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  • 1.
    Borg, Markus
    RISE., Swedish ICT, SICS, Security Lab. Lund University, Sweden.
    TuneR: A Framework for Tuning Software Engineering Tools with Hands-on Instructions in R2016Inngår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 28, nr 6, s. 427-459Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Numerous tools automating various aspects of software engineering have been developed, and many of the tools are highly configurable through parameters. Understanding the parameters of advanced tools often requires deep understanding of complex algorithms. Unfortunately, suboptimal parameter settings limit the performance of tools and hinder industrial adaptation, but still few studies address the challenge of tuning software engineering tools. We present TuneR, an experiment framework that supports finding feasible parameter settings using empirical methods. The framework is accompanied by practical guidelines of how to use R to analyze the experimental outcome. As a proof-of-concept, we apply TuneR to tune ImpRec, a recommendation system for change impact analysis in a software system that has evolved for more than two decades. Compared with the output from the default setting, we report a 20.9% improvement in the response variable reflecting recommendation accuracy. Moreover, TuneR reveals insights into the interaction among parameters, as well as nonlinear effects. TuneR is easy to use, thus the framework has potential to support tuning of software engineering tools in both academia and industry.

    Fulltekst (pdf)
    FULLTEXT01
  • 2.
    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, Digitala system, Industriella system.
    Muccini, Henry
    Alstom, Sweden.
    Model-based generation of test scripts across product variants: An experience report from the railway industry2022Inngår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 34, nr 11, artikkel-id e2498Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 3.
    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 testing2024Inngår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, nr 5, artikkel-id e2591Artikkel i tidsskrift (Fagfellevurdert)
    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. 

    Fulltekst (pdf)
    fulltext
  • 4.
    Papatheocharous, Efi
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS. University of Cyprus, Cyprus.
    Bibi, Stamatia
    University of Western Macedonia, Greece.
    Stamelos, Ioannis
    Aristotle University, Greece.
    Andreou, Andreas S
    Cyprus University of Technology, Cyprus.
    An investigation of effort distribution among development phases: A four-stage progressive software cost estimation model2017Inngår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 29, nr 10, artikkel-id e1881Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Software cost estimation is a key process in project management. Estimations in the initial project phases are made with a lot of uncertainty that influences estimation accuracy which typically increases as the project progresses in time. Project data collected during the various project phases can be used in a progressive time-dependent fashion to train software cost estimation models. Our motivation is to reduce uncertainty and increase confidence based on the understanding of patterns of effort distributions in development phases of real-world projects. In this work, we study effort distributions and suggest a four-stage progressive software cost estimation model, adjusting the initial effort estimates during the development life-cycle based on newly available data. Initial estimates are reviewed on the basis of the experience gained as development progresses and as new information becomes available. The proposed model provides an early, a post-planning, a post-specifications, and a post-design estimate, while it uses industrial data from the ISBSG (R10) dataset. The results reveal emerging patterns of effort distributions and indicate that the model provides effective estimations and exhibits high explanatory value. Contributions in lessons learned and practical implications are also provided.

  • 5.
    Papatheocharous, Efi
    et al.
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system. Blekinge Institute of Technology, Sweden.
    Wohlin, Claes
    Blekinge Institute of Technology, Sweden.
    Badampudi, Deepika
    Blekinge Institute of Technology, Sweden.
    Carlson, Jan
    Mälardalen University, Sweden.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Sweden.
    Context factors perceived important when looking for similar experiences in decision-making for software components: An interview study2024Inngår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    During software evolution, decisions related to components’ origin or source significantly impact the quality properties of the product and development metrics such as cost, time to market, ease of maintenance, and further evolution. Thus, such decisions should ideally be supported by evidence, i.e., using previous experiences and information from different sources, even own previous experiences. A hindering factor to such reuse of previous experiences is that these decisions are highly context-dependent and it is difficult to identify when previous experiences come from sufficiently similar contexts to be useful in a current setting. Conversely, when documenting a decision (as a decision experience), it is difficult to know which context factors will be most beneficial when reusing the experience in the future. An interview study is performed to identify a list of context factors that are perceived to be most important by practitioners when using experiences to support decision-making for component sourcing, using a specific scenario with alternative sources of experiences. We observed that the further away (from a company or an interviewee) the experience evidence is, as is the case for online experiences, the more context factors are perceived as important by practitioners to make use of the experience. Furthermore, we discuss and identify further research to make this type of decision-making more evidence-based. 

    Fulltekst (pdf)
    fulltext
  • 6.
    Wohlin, Claes
    et al.
    Blekinge Institute of Technology, Sweden.
    Papatheocharous, Efi
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system.
    Carlson, Jan
    Mälardalen University, Sweden.
    Petersen, Kai
    Blekinge Institute of Technology, Sweden; University of Applied Sciences Flensburg, Germany.
    Alégroth, Emil
    Blekinge Institute of Technology, Sweden.
    Axelsson, Jakob
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system. Mälardalen University, Sweden.
    Badampudi, Deepika
    Blekinge Institute of Technology, Sweden.
    Borg, Markus
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system.
    Cicchetti, Antonio
    Mälardalen University, Sweden.
    Ciccozzi, Frederico
    Mälardalen University, Sweden.
    Olsson, Thomas
    RISE Research Institutes of Sweden, Digitala system, Mobilitet och system.
    Sentilles, Severine
    Mälardalen University, Sweden.
    Svahnberg, Mikael
    Blekinge Institute of Technology, Sweden.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Sweden.
    Gorschek, Tony
    Blekinge Institute of Technology, Sweden.
    Towards evidence-based decision-making for identification and usage of assets in composite software: A research roadmap2021Inngår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 33, nr 6, artikkel-id e2345Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Software engineering is decision intensive. Evidence-based software engineering is suggested for decision-making concerning the use of methods and technologies when developing software. Software development often includes the reuse of software assets, for example, open-source components. Which components to use have implications on the quality of the software (e.g., maintainability). Thus, research is needed to support decision-making for composite software. This paper presents a roadmap for research required to support evidence-based decision-making for choosing and integrating assets in composite software systems. The roadmap is developed as an output from a 5-year project in the area, including researchers from three different organizations. The roadmap is developed in an iterative process and is based on (1) systematic literature reviews of the area; (2) investigations of the state of practice, including a case survey and a survey; and (3) development and evaluation of solutions for asset identification and selection. The research activities resulted in identifying 11 areas in need of research. The areas are grouped into two categories: areas enabling evidence-based decision-making and those related to supporting the decision-making. The roadmap outlines research needs in these 11 areas. The research challenges and research directions presented in this roadmap are key areas for further research to support evidence-based decision-making for composite software. © 2021 The Authors.

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