Software systems often target a variety of different market segments. Targeting varying customer requirements requires a product-focused development process. Software Product Line (SPL) engineering is one possible approach based on reuse rationale to aid quick delivery of quality product variants at scale. SPLs reuse common features across derived products while still providing varying configuration options. The common features, in most cases, are realized by reusable assets. In practice, the assets are reused in a clone-and-own manner to reduce the upfront cost of systematic reuse. Besides, the assets are implemented in increments, and requirements prioritization also has to be done. In this context, the manual reuse analysis and prioritization process become impractical when the number of derived products grows. Besides, the manual reuse analysis process is time-consuming and heavily dependent on the experience of engineers. In this licentiate thesis, we study requirements-level reuse recommendation and prioritization for SPL assets in industrial settings. We first identify challenges and opportunities in SPLs where reuse is done in a clone-and-own manner. We then focus on one of the identified challenges: requirements-based SPL assets reuse and provide automated support for identifying reuse opportunities for SPL assets based on requirements. Finally, we provide automated support for requirements prioritization in the presence of dependencies resulting from reuse.

[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.

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.

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.

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.

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.

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

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.

In this paper we analyse two variants of SIMON family of light-weight block ciphers against linear cryptanalysis and present the best linear cryptanalytic results on these variants of reduced-round SIMON to date. We propose a time-memory trade-off method that finds differential/linear trails for any permutation allowing low Hamming weight differential/linear trails. Our method combines low Hamming weight trails found by the correlation matrix representing the target permutation with heavy Hamming weight trails found using a Mixed Integer Programming model representing the target differential/linear trail. Our method enables us to find a 17-round linear approximation for SIMON-48 which is the best current linear approximation for SIMON-48. Using only the correlation matrix method, we are able to find a 14-round linear approximation for SIMON-32 which is also the current best linear approximation for SIMON-32. The presented linear approximations allow us to mount a 23-round key recovery attack on SIMON-32 and a 24-round Key recovery attack on SIMON-48/96 which are the current best results on SIMON-32 and SIMON-48. In addition we have an attack on 24 rounds of SIMON-32 with marginal complexity.

We point out the risks of protecting relational databases viaSearchable Symmetric Encryption (SSE) schemes by proposing an infer-ence attack exploiting the structural properties of relational databases.We show that record-injection attacks mounted on relational databaseshave worse consequences than their file-injection counterparts on un-structured databases. Moreover, we discuss some techniques to reducethe effectiveness of inference attacks exploiting the access pattern leak-age existing in SSE schemes. To the best of our knowledge, this is thefirst work that investigates the security of relational databases protectedby SSE schemes.

Communication networks are vital for society and network availability is therefore crucial. There is a huge potential in using network telemetry data and machine learning algorithms to proactively detect anomalies and remedy problems before they affect the customers. In practice, however, there are many steps on the way to get there. In this paper we present ongoing development work on efficient data collection pipelines, anomaly detection algorithms and analysis of traffic patterns and predictability.

Building open-domain conversational systems (or chatbots) that produce convincing responses is a recognized challenge. Recent state-of-the-art (SoTA) transformer-based models for the generation of natural language dialogue have demonstrated impressive performance in simulating human-like, single-turn conversations in English.This work investigates, by an empirical study, the potential for transfer learning of such models to Swedish language. DialoGPT, an English language pre-trained model, is adapted by training on three different Swedish language conversational datasets obtained from publicly available sources: Reddit, Familjeliv and the GDC. Perplexity score (an automated intrinsic metric) and surveys by human evaluation were used to assess the performances of the fine-tuned models. We also compare the DialoGPT experiments with an attention-mechanism-based seq2seq baseline model, trained on the GDC dataset. The results indicate that the capacity for transfer learning can be exploited with considerable success. Human evaluators asked to score the simulated dialogues judged over 57% of the chatbot responses to be human-like for the model trained on the largest (Swedish) dataset. The work agrees with the hypothesis that deep monolingual models learn some abstractions which generalize across languages. We contribute the codes, datasets and model checkpoints and host the demos on the HuggingFace platform.

The Internet of Things (IoT) for smart cities needs accessible open data and open systems, so that industries and citizens can develop new services and applications. As an example, the authors provide a case study of the GreenIoT platform in Uppsala, Sweden.

Information-centric networking~(ICN) has been introduced as a potential future networking architecture. ICN promises an architecture that makes information independent from location, application, storage, and transportation. Still, it is not without challenges. Notably, there are several outstanding issues regarding congestion control: Since ICN is more or less oblivious to the location of information, it opens up for a single application flow to have several sources, something which blurs the notion of transport flows, and makes it very difficult to employ traditional end-to-end congestion control schemes in these networks. Instead, ICN networks often make use of hop-by-hop congestion control schemes. However, these schemes are also tainted with problems, e.g., several of the proposed ICN congestion controls assume fixed link capacities that are known beforehand. Since this seldom is the case, this paper evaluates the consequences in terms of latency, throughput, and link usage, variable link capacities have on a hop-by-hop congestion control scheme, such as the one employed by the Multipath-aware ICN Rate-based Congestion Control~(MIRCC). The evaluation was carried out in the OMNeT++ simulator, and demonstrates how seemingly small variations in link capacity significantly deteriorate both latency and throughput, and often result in inefficient network link usage.

Many IoT applications are inherently information-centric, making it advantageous to use ICN transport. We demonstrate CCN-lite ported to run on Contiki sensor motes with limited processing and storage resources. We show a method for mapping streams of sensor data to a stream of immutable CCN named data objects, and an adaptive probing method to find the newest value. We also demonstrate interoperation between MQTT and CCN via a gateway. A higher level goal is to use ICN as an open interface for accessing IoT data.

Many protocols in low-power wireless networks require a leader to bootstrap and maintain their operation. For example, Chaos and Glossy networks need an initiator to synchronize and initiate the communication rounds. Commonly, these protocols use a fixed, compile-time defined node as the leader. In this work, we tackle the challenge of dynamically bootstrapping the network and electing a leader in low-power wireless scenarios.

Many protocols in low-power wireless networks require a root nodeor a leader to bootstrap and maintain its operation. For example,Chaos and Glossy networks need an initiator to synchronize andinitiate the communications rounds. Commonly, these protocolsuse a xed, compile-time dened node as the leader. In this work,we tackle the challenge of dynamically bootstrapping the networkand electing a leader in low-power wireless scenarios, and we focuson Chaos-style networks

Route-based simulated traffic data for high-resolution analysis of heavy goods transport on the Swedish road network In this report, a national database has been created regarding freight transport with heavy road vehicles. The primary purpose of the work is to serve as input for further analysis of what appropriate charging infrastructure planning and placement should look like given the knowledge of the transport work. It has thus been no ambition to give any recommendations in this report about, for example, expansion of charging infrastructure, but rather to collect and process information/data as well as develop methods and finally generate a data set that is useful and well representative of the traffic on the national road network. By the time of this publication, a dataset is available based on data from the Swedish Transport Administration’s Samgods-model with its simulations of transport connections based on transport demand between producer and consumer zones. In addition, all transport connections have been translated into routes (how trucks drive from A to B) on the road network, to enable analysis of electrification of/at specific road segments. Finally, the dataset has also been calibrated in various ways to better match statistics and actual measurements, as some major differences/deviations compared to some of them were identified. What the data set now consists of can be summarized as the number of truck movements and tons of goods that annually pass each road segment of the Swedish road network (and on some foreign roads). Furthermore, these totals can be easily divided into subsets and linked to specific routes, types of trucks (weight classes), origin, etcetera. Some shortcomings/limitations have been noticed during the production of this data set, such as the fact that the Samgods-model seems to miss a lot of transport in metropolitan areas, that the routing carried out by all flows is not completely perfect (which has partly to do with requests from OpenStreetMap), that the methods for generating new routes based on population density within municipalities are unlikely to be fully representative of where the transport is going, or that the data itself is based on a simulation model that tries to optimize which type of transport should be used to meet which demand. A couple of additional things may be worth clarifying: (1) The data only tells the number of transports or shipped goods between start and end nodes. Thus, there is no way to determine what the movement pattern of individual vehicle individuals looks like between routes, nor when in time each transport is performed. (2) The data only includes freight transport, and thus "misses" for example all passenger car traffic, which should also be seen as potential users of the charging infrastructure and thus be included in the calculations in the future. It would therefore be interesting to include these in some way in the next step.

Almost all data sharing regulations have origins from the EU. At EU level, three trends can be identified for data sharing. The first trend is that data sharing more and more is regulated by legislation. Current regulations are being amended and many new regulations are underway within the EU. Data sharing legislations are thus in an expansive phase. There are also many reasons why the EU believes that a certain regulatory framework is needed, such as: • Information security: Historically, information security has generated a large amount of activity in the field of regulatory framework. This includes, for example, cyber security and preventing data breaches. • Human health: Human health is also a reason to regulate data sharing. Examples of regulations in this area are the GDPR and sharing of sensitive personal data. • Consumer protection: There are also regulations aimed at strengthening consumer protection and ensuring that, for example, digital services are safe for consumers to share data in. • A free and efficient internal market: For the EU, it is important to create an internal market for data sharing. Many regulations are aimed at ensuring that SMEs can compete with large companies. Example of legislation in this area is the Platform Regulation. • Increased innovation power: For the EU, it is also important to increase innovation capacity in the internal market. One way is to protect innovations through, for example, copyright and trade secrets rules. • Increased transparency and trust: To create an internal market, people and companies also need to feel safe sharing data. Example of legislation within this area is the proposed Data Governance Act. • Fundamental rights and freedoms: Finally, the EU is reassessing in many regulatory frameworks in terms of respect of fundamental human rights and freedoms. Examples of regulations in this area are the GDPR and the e-Privacy regulation. The EU is also working on developing a code on this theme. The code shall guide the future work on the develop of new legislation. The second trend is for the EU to encourage industry organizations to develop voluntary rules on data sharing (code of conduct) to accelerate the creation of an internal market for data sharing. An example of this is the Code of Conduct for sharing agricultural data in agreements. The Free Flow of non-personal data regulation would also like to see industry organizations develop principles for data sharing. The third trend is that the EU would like to see us all make more data publicly available or that we donate data, both from authorities and individuals (open data and altruism). Examples of this are the Open Data Directive and the forthcoming Data Governance Act. In this lies a conflict of interest between information security and open data that is not easy to solve. The challenge lies in the fact that each individual dataset itself does not have to reveal anything sensitive. However, if many datasets are added together, aggregated data can reveal too much. The EU is also interested in data sharing for certain sectors, of which vehicles and mobility is an area that is becoming more and more regulated in terms of data sharing. Here, a lot of new regulations are expected that will have a major impact on the sector, both in terms of vehicle development but also in terms of the development of new business models. The trend is towards vehicle manufacturers being increasingly forced to share data with authorities. When it comes to logistics, the pressure from new legislation about data sharing is not as clear. The existing legislation is more about the safe distribution of goods in a crisis or regarding sharing data from certain goods e.g., tobacco. What problems does the EU address in its mobility and vehicle regulations? • Human health: Compared to the general regulatory framework, there is a clear emphasis on human health and data sharing in the regulations. It is both about data sharing related to air quality but also road safety. • Consumer protection: There are also regulations aimed at strengthening consumer protection, e.g., for manufacturers to inform consumers about how much exhaust fumes a particular vehicle emits so that the consumer can make an informed choice based on this aspect between different manufacturers. • A free functioning efficient internal market: Examples of legislation in this area are the access of independent branded workshops to data from connected vehicles to increase competition. At EU level, there are several regulatory frameworks in the pipeline that will have a major impact on what we want to explore in our project. In the HITS2024 project, we want to explore and test efficient city logistics based on different vehicle concepts and logistics solutions. At EU level, a forthcoming e-Privacy Regulation is being discussed. The regulation will dictate how data from vehicles is allowed to be transfer to a cloud solution i.e., the connection as such. The e-Privacy Regulation is closely related to the GDPR, but there are also differences between these regulations. The GDPR accepts consent and balancing of interests to collect personal data while the e-Privacy Regulation only accepts consent (at the time of writing). The challenge for the automotive industry, for example, is that an autonomous vehicle can only collect personal data based on balancing interests because it is not doable to work with consent. However, if the e-Privacy Regulation in its current state is approved, the data will not be allowed to leave the vehicle because there is no consent. Another challenge is the upcoming AI Act. The AI Act distinguishes between technologies that already have an international regulatory framework for, e.g., type approval of a truck and technology where only the EU regulates the issue, e.g., machines. But a vehicle consists of many different “parts” and not all parts are type approved. How do you fit different technologies and different legislation together in an autonomous truck? In the logistics area, the upcoming Data Act can be of great importance as it will be about data sharing between companies. Until now, coordination between different data regulations has not always been optimal. The same phenomenon has been regulated in different regulations. There is a risk that different regulations in the future will find it difficult to co-exist with each other. How will, for example, GDPR, e-Privacy regulation and Data Act work together in a vehicle and logistics context? Developments in this area need to be followed.

Integer time series are often subject to constraints on the aggregation of the integer features of all occurrences of some pattern within the series. For example, the number of inflexions may be constrained, or the sum of the peak maxima, or the minimum of the peak widths. It is currently unknown how to maintain domain consistency efficiently on such constraints. We propose parametric ways of systematically deriving glue constraints, which are a particular kind of implied constraints, as well as aggregation bounds that can be added to the decomposition of time-series constraints [5]. We evaluate the beneficial propagation impact of the derived implied constraints and bounds, both alone and together.

First this report presents a restricted set of finite transducers used to synthesise structural time-series constraints described by means of a multi-layered function composition scheme. Second it provides the corresponding synthesised catalogue of structural time-series constraints where each constraint is explicitly described in terms of automata with accumulators.

Body area networks (BANs), cloud computing, and machine learning are platforms that can potentially enable advanced healthcare outside the hospital. By applying distributed sensors and drug delivery devices on/in our body and connecting to such communication and decision-making technology, a system for remote diagnostics and therapy is achieved with additional autoregulation capabilities. Challenges with such autarchic on-body healthcare schemes relate to integrity and safety, and interfacing and transduction of electronic signals into biochemical signals, and vice versa. Here, we report a BAN, comprising flexible on-body organic bioelectronic sensors and actuators utilizing two parallel pathways for communication and decision-making. Data, recorded from strain sensors detecting body motion, are both securely transferred to the cloud for machine learning and improved decision-making, and sent through the body using a secure body-coupled communication protocol to auto-actuate delivery of neurotransmitters, all within seconds. We conclude that both highly stable and accurate sensing—from multiple sensors—are needed to enable robust decision making and limit the frequency of retraining. The holistic platform resembles the self-regulatory properties of the nervous system, i.e., the ability to sense, communicate, decide, and react accordingly, thus operating as a digital nervous system. © 2021, The Author(s).

Denna rapport beskriver dels en metod att bedöma rangerbangårdens förmåga att hantera tågbildning och dels en pilotstudie som gjorts i Hallsberg i projektet PRAGGE2. Metoden, kallad PRAGGE-metoden, bygger på att en optimerande programvara utvecklad i tidigare projekt, kallad RanPlan, används för att undersöka det extra arbete som olika bangårdsutformningar ger upphov till. Extraarbete mäts som ett nyckeltal, ER (extra valldrag), som är en funktion av antalet vagnar som får hanteras flera gånger över rangervallen. Ju högre ER-värde desto arbetsammare är det för bangården att skapa de avgående tågen. Inom ramen för denna studie har riktningsgruppens antal spår samt längder undersökts, U-gruppens betydelse för Hallsbergs rangerbangård har belysts med speciellt fokus på den nuvarande situationen med det ibland förekommande tågkön in till infartsgruppen. Vidare har en enklare undersökning av ett ev. spårbehov för s.k. ”block-swaps” (byte av ett fåtal större vagnsgrupper mellan tåg) gjorts samt även ett försök att påbörja en kategorisering eller kapacitetsbeskrivning av en rangerbangård.

Följande rapport redovisar arbetet med en av två piloter i projektet PRAGGE2, undersökning av tre bangårdsalternativ för rustningen/ombyggnaden av Sävenäs rangerbangård i Göteborg. I detta arbete har en metod utvecklad under det första PRAGGE-projektet använts för att studera tre olika utvecklingsalternativ av rangerbangården i Sävenäs, framtagna av Sweco i separat projekt. PRAGGE-metoden bygger på att mäta det extra arbete i termer av att mäta det antal gånger en vagn rangeras extra på bangården på grund av trängsel, väsentligen för få spår att bygga avgående tåg på. Metoden utgår från den i utfallsdata definierade tidtabellen och bokningen och skapar en rangerplan med minimalt antal extra rangerade vagnar över vall. Green Cargo har för PRAGGE2 ställt data från 2014 års trafik till vårt förfogande. Resultatet från pilotstudien är att en av bangårdsutformningarna från Swecos tre framtagna alternativ inte bedöms ha tillräcklig kapacitet medan de två andra har utvärderats vidare. Den ena av dem bedömdes dock inte få plats på den yta som anses kunna ställas till förfogande varvid enbart ett alternativ återstår. Erfarenheten från piloten är att PRAGGE-metoden fungerar som en kvantitativ utvärderingsmetod för att bedöma kapaciteten på föreslagen grov utformning av rangerbangården och som kompletterar de kvalitativa utvärderingsmetoder som också används.

Vehicle platooning has been studied for several decades, with objectives such as improved traffic throughput on existing infrastructure or reduced energy consumption. All the time, it has been apparent that safety is an important issue. However, there are no comprehensive analyses of what is needed to achieve safety in platooning, but only scattered pieces of information. This paper investigates, through a systematic literature review, what is known about safety for platooning, including what analysis methods have been used, what hazards and failures have been identified, and solution elements that have been proposed to improve safety. Based on this, a gap analysis is performed to identify outstanding questions that need to be addressed in future research. These include dealing with a business ecosystem of actors that cooperate and compete around platooning, refining safety analysis methods to make them suitable for systems-of-systems, dealing with variability in vehicles, and finding solutions to various human factors issues.

Agile approaches have been highly influential to the software engineering practices in many organizations, and are increasingly being applied in larger companies, and for developing systems outside the pure software domain. To understand more about the current state of agile, its applications to safety-critical systems, and the consequences on innovation and large organizations, a seminar was organized in Stockholm in 2014. This paper gives an overview of the topics discussed at that seminar, a summary of the main results and suggestions for future work as input to a research agenda for agile development of safety-critical software.

Selecting sourcing options for software assets and components is an important process that helps companies to gain and keep their competitive advantage. The sourcing options include: in-house, COTS, open source and outsourcing. The objective of this paper is to further refine, extend and validate a solution presented in our previous work. The refinement includes a set of decision-making activities, which are described in the form of a process-line that can be used by decision-makers to build their specific decision-making process. We conducted five case studies in three companies to validate the coverage of the set of decision-making activities. The solution in our previous work was validated in two cases in the first two companies. In the validation, it was observed that no activity in the proposed set was perceived to be missing, although not all activities were conducted and the activities that were conducted were not executed in a specific order. Therefore, the refinement of the solution into a process-line approach increases the flexibility and hence it is better in capturing the differences in the decision-making processes observed in the case studies. The applicability of the process-line was then validated in three case studies in a third company

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.

Today, enterprise integration and cross-enterprise collaboration is becoming evermore important. The Internet of things, digitization and globalization are pushing continuous growth in the integration market. However, setting up integration systems today is still largely a manual endeavor. Most probably, future integration will need to leverage more automation in order to keep up with demand. This paper presents a first version of a system that uses tools from artificial intelligence and machine learning to ease the integration of information systems, aiming to automate parts of it. Three models are presented and evaluated for precision and recall using data from real, past, integration projects. The results show that it is possible to obtain F0.5 scores in the order of 80% for models trained on a particular kind of data, and in the order of 60%-70% for less specific models trained on a several kinds of data. Such models would be valuable enablers for integration brokers to keep up with demand, and obtain a competitive advantage. Future work includes fusing the results from the different models, and enabling continuous learning from an operational production system.

The proper alignment of requirements engineering and testing (RET) can be key to software's success. Three practices can provide effective RET alignment: using test cases as requirements, harvesting trace links, and reducing distances between requirements engineers and testers. The Web extra https://youtu.be/M65ZKxfxqME is an audio podcast of author Elizabeth Bjarnason reading the the Requirements column she cowrote with Markus Borg.

Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great opportunities, thus coined “Software 2.0,” but also great challenges for the engineering community to tackle. Due to the experimental approach used by data scientists when developing machine learning models, agility is an essential characteristic. In this keynote address, we discuss two contemporary development phenomena that are fundamental in machine learning development, i.e., notebook interfaces and MLOps. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context. Machine learning-based solutions are dynamic in nature, and we argue that reinforced continuous engineering is required to quality assure the trustworthy AI systems of tomorrow.

AI solutions seem to appear in any and all application domains. As AI becomes more pervasive, the importance of quality assurance increases. Unfortunately, there is no consensus on what artificial intelligence means and interpretations range from simple statistical analysis to sentient humanoid robots. On top of that, quality is a notoriously hard concept to pinpoint. What does this mean for AI quality? In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing. Finally, we present our ongoing work on establishing the AIQ Meta-Testbed. 

Artificial Intelligence (AI) is increasingly used in critical applications. Thus, the need for dependable AI systems is rapidly growing. In 2018, the European Commission appointed experts to a High-Level Expert Group on AI (AI-HLEG). AI- HLEG defined Trustworthy AI as 1) lawful, 2) ethical, and 3) robust and specified seven corresponding key requirements. To help development organizations, AI-HLEG recently published the Assessment List for Trustworthy AI (ALTAI). We present an illustrative case study from applying ALTAI to an ongoing development project of an Advanced Driver-Assistance System (ADAS) that relies on Machine Learning (ML). Our experience shows that ALTAI is largely applicable to ADAS development, but specific parts related to human agency and transparency can be disregarded. Moreover, bigger questions related to societal and environmental impact cannot be tackled by an ADAS supplier in isolation. We present how we plan to develop the ADAS to ensure ALTAI-compliance. Finally, we provide three recommendations for the next revision of ALTAI, i.e., life-cycle variants, domainspecific adaptations, and removed redundancy.

Abundant data is the key to successful machine learning. However, supervised learning requires annotated data that are often hard to obtain. In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier. AL can be successfully combined with self-training, i.e., extending a training set with the unlabelled examples for which a classifier is the most certain. We report our experiences on using AL in a systematic manner to train an SVM classifier for Stack Overflow posts discussing performance of software components. We show that the training examples deemed as the most valuable to the classifier are also the most difficult for humans to annotate. Despite carefully evolved annotation criteria, we report low inter-rater agreement, but we also propose mitigation strategies. Finally, based on one annotator's work, we show that self-training can improve the classification accuracy. We conclude the paper by discussing implication for future text miners aspiring to use AL and self-training.

With ever-increasing productivity targets in mining operations, there is a growing interest in mining automation. The PIMM project addresses the fundamental challenge of network communication by constructing a pilot 5G network in the underground mine Kankberg. In this report, we discuss how such a 5G network could constitute the essential infrastructure to organize existing systems in Kankberg into a system-of-systems (SoS). In this report, we analyze a scenario in which LiDAR equipped vehicles operating in the mine are connected to existing mine mapping and positioning solutions. The approach is motivated by the approaching era of remote controlled, or even autonomous, vehicles in mining operations. The proposed SoS could ensure continuously updated maps of Kankberg, rendered in unprecedented detail, supporting both productivity and safety in the underground mine. We present four different SoS solutions from an organizational point of view, discussing how development and operations of the constituent systems could be distributed among Boliden and external stakeholders, e.g., the vehicle suppliers, the hauling company, and the developers of the mapping software. The four scenarios are compared from both technical and business perspectives, and based on trade-off discussions and SWOT analyses. We conclude our report by recommending continued research along two future paths, namely a closer cooperation with the vehicle suppliers, and further feasibility studies regarding establishing a Kankberg software ecosystem.

Petri nets are a common method for modeling and simulation of systems biology application cases. Usually different Petri net concepts (e.g. discrete, hybrid, functional) are demanded depending on the purpose of the application cases. Modeling complex application cases requires a unification of those concepts, e.g. hybrid functional Petri nets (HFPN) and extended hybrid Petri nets (xHPN). Existing tools have certain limitations which motivated the extension of VANESA, an existing open-source editor for biological networks. The extension can be used to model, simulate, and visualize Petri nets based on the xHPN formalism. Moreover, it comprises additional functionality to support and help the user. Complex (kinetic) functions are syntactically analyzed and mathematically rendered. Based on syntax and given physical unit information, modeling errors are revealed. The numerical simulation is seamlessly integrated and executed in the background by the open-source simulation environment OpenModelica utilizing the Modelica library PNlib. Visualization of simulation results for places, transitions, and arcs are useful to investigate and understand the model and its dynamic behavior. The impact of single parameters can be revealed by comparing multiple simulation results. Simulation results, charts, and entire specification of the Petri net model as Latex file can be exported. All these features are shown in the demonstration case. The utilized Petri net formalism xHPN is fully specified and implemented in PNlib. This assures transparency, reliability, and comprehensible simulation results. Thus, the combination of VANESA and OpenModelica shape a unique open-source Petri net environment focusing on systems biology application cases. VANESA is available at: http://agbi.techfak.uni-bielefeld.de/vanesa. © 2021 The Authors

In order to be able to successfully defend an IT system it is useful to have an accurate appreciation of the cyber threat that goes beyond stereotypes. To effectively counter potentially decisive and skilled attackers it is necessary to understand, or at least model, their behavior. Although the real motives for untraceable anonymous attackers will remain a mystery, a thorough understanding of their observable actions can still help to create well-founded attacker profiles that can be used to design effective countermeasures and in other ways enhance cyber defense efforts. In recent work empirically founded attacker profiles, so-called attacker personas, have been used to assess the overall threat situation for an organization. In this paper we elaborate on 1) the use of attacker personas as a technique for attacker profiling, 2) the design of tailor-made cyber defense exercises for the purpose of obtaining the necessary empirical data for the construction of such attacker personas, and 3) how attacker personas can be used for enhancing the situational awareness within the cyber domain. The paper concludes by discussing the possibilities and limitations of using cyber defense exercises for data gathering, and what can and cannot be studied in such exercises.

Adequate privacy protection is crucial for implementing modern AI algorithms in medicine. With Fully Homomorphic Encryption (FHE), a party without access to the secret key can perform calculations and advanced analytics on encrypted data without taking part of either the input data or the results. FHE can therefore work as an enabler for situations where computations are carried out by parties that are denied plain text access to sensitive data. It is a scenario often found with digital services that process personal health-related data or medical data originating from a healthcare provider, for example, when the service is delivered by a third-party service provider located in the cloud. There are practical challenges to be aware of when working with FHE. The current work aims to improve accessibility and reduce barriers to entry by providing code examples and recommendations to aid developers working with health data in developing FHE-based applications. HEIDA is available on the GitHub repository: https://github.com/rickardbrannvall/HEIDA.

Developing  efficient  and  effective  decision  making support  includes  identifying  means  to  reduce  repeated  manual work  and  providing  possibilities  to  take  advantage  of  the  experience  gained  in  previous  decision  situations.  For  this  to  be possible,  there  is  a  need  to  explicitly  model  the  context  of  a decision  case,  for  example  to  determine  how  much  the  evidence from one decision case can be trusted in another, similar context. In earlier work, context has been recognized as important when transferring  and  understanding  outcomes  between  cases.  The contribution of this paper is threefold. First, we describe different ways   of   utilizing   context   in   an   envisioned   decision   support system.  Thereby,  we  distinguish  between  internal  and  external context  usage,  possibilities  of  context  representation,  and  context  inheritance.  Second,  we  present  a  systematically  developed context  model  comprised  of  five  types  of  context  information, namely organization, product, stakeholder, development method &  technology,  and  market  &  business.  Third,  we  exemplary illustrate the relation of the context information to architectural decision  making  using  existing  literature.

Extracting semantically useful natural language sentence representations frompre-trained deep neural networks such as Transformers remains a challenge. Wefirst demonstrate that pre-training objectives impose a significant task bias ontothe final layers of models, with a layer-wise survey of the Semantic Textual Similarity (STS) correlations for multiple common Transformer language models. Wethen propose a new self-supervised method called Contrastive Tension (CT) tocounter such biases. CT frames the training objective as a noise-contrastive taskbetween the final layer representations of two independent models, in turn makingthe final layer representations suitable for feature extraction. Results from multiple common unsupervised and supervised STS tasks indicate that CT outperformsprevious State Of The Art (SOTA), and when combining CT with supervised datawe improve upon previous SOTA results with large margins.

We propose a portfolio of exact and metaheuristic methods for the rich examination timetabling problem introduced by Battistutta et al. (in: Hebrard, Musliu (eds) 17th International conference on the integration of constraint programming, artificial intelligence, and operations research (CPAIOR-2020), LNCS, vol 12296. Springer, Berlin, pp 69–81, 2020). The problem includes several real-world features that arise in Italian universities, such as examinations split into two parts, possible requirements of multiple rooms for a single examination, and unavailabilities and preferences for periods and rooms. We developed a CP model encoded in the MiniZinc modeling language and solved it with Gecode, as well as two MIP models solved with Gurobi. The first MIP model is encoded natively and the second one again in MiniZinc. Finally, we extended the metaheuristic method based on simulated annealing of Battistutta et al. by introducing a new neighborhood relation. We compare the different techniques on the real-world instances provided by Battistutta et al., which have been slightly refined by correcting some semantic issues. Finally, we developed a solution checker that is publicly available, together with all instances and solutions, for inspection and future comparisons.

With the rapid development of artificial intelligence technology, the deep learning method has been introduced for vehicle trajectory prediction in the internet of vehicles, since it provides relative accurate prediction results, which is one of the critical links to guarantee security in the distributed mixed-driving scenario. In order to further enhance prediction accuracy by making full utilization of complex traffic scenes, an improved multimodal trajectory prediction method based on deep inverse reinforcement learning is proposed. Firstly, a fused dilated convolution module for better extracting raster features is introduced into the existing multimodal trajectory prediction network backbone. Then, a reward update policy with inferred goals is improved by learning the state rewards of goals and paths separately instead of original complex rewards, which can reduce the requirement for predefined goal states. Furthermore, a correction factor is introduced in the existing trajectory generator module, which can better generate diverse trajectories by penalizing trajectories with little difference. Abundant experiments on the current popular public dataset indicate that the prediction results of our proposed method are a better fit with the basic structure of the given traffic scenario in a long-term prediction range, which verifies the effectiveness of our proposed method. © 2022 by the authors.

As design, deployment and operation complexity increase in mobile systems, adaptive self-learning techniques have become essential enablers in mitigation and control of the complexity problem. Artificial intelligence and, in particular, reinforcement learning has shown great potential in learning complex tasks through observations. The majority of ongoing reinforcement learning research activities focus on single-Agent problem settings with an assumption of accessibility to a globally observable state and action space. In many real-world settings, such as LTE or 5G, decision making is distributed and there is often only local accessibility to the state space. In such settings, multi-Agent learning may be preferable, with the added challenge of ensuring that all agents collaboratively work towards achieving a common goal. We present a novel cooperative and distributed actor-critic multi-Agent reinforcement learning algorithm. We claim the approach is sample efficient, both in terms of selecting observation samples and in terms of assignment of credit between subsets of collaborating agents. 

The number of connected mobile devices is increasing rapidly with more than 10 billion expected by 2022. Their total aggregate energy consumption poses a significant concern to society. The current 3gpp (3rd Generation Partnership Project) LTE/LTE-Advanced standard incorporates an energy saving technique called discontinuous reception (DRX). It is expected that 5G will use an evolved variant of this scheme. In general, the single selection of DRX parameters per device is non trivial. This paper describes how to improve energy efficiency of mobile devices by selecting DRX based on the traffic profile per device. Our particular approach uses a two phase data-driven strategy which tunes the selection of DRX parameters based on a smart fast energy model. The first phase involves the off-line selection of viable DRX combinations for a particular traffic mix. The second phase involves an on-line selection of DRX from this viable list. The method attempts to guarantee that latency is not worse than a chosen threshold. Alternatively, longer battery life for a device can be traded against increased latency. We built a lab prototype of the system to verify that the technique works and scales on a real LTE system. We also designed a sophisticated traffic generator based on actual user data traces. Complementary method verification has been made by exhaustive off-line simulations on recorded LTE network data. Our approach shows significant device energy savings, which has the aggregated potential over billions of devices to make a real contribution to green, energy efficient networks.

With the advent of the smart industry, Industrial Control Systems (ICS) moved from isolated environments to connected platforms to meet Industry 4.0 targets. The inherent connectivity in these services exposes such systems to increased cybersecurity risks. To protect ICSs against cyberattacks, intrusion detection systems (IDS) empowered by machine learning are used to detect abnormal behavior of the systems. Operational ICSs are not safe environments to research IDSs due to the possibility of catastrophic risks. Therefore, realistic ICS testbeds enable researchers to analyze and validate their IDSs in a controlled environment. Although various ICS testbeds have been developed, researchers’ access to a low-cost, extendable, and customizable testbed that can accurately simulate ICSs and suits security research is still an important issue. In this paper, we present ICSSIM, a framework for building customized virtual ICS security testbeds in which various cyber threats and network attacks can be effectively and efficiently investigated. This framework contains base classes to simulate control system components and communications. Simulated components are deployable on actual hardware such as Raspberry Pis, containerized environments like Docker, and simulation environments such as GNS-3. ICSSIM also offers physical process modeling using software and hardware in the loop simulation. This framework reduces the time for developing ICS components and aims to produce extendable, versatile, reproducible, low-cost, and comprehensive ICS testbeds with realistic details and high fidelity. We demonstrate ICSSIM by creating a testbed and validating its functionality by showing how different cyberattacks can be applied. © 2023 The Authors

Over the past few decades, Industrial Control Systems (ICS) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. Although a few commonly used datasets may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. This paper introduces the ’ICS-Flow’ dataset, which offers network data and process state variables logs for supervised and unsupervised ML-based IDS assessment. The network data includes normal and anomalous network packets and flows captured from simulated ICS components and emulated networks, where the anomalies were applied to the system through various cyberattacks. We also proposed an open-source tool, ’ICSFlowGenerator,’ for generating network flow parameters from Raw network packets. The final dataset comprises over 25,000,000 raw network packets, network flow records, and process variable logs. The paper describes the methodology used to collect and label the dataset and provides a detailed data analysis. Finally, we implement several ML models, including the decision tree, random forest, and artificial neural network to detect anomalies and attacks, demonstrating that our dataset can be used effectively for training intrusion detection ML models.