sweden.
    Requirements-driven Reuse Recommendation2021In: 25th ACM International Systems and Software Product Line Conference, ACM , 2021, Vol. AConference paper (Refereed)
    Abstract [en]

    This tutorial explores requirements-based reuse recommendation for product line assets in the context of clone-and-own product lines.

  • 11.
    Abbas, Muhammad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalens University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden.
    Enoiu, Eduard Paul
    Mälardalens University, Sweden.
    Sundmark, Daniel
    Mälardalens University, Sweden.
    Lindskog, Claes
    Bombardier Transportation AB, Sweden.
    Automated Reuse Recommendation of Product Line Assets based on Natural Language Requirements2020In: Reuse in Emerging Software Engineering Practices, Springer International Publishing , 2020, Vol. 12541, p. 173-189Conference paper (Refereed)
    Abstract [en]

    Software product lines (SPLs) are based on reuse rationale to aid quick and quality delivery of complex products at scale. Deriving a new product from a product line requires reuse analysis to avoid redundancy and support a high degree of assets reuse. In this paper, we propose and evaluate automated support for recommending SPL assets that can be reused to realize new customer requirements. Using the existing customer requirements as input, the approach applies natural language processing and clustering to generate reuse recommendations for unseen customer requirements in new projects. The approach is evaluated both quantitatively and qualitatively in the railway industry. Results show that our approach can recommend reuse with 74% accuracy and 57.4% exact match. The evaluation further indicates that the recommendations are relevant to engineers and can support the product derivation and feasibility analysis phase of the projects. The results encourage further study on automated reuse analysis on other levels of abstractions.

  • 12.
    Abbas, Muhammad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Enoiu, Eduard
    Mälardalen University, Sweden.
    Sundamark, Daniel
    Mälardalen University, Sweden.
    Lindskog, Claes
    Bombardier Transportation AB, Sweden.
    Automated Reuse Recommendation of Product Line Assets Based on Natural Language Requirements2020In: Lecture Notes in Computer Science, Springer Science and Business Media Deutschland GmbH , 2020, Vol. 12541, p. 173-189Conference paper (Refereed)
    Abstract [en]

    Software product lines (SPLs) are based on reuse rationale to aid quick and quality delivery of complex products at scale. Deriving a new product from a product line requires reuse analysis to avoid redundancy and support a high degree of assets reuse. In this paper, we propose and evaluate automated support for recommending SPL assets that can be reused to realize new customer requirements. Using the existing customer requirements as input, the approach applies natural language processing and clustering to generate reuse recommendations for unseen customer requirements in new projects. The approach is evaluated both quantitatively and qualitatively in the railway industry. Results show that our approach can recommend reuse with 74% accuracy and 57.4% exact match. The evaluation further indicates that the recommendations are relevant to engineers and can support the product derivation and feasibility analysis phase of the projects. The results encourage further study on automated reuse analysis on other levels of abstractions. 

  • 13.
    Abbaspour Assadollah, Sara
    et al.
    Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE, Swedish ICT, SICS.
    Eldh, Sigrid
    Ericsson AB, Sweden.
    Sundmark, Daniel
    Mälardalen University, Sweden.
    Hansson, Hans
    Mälardalen University, Sweden.
    A Model for Systematic Monitoring and Debugging of Starvation Bugs in Multicore Software2016In: Proceedings of the 1st International Workshop on Specification, Comprehension, Testing, and Debugging of Concurrent Programs (SCTDCP 2016), 2016, p. 7-11Conference paper (Refereed)
    Abstract [en]

    With the development of multicore hardware, concurrent, parallel and multicore software are becoming increasingly popular. Software companies are spending a huge amount of time and resources to nd and debug the bugs. Among all types of software bugs, concurrency bugs are also important and troublesome. This type of bugs is increasingly becoming an issue particularly due to the growing prevalence of multicore hardware. In this position paper, we propose a model for monitoring and debugging Starvation bugs as a type of concurrency bugs in multicore software. The model is composed into three phases: monitoring, detecting and debugging. The monitoring phase can support detecting phase by storing collected data from the system execution. The detecting phase can support debugging phase by comparing the stored data with starvation bug's properties, and the debugging phase can help in reproducing and removing the Starvation bug from multicore software. Our intention is that our model is the basis for developing tool(s) to enable solving Starvation bugs in software for multicore platforms.

  • 14.
    Bashir, Sarmad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Abbas, Muhammad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Ferrari, Alessio
    CNR-ISTI, Italy.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden.
    Lindberg, Pernilla
    Alstom, Sweden.
    Requirements Classification for Smart Allocation: A Case Study in the Railway Industry2023In: 31st IEEE International Requirements Engineering Conference, Hannover, Germany: IEEE , 2023Conference paper (Refereed)
    Abstract [en]

    Allocation of requirements to different teams is a typical preliminary task in large-scale system development projects. This critical activity is often performed manually and can benefit from automated requirements classification techniques. To date, limited evidence is available about the effectiveness of existing machine learning (ML) approaches for requirements classification in industrial cases. This paper aims to fill this gap by evaluating state-of-the-art language models and ML algorithms for classification in the railway industry. Since the interpretation of the results of ML systems is particularly relevant in the studied context, we also provide an information augmentation approach to complement the output of the ML-based classification. Our results show that the BERT uncased language model with the softmax classifier can allocate the requirements to different teams with a 76% F1 score when considering requirements allocation to the most frequent teams. Information augmentation provides potentially useful indications in 76% of the cases. The results confirm that currently available techniques can be applied to real-world cases, thus enabling the first step for technology transfer of automated requirements classification. The study can be useful to practitioners operating in requirements-centered contexts such as railways, where accurate requirements classification becomes crucial for better allocation of requirements to various teams.

  • 15.
    Bashir, Sarmad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.
    Abbas, Muhammad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Enoiu, Eduard
    Mälardalen University, Sweden.
    Bohlin, Markus
    Mälardalen University, Sweden.
    Lindberg, Pernilla
    Alstom, Sweden.
    Requirement or Not, That is the Question: A Case from the Railway Industry2023In: Lecture Notes in Computer Science. Volume 13975. Pages 105 - 121 2023, Springer Science and Business Media Deutschland GmbH , 2023, p. 105-121Conference paper (Refereed)
    Abstract [en]

    Requirements in tender documents are often mixed with other supporting information. Identifying requirements in large tender documents could aid the bidding process and help estimate the risk associated with the project.  Manual identification of requirements in large documents is a resource-intensive activity that is prone to human error and limits scalability. This study compares various state-of-the-art approaches for requirements identification in an industrial context. For generalizability, we also present an evaluation on a real-world public dataset. We formulate the requirement identification problem as a binary text classification problem. Various state-of-the-art classifiers based on traditional machine learning, deep learning, and few-shot learning are evaluated for requirements identification based on accuracy, precision, recall, and F1 score. Results from the evaluation show that the transformer-based BERT classifier performs the best, with an average F1 score of 0.82 and 0.87 on industrial and public datasets, respectively. Our results also confirm that few-shot classifiers can achieve comparable results with an average F1 score of 0.76 on significantly lower samples, i.e., only 20% of the data.  There is little empirical evidence on the use of large language models and few-shots classifiers for requirements identification. This paper fills this gap by presenting an industrial empirical evaluation of the state-of-the-art approaches for requirements identification in large tender documents. We also provide a running tool and a replication package for further experimentation to support future research in this area. © 2023, The Author(s)

  • 16.
    Bradbury, Jeremy
    et al.
    Ontario Tech University, canada.
    Kruse, Peter
    Expleo Germany GmbH, Germany.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Schlingloff, Holger
    Ontario Tech University, canada.
    ToCaMS – Workshop on Testing of Configurable and Multi-variant Systems2020In: 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 2020Conference paper (Refereed)
    Abstract [en]

    Due to increasing market diversification and customer demand, more and more software-based products and services are customizable or are designed in the form of many different variants. This brings about new challenges for the software quality assurance processes: How shall the variability be modelled in order to make sure that all features are being tested? Is it better to test selected variants on a concrete level, or can the generic software and baseline be tested abstractly? Can knowledge-based AI techniques be used to identify and prioritize test cases? How can the quality of a generic test suite be assessed? What are appropriate coverage criteria for configurable modules? If it is impossible to test all possible variants, which products and test cases should be selected for test execution? Can security-testing methods be leveraged to an abstract level?

  • 17.
    Bucaioni, Alessio
    et al.
    Mälardalen University, Sweden.
    Di Silvestro, Fabio
    Gear of Leo, Sweden.
    Singh, Inderjeet
    Alstom, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Muccini, Henry
    Alstom, Sweden.
    Model-based generation of test scripts across product variants: An experience report from the railway industry2022In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 34, no 11, article id e2498Article in journal (Refereed)
    Abstract [en]

    Software product line engineering emerged as an effective approach for the development of families of software-intensive systems in several industries. Although its use has been widely discussed and researched, there are still several open challenges for its industrial adoption and application. One of these is how to efficiently develop and reuse shared software artifacts, which have dependencies on the underlying electrical and hardware systems of products in a family. In this work, we report on our experience in tackling such a challenge in the railway industry and present a model-based approach for the automatic generation of test scripts for product variants in software product lines. The proposed approach is the result of an effort leveraging the experiences and results from the technology transfer activities with our industrial partner Alstom SA in Sweden. We applied and evaluated the proposed approach on the Aventra software product line from Alstom SA. The evaluation showed that the proposed approach mitigates the development effort, development time, and consistency drawbacks associated with the traditional, manual creation of test scripts. We performed an online survey involving 37 engineers from Alstom SA for collecting feedback on the approach. The result of the survey further confirms the aforementioned benefits. © 2022 The Authors.

  • 18.
    Campeanu, Gabriel
    et al.
    Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE, Swedish ICT, SICS. Mälardalen University, Sweden.
    A 2-Layer Component-based Architecture for Heterogeneous CPU-GPU Embedded Systems2016In: Information Technology: New Generations / [ed] Shahram Latifi, 2016, 14, Vol. 448, p. 629-639Conference paper (Refereed)
    Abstract [en]

    Traditional embedded systems are evolving into heterogeneous systems in order to address new and more demanding software requirements. Modern embedded systems are constructed by combining different computation units, such as traditional CPUs, with Graphics Processing Units (GPUs). Adding GPUs to conventional CPU-based embedded systems enhances the computation power but also increases the complexity in developing software applications. A method that can help to tackle and address the software complexity issue of heterogeneous systems is component-based development. The allocation of the software application onto the appropriate computation node is greatly influenced by the system information load. The allocation process is increased in difficulty when we use, instead of common CPU-based systems, complex CPU-GPU systems. This paper presents a 2-layer component-based architecture for heterogeneous embedded systems, which has the purpose to ease the software-to-hardware allocation process. The solution abstracts the CPU-GPU detailed component-based design into single software components in order to decrease the amount of information delivered to the allocator. The last part of the paper describes how the allocation process may be modified using our proposed solution, when applied on a real system demonstrator.

  • 19.
    Campeanu, Gabriel
    et al.
    Bombardier Transportation, Sweden.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    A Two-Layer Component-Based Allocation for Embedded Systems with GPUs2019In: designs, ISSN 2411-9660, Vol. 3, no 1, p. 1-14, article id 6Article in journal (Other academic)
    Abstract [en]

    Component-based development is a software engineering paradigm that can facilitate the construction of embedded systems and tackle its complexities. The modern embedded systems have more and more demanding requirements. One way to cope with such a versatile and growing set of requirements is to employ heterogeneous processing power, i.e., CPU–GPU architectures. The new CPU–GPU embedded boards deliver an increased performance but also introduce additional complexity and challenges. In this work, we address the component-to-hardware allocation for CPU–GPU embedded systems. The allocation for such systems is much complex due to the increased amount of GPU-related information. For example, while in traditional embedded systems the allocation mechanism may consider only the CPU memory usage of components to find an appropriate allocation scheme, in heterogeneous systems, the GPU memory usage needs also to be taken into account in the allocation process. This paper aims at decreasing the component-to-hardware allocation complexity by introducing a two-layer component-based architecture for heterogeneous embedded systems. The detailed CPU–GPU information of the system is abstracted at a high-layer by compacting connected components into single units that behave as regular components. The allocator, based on the compacted information received from the high-level layer, computes, with a decreased complexity, feasible allocation schemes. In the last part of the paper, the two-layer allocation method is evaluated using an existing embedded system demonstrator; namely, an underwater robot.

  • 20.
    Campeanu, Gabriel
    et al.
    Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE, Swedish ICT, SICS.
    Run-time component allocation in CPU-GPU embedded systems2017In: Proceedings of the ACM Symposium on Applied Computing, 2017, p. 1259-1265Conference paper (Refereed)
    Abstract [en]

    Nowadays, many of the modern embedded applications such as vehicles and robots, interact with the environment and receive huge amount of data through various sensors such as cameras and radars. The challenge of processing large amount of data, within an acceptable performance, is solved by employing embedded systems that incorporate complementary attributes of CPUs and Graphics Processing Units (GPUs), i.e., sequential and parallel execution models.component-based development (CBD) is a software engineering methodology that augments the applications development through reuse of software blocks known as components. In developing a CPU-GPU embedded application using CBD, allocation of components to different processing units of the platform is an important activity which can affect the overall performance of the system. In this context, there is also often the need to support and achieve run-time component allocation due to various factors and situations that can happen during system execution, such as switching off parts of the system for energy saving. In this paper, we provide a solution that dynamically allocates components using various system information such as the available resources (e.g., available GPU memory) and the software behavior (e.g., in terms of GPU memory usage). The novelty of our work is a formal allocation model that considers GPU system characteristics computed on-the-fly through software monitoring solutions. For the presentation and validation of our solution, we utilize an existing underwater robot demonstrator.

  • 21.
    Ferrari, Fabiano C.
    et al.
    Federal University of São Carlos, Brazil.
    Durelli, Vinicius H. S.
    Federal University of São João del-Rei, Brazil.
    Andler, Sten F.
    University of Skövde, Sweden.
    Offutt, Jeff
    University at Albany, USA.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Müllner, Nils
    DLR German Aerospace Center, Germany.
    On transforming model‐based tests into code: A systematic literature review2023In: Software testing, verification & reliability, ISSN 0960-0833, E-ISSN 1099-1689Article in journal (Refereed)
    Abstract [en]

    Model-based test design is increasingly being applied in practice and studied in research. Model-based testing (MBT) exploits abstract models of the software behaviour to generate abstract tests, which are then transformed into concrete tests ready to run on the code. Given that abstract tests are designed to cover models but are run on code (after transformation), the effectiveness of MBT is dependent on whether model coverage also ensures coverage of key functional code. In this article, we investigate how MBT approaches generate tests from model specifications and how the coverage of tests designed strictly based on the model translates to code coverage. We used snowballing to conduct a systematic literature review. We started with three primary studies, which we refer to as the initial seeds. At the end of our search iterations, we analysed 30 studies that helped answer our research questions. More specifically, this article characterizes how test sets generated at the model level are mapped and applied to the source code level, discusses how tests are generated from the model specifications, analyses how the test coverage of models relates to the test coverage of the code when the same test set is executed and identifies the technologies and software development tasks that are on focus in the selected studies. Finally, we identify common characteristics and limitations that impact the research and practice of MBT: (i) some studies did not fully describe how tools transform abstract tests into concrete tests, (ii) some studies overlooked the computational cost of model-based approaches and (iii) some studies found evidence that bears out a robust correlation between decision coverage at the model level and branch coverage at the code level. We also noted that most primary studies omitted essential details about the experiments.

  • 22.
    Helali Moghadam, Mahshid
    et al.
    RISE Research Institutes of Sweden. Mälardalen University, Sweden.
    Borg, Markus
    RISE Research Institutes of Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden.
    Mousavirad, Seyed
    Universidade da Beira Interior, Portugal.
    Bohlin, Markus
    RISE Research Institutes of Sweden. Mälardalen University, Sweden.
    Lisper, Björn
    Mälardalen University, Sweden.
    Machine learning testing in an ADAS case study using simulation-integrated bio-inspired search-based testing2023In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481Article in journal (Refereed)
    Abstract [en]

    This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (Formula presented.) and (Formula presented.) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints. © 2023 The Authors. 

  • 23.
    Helali Moghadam, Mahshid
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Hamidi, Golrokh
    Mälardalen University, Sweden.
    Borg, Markus
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Bohlin, Markus
    Mälardalen University, Sweden.
    Lisper, Björn
    Mälardalen University, Sweden.
    Potena, Pasqualina
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Performance Testing Using a Smart Reinforcement Learning-Driven Test Agent2021In: 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, p. 2385-2394Conference paper (Refereed)
    Abstract [en]

    Performance testing with the aim of generating an efficient and effective workload to identify performance issues is challenging. Many of the automated approaches mainly rely on analyzing system models, source code, or extracting the usage pattern of the system during the execution. However, such information and artifacts are not always available. Moreover, all the transactions within a generated workload do not impact the performance of the system the same way, a finely tuned workload could accomplish the test objective in an efficient way. Model-free reinforcement learning is widely used for finding the optimal behavior to accomplish an objective in many decision-making problems without relying on a model of the system. This paper proposes that if the optimal policy (way) for generating test workload to meet a test objective can be learned by a test agent, then efficient test automation would be possible without relying on system models or source code. We present a self-adaptive reinforcement learning-driven load testing agent, RELOAD, that learns the optimal policy for test workload generation and generates an effective workload efficiently to meet the test objective. Once the agent learns the optimal policy, it can reuse the learned policy in subsequent testing activities. Our experiments show that the proposed intelligent load test agent can accomplish the test objective with lower test cost compared to common load testing procedures, and results in higher test efficiency.

  • 24.
    Helali Moghadam, Mahshid
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Borg, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Bohlin, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Lisper, Björn
    Mälardalen University, Sweden.
    Adaptive Runtime Response Time Control in PLC-based Real-Time Systems using Reinforcement Learning2018Conference paper (Refereed)
    Abstract [en]

    Timing requirements such as constraints on response time are key characteristics of real-time systems and violations of these requirements might cause a total failure, particularly in hard real-time systems. Runtime monitoring of the system properties is of great importance to check the system status and mitigate such failures. Thus, a runtime control to preserve the system properties could improve the robustness of the system with respect to timing violations. Common control approaches may require a precise analytical model of the system which is difficult to be provided at design time. Reinforcement learning is a promising technique to provide adaptive model-free control when the environment is stochastic, and the control problem could be formulated as a Markov Decision Process. In this paper, we propose an adaptive runtime control using reinforcement learning for real-time programs based on Programmable Logic Controllers (PLCs), to meet the response time requirements. We demonstrate through multiple experiments that our approach could control the response time efficiently to satisfy the timing requirements.

  • 25.
    Helali Moghadam, Mahshid
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Borg, Markus
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Bohlin, Markus
    Mälardalen University, Sweden.
    Lisper, Björn
    Mälardalen University, Sweden.
    An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning2022In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, p. 127-159Article in journal (Refereed)
    Abstract [en]

    Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case-based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learned policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments in a simulated performance testing setup, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process and performs adaptively without access to source code and performance models. © 2021, The Author(s).

  • 26.
    Helali Moghadam, Mahshid
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Borg, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Bohlin, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Lisper, Björn
    Mälardalen University, Sweden.
    Learning-based Response Time Analysis in Real-Time Embedded Systems: A Simulation-based Approach2018Conference paper (Refereed)
    Abstract [en]

    Response time analysis is an essential task to verify the behavior of real-time systems. Several response time analysis methods have been proposed to address this challenge, particularly for real-time systems with different levels of complexity. Static analysis is a popular approach in this context, but its practical applicability is limited due to the high complexity of the industrial real-time systems, as well as many unpredictable run-time events in these systems. In this work-in-progress paper, we propose a simulation-based response time analysis approach using reinforcement learning to find the execution scenarios leading to the worst-case response time. The approach learns how to provide a practical estimation of the worst-case response time through simulating the program without performing static analysis. Our initial study suggests that the proposed approach could be applicable in the simulation environments of the industrial real-time control systems to provide a practical estimation of the execution scenarios leading to the worst-case response time.

  • 27.
    Helali Moghadam, Mahshid
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Borg, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Bohlin, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Lisper, Björn
    Mälardalen University, Sweden.
    Learning-Based Self-Adaptive Assurance of Timing Properties in a Real-Time Embedded System2018Conference paper (Refereed)
    Abstract [en]

    Providing an adaptive runtime assurance technique to meet the performance requirements of a real-time system without the need for a precise model could be a challenge. Adaptive performance assurance based on monitoring the status of timing properties can bring more robustness to the underlying platform. At the same time, the results or the achieved policy of this adaptive procedure could be used as feedback to update the initial model, and consequently for producing proper test cases. Reinforcement-learning has been considered as a promising adaptive technique for assuring the satisfaction of the performance properties of software-intensive systems in recent years. In this work-in-progress paper, we propose an adaptive runtime timing assurance procedure based on reinforcement learning to satisfy the performance requirements in terms of response time. The timing control problem is formulated as a Markov Decision Process and the details of applying the proposed learning-based timing assurance technique are described.

  • 28.
    Helali Moghadam, Mahshid
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS. Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Borg, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Bohlin, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Lisper, Björn
    Mälardalen University, Sweden.
    Machine Learning to Guide Performance Testing: An Autonomous Test Framework2019In: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'19, 2019, 2019Conference paper (Refereed)
    Abstract [en]

    Satisfying performance requirements is of great importance for performance-critical software systems. Performance analysis to provide an estimation of performance indices and ascertain whether the requirements are met is essential for achieving this target. Model-based analysis as a common approach might provide useful information but inferring a precise performance model is challenging, especially for complex systems. Performance testing is considered as a dynamic approach for doing performance analysis. In this work-in-progress paper, we propose a self-adaptive learning-based test framework which learns how to apply stress testing as one aspect of performance testing on various software systems to find the performance breaking point. It learns the optimal policy of generating stress test cases for different types of software systems, then replays the learned policy to generate the test cases with less required effort. Our study indicates that the proposed learning-based framework could be applied to different types of software systems and guides towards autonomous performance testing.

  • 29.
    Helali Moghadam, Mahshid
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Malardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Borg, Markus
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Bohlin, Markus
    RISE Research Institutes of Sweden.
    Lisper, Björn
    Malardalen University, Sweden.
    Poster: Performance Testing Driven by Reinforcement Learning2020In: 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST), 2020, p. 402-405Conference paper (Refereed)
    Abstract [en]

    Performance testing remains a challenge, particularly for complex systems. Different application-, platform- and workload-based factors can influence the performance of software under test. Common approaches for generating platform- and workload-based test conditions are often based on system model or source code analysis, real usage modeling and use-case based design techniques. Nonetheless, creating a detailed performance model is often difficult, and also those artifacts might not be always available during the testing. On the other hand, test automation solutions such as automated test case generation can enable effort and cost reduction with the potential to improve the intended test criteria coverage. Furthermore, if the optimal way (policy) to generate test cases can be learnt by testing system, then the learnt policy can be reused in further testing situations such as testing variants, evolved versions of software, and different testing scenarios. This capability can lead to additional cost and computation time saving in the testing process. In this research, we present an autonomous performance testing framework which uses a model-free reinforcement learning augmented by fuzzy logic and self-adaptive strategies. It is able to learn the optimal policy to generate platform- and workload-based test conditions which result in meeting the intended testing objective without access to system model and source code. The use of fuzzy logic and self-adaptive strategy helps to tackle the issue of uncertainty and improve the accuracy and adaptivity of the proposed learning. Our evaluation experiments show that the proposed autonomous performance testing framework is able to generate the test conditions efficiently and in a way adaptive to varying testing situations.

  • 30.
    Kiss, Akos
    et al.
    University of Szeged, Hungary.
    Marín, Beatriz
    Universitat Politècnica de València, Spain.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    13th Workshop on Automating Test Case Design, Selection and Evaluation (A-TEST 2022) Co-Located with ESEC/FSE Conference2023In: Software Engineering Notes: an Informal Newsletter of The Specia, ISSN 0163-5948, E-ISSN 1943-5843, Vol. 48, no 1, p. 76-78Article in journal (Refereed)
    Abstract [en]

    The Workshop on Automating Test Case Design, Selection and Evaluation (A-TEST) has provided a venue for researchers and industry members alike to exchange and discuss trending views, ideas, state of the art, work in progress, and scientific results on automated testing. Up until now it has run 13 editions since 2009. The 13th edition of the A-TEST workshop has been performed as an in-person workshop in Singapore during 17 to 18 of November, 2022. This edition of the A-TEST workshop was co-located with ESEC/FSE 2022 conference.

  • 31.
    Lisper, Björn
    et al.
    Mälardalen University, Sweden.
    Lindstrom, Birgitta
    University of Skövde, Sweden.
    Potena, Pasqualina
    RISE - Research Institutes of Sweden, ICT, SICS.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden, ICT, SICS.
    Bohlin, Markus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Targeted Mutation: Efficient Mutation Analysis for Testing Non-Functional Properties2017In: Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017, 2017, p. 65-68Conference paper (Refereed)
    Abstract [en]

    Mutation analysis has proven to be a strong technique for software testing. Unfortunately, it is also computationally expensive and researchers have therefore proposed several different approaches to reduce the effort. None of these reduction techniques however, focuses on non-functional properties. Given that our goal is to create a strong test suite for testing a certain non-functional property, which mutants should be used? In this paper, we introduce the concept of targeted mutation, which focuses mutation effort to those parts of the code where a change can make a difference with respect to the targeted non-functional property. We show how targeted mutation can be applied to derive efficient test suites for estimating the Worst-Case Execution Time (WCET). We use program slicing to direct the mutations to the parts of the code that are likely to have the strongest influence on execution time. Finally, we outline an experimental procedure for how to evaluate the technique.

  • 32.
    Moravvej, S. V.
    et al.
    Isfahan University of Technology, Iran.
    Mousavirad, S. J.
    Hakim Sabzevari Univesity, Iran.
    Helali Moghadam, Mahshid
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    An LSTM-Based Plagiarism Detection via Attention Mechanism and a Population-Based Approach for Pre-training Parameters with Imbalanced Classes2021In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Open AccessVolume 13110 LNCS, Pages 690-701, Springer Science and Business Media Deutschland GmbH , 2021, p. 690-701Conference paper (Refereed)
    Abstract [en]

    Plagiarism is one of the leading problems in academic and industrial environments, which its goal is to find the similar items in a typical document or source code. This paper proposes an architecture based on a Long Short-Term Memory (LSTM) and attention mechanism called LSTM-AM-ABC boosted by a population-based approach for parameter initialization. Gradient-based optimization algorithms such as back-propagation (BP) are widely used in the literature for learning process in LSTM, attention mechanism, and feed-forward neural network, while they suffer from some problems such as getting stuck in local optima. To tackle this problem, population-based metaheuristic (PBMH) algorithms can be used. To this end, this paper employs a PBMH algorithm, artificial bee colony (ABC), to moderate the problem. Our proposed algorithm can find the initial values for model learning in all LSTM, attention mechanism, and feed-forward neural network, simultaneously. In other words, ABC algorithm finds a promising point for starting BP algorithm. For evaluation, we compare our proposed algorithm with both conventional and population-based methods. The results clearly show that the proposed method can provide competitive performance. 

  • 33.
    Mousavirad, Seyed
    et al.
    Hakim Sabzevari University, Iran.
    Schaefer, Gerald
    Loughborough University, UK.
    Helali Moghadam, Mahshid
    Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Pedram, Mahdi
    Lorestan University of Medical Sciences, Iran.
    A population-based automatic clustering algorithm for image segmentation2021In: GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, Inc , 2021, p. 1931-1936Conference paper (Refereed)
    Abstract [en]

    Clustering is one of the prominent approaches for image segmentation. Conventional algorithms such as k-means, while extensively used for image segmentation, suffer from problems such as sensitivity to initialisation and getting stuck in local optima. To overcome these, population-based metaheuristic algorithms can be employed. This paper proposes a novel clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a powerful population-based algorithm to tackle optimisation problems. One of the advantages of our proposed algorithm is that it does not require any information about the number of clusters. To verify the effectiveness of our proposed algorithm, we present a set of experiments based on objective function evaluation and image segmentation criteria to show that our proposed algorithm outperforms existing approaches.

  • 34.
    Mullner, Nils
    et al.
    Malardalen University, Sweden.
    Khan, Saifullah
    Carl von Ossietzky Universitat, Germany.
    Rahman, Md Habibur
    Carl von Ossietzky Universitat, Germany.
    Afzal, Wasif
    Malardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE, Swedish ICT, SICS.
    Simulation-Based Safety Testing Brake-by-Wire2017In: Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017, 2017, p. 61-64Conference paper (Refereed)
    Abstract [en]

    Mechanical systems in cars are replaced by electronic equivalents. To be authorized for the road, validation that the replacements are at least as good as the old systems is required. For electronic braking systems (brake-by-wire), this goodness translates to safety in terms of maintaining timing constraints. Yet, in the future, the safety of braking maneuvres will depend, not only, on electronic brakes, but also on cooperative driving maneuvres orchestrated among many cars. Connecting both brake-by-wire on the microscopic level with cooperative braking on the macroscopic level allows for determining safety on a broader scale, as both systems feed from the same resource: Time. This paper discusses work-in-progress, introducing and combining two threads: electronic brakes and cooperative braking. Discussing safety on two levels simultaneously motivates connecting a Simulink model of an electronic brake-by-wire system with the traffic simulator SUMO for conducting the required combined validation. How safe is a car in relation to a given maximal braking distance? What is the optimal distribution of reaction time between electronic brakes and cooperative braking? The validation focuses on non-functional safety limited by temporal constraints (translated to braking distance). It can be exploited in an early validation approach to help reduce costs of more expensive real world experimentation. It can also determine the boundaries at which sufficient safety can be guaranteed. © 2017 IEEE.

  • 35.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden, ICT, SICS.
    Integrationdistiller: Automating integration analysis and testing of object-oriented applications2019In: Proceedings of the IEEE International Conference on Industrial Technology, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 1385-1392Conference paper (Refereed)
    Abstract [en]

    Software systems typically consist of various interacting components and units. While these components can be tested and shown to work correctly in isolation, when integrated and start interacting with each other, they may fail to produce the desired behaviors and results. Integration testing plays an important role in revealing issues in interactions among cooperating components. Identifying different interaction scenarios, however, is not a trivial task when performing integration testing. On the other hand, most of the integration testing solutions proposed in the literature are manual which hinders their scalability and applicability when it comes to large industrial systems. In this paper we introduce IntegrationDistiller as an automated solution and tool to identify integration scenarios and generate test cases (in the form of method call sequences) for.NET applications. It works by analyzing the code and automatically identifying class couplings, interacting methods, as well as invocation points. Moreover, the tool also helps and supports testers in identifying timing issues at integration level by automatic code instrumentation at invocation points. The code analysis engine of IntegrationDistiller is built and automated using.NET compiler platform, known as Roslyn. Hence, this work is the first in utilizing Roslyn features for automatic integration analysis and integration test case generation. This work has been done as part of our collaboration with ABB Industrial Automation Control Technologies (IACT) in Västerås-Sweden to address the integration testing challenges of the software part of the ABB Ability™ 800xA distributed control systems.

  • 36.
    Saadatmand, Mehrdad
    RISE, Swedish ICT, SICS.
    Towards Automating Integration Testing of .NET Applications using Roslyn2017Conference paper (Refereed)
    Abstract [en]

    The increasing complexity and size of software products combined with pressure to have shorter time-to-market is making manual testing techniques too costly and unscalable. This is particularly observed in industrial systems where continuous integration and deployment are applied. Therefore, there is a growing need to automate the testing process and make it scalable with respect to the context of real-world and large industrial applications. While there are already some solutions for generation of unit level test cases, automatic generation of integration level test cases to verify interaction of software components poses specific challenges especially in object-oriented applications. In this paper, we describe our ongoing work in introducing a solution to automate generation of integration test cases for C# applications by exploiting the code analysis capabilities of Microsoft .NET compiler platform known as Roslyn. This is done in collaboration with ABB Industrial Automation Control Technologies (IACT) in Västerås-Sweden, where the software for 800xA distributed control system is developed.

  • 37.
    Saadatmand, Mehrdad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Abbas, Muhammad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.
    Enoiu, Eduard Paul
    Mälardalen University, Sweden.
    Schlingloff, Bernd-Holger
    Fraunhofer, Germany.
    Afzal, Wasif
    Mälardalen University, Sweden.
    Dornauer, Benedikt
    University of Innsbruck, Austria.
    Felderer, Michael
    University of Innsbruck, Austria.
    SmartDelta project: Automated quality assurance and optimization across product versions and variants2023In: Microprocessors and microsystems, ISSN 0141-9331, E-ISSN 1872-9436, p. 104967-104967, article id 104967Article in journal (Refereed)
    Abstract [en]

    Software systems are often built in increments with additional features or enhancements on top of existing products. This incremental development may result in the deterioration of certain quality aspects. In other words, the software can be considered an evolving entity emanating different quality characteristics as it gets updated over time with new features or deployed in different operational environments. Approaching software development with this mindset and awareness regarding quality evolution over time can be a key factor for the long-term success of a company in today’s highly competitive market of industrial software-intensive products. Therefore, it is important to be able to accurately analyze and determine the quality implications of each change and increment to a software system. To address this challenge, the multinational SmartDelta project develops automated solutions for the quality assessment of product deltas in a continuous engineering environment. The project provides smart analytics from development artifacts and system executions, offering insights into quality degradation or improvements across different product versions, and providing recommendations for the next builds. This paper presents the challenges in incremental software development tackled in the scope of the SmartDelta project, and the solutions that are produced and planned in the project, along with the industrial impact of the project for software-intensive industrial systems.

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  • 38.
    Saadatmand, Mehrdad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Enoiu, Eduard P
    Mälardalen University, Sweden.
    Schlingloff, Holger
    Fraunhofer, Gerany.
    Felderer, Michael
    University of Innsbruck, Austria.
    Afzal, Wasif
    Mälardalen University, Sweden.
    SmartDelta: Automated Quality Assurance and Optimization in Incremental Industrial Software Systems Development2022In: Proceedings - 2022 25th Euromicro Conference on Digital System Design, DSD 2022, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 754-760Conference paper (Refereed)
    Abstract [en]

    A common phenomenon in software development is that as a system is being built and incremented with new features, certain quality aspects of the system begin to deteriorate. Therefore, it is important to be able to accurately analyze and determine the quality implications of each change and increment to a system. To address this topic, the multinational SmartDelta project develops automated solutions for quality assessment of product deltas in a continuous engineering environment. The project will provide smart analytics from development artifacts and system executions, offering insights into quality degradation or improvements across different product versions, and providing recommendations for next builds. 

  • 39.
    Saadatmand, Mehrdad
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Hansson, Hans
    RISE - Research Institutes of Sweden, ICT, SICS. Mälardalen University.
    Thane, Henrik
    Safety Integrity AB.
    Hänninen, Kaj
    Mälardalen University.
    Inadequate Risk Analysis Might Jeopardize The Functional Safety of Modern Systems2016Report (Other academic)
    Abstract [en]

    In the early 90s, researchers began to focus on security as an important property to address in combination with safety. Over the years, researchers have proposed approaches to harmonize activities within the safety and security disciplines. Despite the academic efforts to identify interdependencies and to propose combined approaches for safety and security, there is still a lack of integration between safety and security practices in the industrial context, as they have separate standards and independent processes often addressed and assessed by different organizational teams and authorities. Specifically, security concerns are generally not covered in any detail in safety standards potentially resulting in successfully safety-certified systems that still are open for security threats from e.g., malicious intents from internal and external personnel and hackers that may jeopardize safety. In recent years security has again received an increasing attention of being an important issue also in safety assurance, as the open interconnected nature of emerging systems makes them susceptible to security threats at a much higher degree than existing more confined products.This article presents initial ideas on how to extend safety work to include aspects of security during the context establishment and initial risk assessment procedures. The ambition of our proposal is to improve safety and increase efficiency and effectiveness of the safety work within the frames of the current safety standards, i.e., raised security awareness in compliance with the current safety standards. We believe that our proposal is useful to raise the security awareness in industrial contexts, although it is not a complete harmonization of safety and security disciplines, as it merely provides applicable guidance to increase security awareness in a safety context.

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  • 40.
    Saadatmand, Mehrdad
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Lindstrom, Birgitta
    University of Skövde, Sweden.
    Bohlin, Markus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Message from the ITEQS 2017 Chairs2017In: 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017, p. 44-45, article id 7899031Article in journal (Refereed)
  • 41.
    Saadatmand, Mehrdad
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Lindström, Birgitta
    University of Skövde, Sweden.
    Aichernig, Bernhard K.
    Graz University of Technology, Austria.
    Message from the ITEQS 2018 workshop chairs2018In: 11th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2018Article in journal (Other academic)
    Abstract [en]

    Testing  a  system  with  respect  to  extra-functional  properties  poses  specific  challenges  as  traditional  methods and approaches for testing the system’s functional correctness may not apply. A few examples of such challenges are: fault localization, the need to have appropriate techniques for different types of extra-functional  properties,  the  role  and  impact  of  the  environment  when  testing,  observability  and  testability  issues.  ITEQS  provides  a  well-focused  forum  with  the  goal  of  bringing  together  researchers  and  practitioners to share ideas, identify challenges, propose solutions and techniques, and in general expand the state of the art in testing EFPs and quality characteristics of software systems and services. ITEQS  2018  attracted  8  submissions  (1  withdrawn).  Each  paper  underwent  a  careful  review  and  discussion  process  by  at  least  three  members  of  the  program  committee.  Based  on  the  reviews,  5  papers  were  selected  for  publication  and  presentation  at  the  workshop.  ITEQS  2018  program  includes  keynotes  by Konstantinos Sagonas and Brian Nielsen, who both kindly accepted our invitation. Konstantinos gives a  keynote  on  testing  for  concurrency  bugs  and  Brian  gives  a  keynote  where  he  discusses  how  we  may  achieve a methodology for compositional testing of real-time systems. Finally,  we  would  like  to  thank  our  general  sponsors,  the  publicity  chairs,  the  program  committee,  the  external reviewers, the keynote speakers, the authors and participants. You all contribute to the quality and success of this workshop. Welcome to the workshop! We hope you will enjoy it. xv

  • 42.
    Saadatmand, Mehrdad
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Lindström, Birgitta
    University of Skövde, Sweden.
    Aichernig, Bernhard Klaus
    Graz University of Technology, Austria.
    Special issue on testing extra-functional properties2020In: Software testing, verification & reliability, ISSN 0960-0833, E-ISSN 1099-1689, Vol. 30, no 1, article id e1726Article in journal (Other academic)
    Download full text (pdf)
    fulltext
  • 43.
    Saadatmand, Mehrdad
    et al.
    RISE, Swedish ICT, SICS. Mälardalen University, Sweden.
    Tahvili, Sahar
    RISE, Swedish ICT, SICS. Mälardalen University, Sweden.
    A Fuzzy Decision Support Approach for Model-Based Tradeoff Analysis of Non-Functional Requirements2015In: 2015 12th International Conference on Information Technology - New Generations, 2015, 22, p. 112-121Conference paper (Refereed)
    Abstract [en]

    One of the main challenges in addressing Non-Functional Requirements (NFRs) in designing systems is to take into account their interdependencies and mutual impacts. For this reason, they cannot be considered in isolation and a careful balance and tradeoff among them should be established. This makes it a difficult task to select design decisions and features that lead to the satisfaction of all different NFRs in the system, which becomes even more difficult when the complexity of a system grows. In this paper, we introduce an approach based on fuzzy logic and decision support systems that helps to identify different design alternatives that lead to higher overall satisfaction of NFRs in the system. This is achieved by constructing a model of the NFRs and then performing analysis on the model. To build the model, we use a modified version of the NFR UML profile which we have introduced in our previous works, and using model transformation techniques we automate the analysis of the model.

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  • 44.
    Saadatmand, Mehrdad
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Truscan, D.
    Åbo Akademi University, Finland.
    Enoiu, E.
    Mälardalen University, Sweden.
    Message from the ITEQS 2022 Workshop Chairs2022In: Proceedings - 2022 IEEE 14th International Conference on Software TestingArticle in journal (Other academic)
  • 45.
    Schlingloff, H.
    et al.
    Fraunhofer, Germany.
    Kruse, P. M.
    Expleo Germany GmbH, Germany.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Excellence in variant testing2020In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2020Conference paper (Refereed)
    Abstract [en]

    In this short paper, we report on the motivation, background and ambition of the ITEA3 project XIVT - excellence in variant testing. We describe a work flow and tool chain for testing of configurable and highly-variant embedded systems in various domains. © 2020 Copyright is held by the owner/author(s).

  • 46.
    Sedaghatbaf, Ali
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Helali Moghadam, Mahshid
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Automated Performance Testing Based on Active Deep Learning2021In: 2021 IEEE/ACM International Conference on Automation of Software Test (AST), 2021, p. 11-19Conference paper (Refereed)
    Abstract [en]

    Generating tests that can reveal performance issues in large and complex software systems within a reasonable amount of time is a challenging task. On one hand, there are numerous combinations of input data values to explore. On the other hand, we have a limited test budget to execute tests. What makes this task even more difficult is the lack of access to source code and the internal details of these systems. In this paper, we present an automated test generation method called ACTA for black-box performance testing. ACTA is based on active learning, which means that it does not require a large set of historical test data to learn about the performance characteristics of the system under test. Instead, it dynamically chooses the tests to execute using uncertainty sampling. ACTA relies on a conditional variant of generative adversarial networks, and facilitates specifying performance requirements in terms of conditions and generating tests that address those conditions. We have evaluated ACTA on a benchmark web application, and the experimental results indicate that this method is comparable with random testing, and two other machine learning methods, i.e. PerfXRL and DN.

  • 47.
    Sirjani, Marjan
    et al.
    Mälardalen University, Sweden; Reykjavik University, Iceland.
    Provenzano, Luciana
    Mälardalen University, Sweden.
    Asadollah, Sara
    Mälardalen University, Sweden.
    Helali Moghadam, Mahshid
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Towards a Verification-Driven Iterative Development of Software for Safety-Critical Cyber-Physical Systems2021In: Journal of Internet Services and Applications, ISSN 1867-4828, E-ISSN 1869-0238, Vol. 12, no 1, article id 2Article in journal (Refereed)
    Abstract [en]

    Software systems are complicated, and the scientific and engineering methodologies for software development are relatively young. Cyber-physical systems are now in every corner of our lives, and we need robust methods for handling the ever-increasing complexity of their software systems. Model-Driven Development is a promising approach to tackle the complexity of systems through the concept of abstraction, enabling analysis at earlier phases of development. In this paper, we propose a model-driven approach with a focus on guaranteeing safety using formal verification. Cyber-physical systems are distributed, concurrent, asynchronous and event-based reactive systems with timing constraints. The actor-based textual modeling language, Rebeca, with model checking support is used for formal verification. Starting from structured requirements and system architecture design the behavioral models, including Rebeca models, are built. Properties of interest are also derived from the structured requirements, and then model checking is used to formally verify the properties. This process can be performed in iterations until satisfaction of desired properties are ensured, and possible ambiguities and inconsistencies in requirements are resolved. The formally verified models can then be used to develop the executable code. The Rebeca models include the details of the signals and messages that are passed at the network level including the timing, and this facilitates the generation of executable code. The natural mappings among the models for requirements, the formal models, and the executable code improve the effectiveness and efficiency of the approach. © 2021, The Author(s).

  • 48.
    Tahvili, Sahar
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS. Mälardalen University, Sweden.
    Afzal, Wasif
    Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden, ICT, SICS.
    Bohlin, Marcus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Ameerjan, Sharvathul Hasan
    Mälardalen University, Sweden.
    ESPRET: A tool for execution time estimation of manual test cases2018In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 146, p. 26-41Article in journal (Refereed)
    Abstract [en]

    Manual testing is still a predominant and an important approach for validation of computer systems, particularly in certain domains such as safety-critical systems. Knowing the execution time of test cases is important to perform test scheduling, prioritization and progress monitoring. In this work, we present, apply and evaluate ESPRET (EStimation and PRediction of Execution Time) as our tool for estimating and predicting the execution time of manual test cases based on their test specifications. Our approach works by extracting timing information for various steps in manual test specification. This information is then used to estimate the maximum time for test steps that have not previously been executed, but for which textual specifications exist. As part of our approach, natural language parsing of the specifications is performed to identify word combinations to check whether existing timing information on various test steps is already available or not. Since executing test cases on the several machines may take different time, we predict the actual execution time for test cases by a set of regression models. Finally, an empirical evaluation of the approach and tool has been performed on a railway use case at Bombardier Transportation (BT) in Sweden.

  • 49.
    Tahvili, Sahar
    et al.
    RISE, Swedish ICT, SICS. Mälardalen University, Sweden.
    Afzal, Wasif
    Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE, Swedish ICT, SICS. Mälardalen University, Sweden.
    Bohlin, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Sundmark, Daniel
    Mälardalen University, Sweden.
    Larsson, Stig
    Towards Earlier Fault Detection by Value-Driven Prioritization of Test Cases Using Fuzzy TOPSIS2016In: Information Technology: New Generations, 2016, 9, Vol. 448, p. 745-759Conference paper (Refereed)
    Abstract [en]

    In industrial software testing, development projects typically set up and maintain test suites containing large numbers of test cases. Executing a large number of test cases can be expensive in terms of effort and wall-clock time. Moreover, indiscriminate execution of all available test cases typically lead to sub-optimal use of testing resources. On the other hand, selecting too few test cases for execution might leave a large number of faults undiscovered. Limiting factors such as allocated budget and time constraints for testing further emphasizes the importance of test case prioritization in order to identify test cases that enable earlier detection of faults while respecting such constraints. In this paper, we propose a multi-criteria decision making approach for prioritizing test cases in order to detect faults earlier. This is achieved by applying the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) decision making technique combined with fuzzy principles. Our solution is based on important criteria such as fault detection probability, execution time, complexity, and other test case properties. By applying the approach on a train control management subsystem from Bombardier Transportation in Sweden, we demonstrate how it helps, in a systematic way, to identify test cases that can lead to early detection of faults while respecting various criteria.

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    FULLTEXT01
  • 50.
    Tahvili, Sahar
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Ahlberg, Marcus
    KTH Royal Institute of Technology, Sweden.
    Fornander, Eric
    KTH Royal Institute of Technology, Sweden.
    Afzal, Wasif
    Mälardalen University, Sweden.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden, ICT, SICS.
    Bohlin, Markus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Sarabi, Mahdi
    Bombardier Transportation AB, Sweden.
    Functional Dependency Detection for Integration Test Cases2018In: Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2018, 2018, p. 207-214Conference paper (Refereed)
    Abstract [en]

    This paper presents a natural language processing (NLP) based approach that, given software requirements specification, allows the functional dependency detection between integration test cases. We analyze a set of internal signals to the implemented modules for detecting dependencies between requirements and thereby identifying dependencies between test cases such that: module 2 depends on module 1 if an output internal signal from module 1 enters as an input internal signal to the module 2. Consequently, all requirements (and thereby test cases) for module 2 are dependent on all the designed requirements (and test cases) for module 1. The dependency information between requirements (and thus corresponding test cases) can be utilized for test case prioritization and scheduling. We have implemented our approach as a tool and the feasibility is evaluated through an industrial use case in the railway domain at Bombardier Transportation (BT), Sweden. © 2018 IEEE.

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