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  • 1.
    Al Mamun, Abdullah
    et al.
    Division of Software Engineering Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
    Berger, Christian
    Division of Software Engineering Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
    Hansson, Jörgen
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Explicating, Understanding and Managing Technical Debt from Self-Driving Miniature Car Projects2014In: Proceedings 2014 6th IEEE International Workshop on Managing Technical Debt: MTD 2014, Los Alamitos, CA: IEEE Computer Society, 2014, p. 11-18Conference paper (Refereed)
    Abstract [en]

    Technical debt refers to various weaknesses in the design or implementation of a system resulting from trade-offs during software development usually for a quick release. Accumulating such debt over time without reducing it can seriously hamper the reusability and maintainability of the software. The aim of this study is to understand the state of the technical debt in the development of self-driving miniature cars so that proper actions can be planned to reduce the debt to have more reusable and maintainable software. A case study on a selected feature from two self-driving miniature car development projects is performed to assess the technical debt. Additionally, an interview study is conducted involving the developers to relate the findings of the case study with the possible root causes. The result of the study indicates that "the lack of knowledge" is not the primary reason for the accumulation of technical debt from the selected code smells. The root causes are rather in factors like time pressure followed by issues related to software/hardware integration and incomplete refactoring as well as reuse of legacy, third party, or open source code.

  • 2.
    Atif, Yacine
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Ding, Jianguo
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Lindström, Birgitta
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Jeusfeld, Manfred
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Andler, Sten F.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Yuning, Jiang
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Brax, Christoffer
    CombiTech AB, Skövde, Sweden.
    Gustavsson, Per M.
    CombiTech AB, Skövde, Sweden.
    Cyber-Threat Intelligence Architecture for Smart-Grid Critical Infrastructures Protection2017Conference paper (Refereed)
    Abstract [en]

    Critical infrastructures (CIs) are becoming increasingly sophisticated with embedded cyber-physical systems (CPSs) that provide managerial automation and autonomic controls. Yet these advances expose CI components to new cyber-threats, leading to a chain of dysfunctionalities with catastrophic socio-economical implications. We propose a comprehensive architectural model to support the development of incident management tools that provide situation-awareness and cyber-threats intelligence for CI protection, with a special focus on smart-grid CI. The goal is to unleash forensic data from CPS-based CIs to perform some predictive analytics. In doing so, we use some AI (Artificial Intelligence) paradigms for both data collection, threat detection, and cascade-effects prediction. 

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  • 3.
    Barrera Diaz, Carlos Alberto
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Simulation-based multi-objective optimization for reconfigurable manufacturing systems: Reconfigurability, manufacturing, simulation, optimization, rms, multi-objective, knowledge discovery2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In today’s global and aggressive market system, for manufacturing companies to remain competitive, they must manufacture high-quality products that can be produced at a low cost; they also must respond efficiently to customers’ predictable and unpredictable needs and demand variations. Increasingly shortened product lifecycles, as well as product customization degrees, lead to swift changes in the market that need to be supported by capable and flexible resources able to produce faster and deliver to the market in shorter periods while maintaining a high degree of cost-efficiency. To cope with all these challenges, the setup of production systems needs to shift toward Reconfigurable Manufacturing Systems (RMSs), making production capable of rapidly and economically changing its functionality and capacity to face uncertainties, such as unforeseen market variations and product changes. Despite the advantages of RMSs, designing and managing these systems to achieve a high-efficiency level is a complex and challenging task that requires optimization techniques. Simulation-based optimization (SBO) methods have been proven to improve complex manufacturing systems that are affected by predictable and unpredictable events. However, the use of SBO methods to tackle challenging RMS design and management processes is underdeveloped and rarely involves Multi-Objective Optimization (MOO). Only a few attempts have applied Simulation-Based Multi-Objective Optimization (SMO) to simultaneously deal with multiple conflictive objectives. Furthermore, due to the intrinsic complexity of RMSs, manufacturing organizations that embrace this type of system struggle with areas such as system configuration, number of resources, and task assignment. Therefore, this dissertation contributes to such areas by employing SMO to investigate the design and management of RMSs. The benefits for decision-makers have been demonstrated when SMO is employed toward RMS-related challenges. These benefits have been enhanced by combining SMO with knowledge discovery and Knowledge-Driven Optimization (KDO). This combination has contributed to current research practices proving to be an effective and supportive decision support tool for manufacturing organizations when dealing with RMS challenges.

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  • 4.
    Eriksson, Patric
    et al.
    University of Skövde, Department of Engineering Science.
    Moore, Philip
    Mechatronics Research group, School of Engineering and Manufacture, De Montfort University, Leicester, UK.
    A role for 'sensor simulation' and 'pre-emptive learning' in computer aided robotics1995In: 26th International Symposium on Industrial Robots, Symposium Proceedings: Competitive automation: new frontiers, new opportunities, Mechanical Engineering Publ. , 1995, p. 135-140Conference paper (Refereed)
    Abstract [en]

    Sensor simulation in Computer Aided Robotics (CAR) can enhance the capabilities of such systems to enable off-line generation of programmes for sensor driven robots. However, such sensor simulation is not commonly supported in current computer aided robotic environments. A generic sensor object model for the simulation of sensors in graphical environments is described in this paper. Such a model can be used to simulate a variety of sensors, for example photoelectric, proximity and ultrasonic sensors. Tests results presented here show that this generic sensor model can be customised to emulate the characteristics of the real sensors. The preliminary findings from the first off-line trained mobile robot are presented. The results indicate that sensor simulation within CARs can be used to train robots to adapt to changing environments.

  • 5.
    Hansson, Jörgen
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Helton, Steve
    The Boeing Company, USA.
    Feiler, Peter H.
    Carnegie Mellon University, Software Engineering Institute, Pittsburgh, PA, USA.
    ROI Analysis of the System Architecture Virtual Integration Initiative2018Report (Other academic)
    Abstract [en]

    The System Architecture Virtual Integration (SAVI) initiative is a multiyear, multimillion dollar program that is developing the capability to virtually integrate systems before designs are implemented and tested on hardware. The purpose of SAVI is to develop a means of countering the costs of exponentially increasing complexity in modern aerospace software systems. The program is sponsored by the Aerospace Vehicle Systems Institute, a research center of the Texas Engineering Experiment Station, which is a member of the Texas A&M University System. This report presents an analysis of the economic effects of the SAVI approach on the development of software-reliant systems for aircraft compared to existing development paradigms. The report describes the detailed inputs and results of a return-on-investment (ROI) analysis to determine the net present value of the investment in the SAVI approach. The ROI is based on rework cost-avoidance attributed to earlier discovery of requirements errors through analysis of virtually integrated models of the embedded software system expressed in the SAE International Architecture Analysis and Design Language (AADL) standard architecture modeling language. The ROI analysis uses conservative estimates of costs and benefits, especially for those parameters that have a proven, strong correlation to overall system-development cost. The results of the analysis, in part, show that the nominal cost reduction for a system that contains 27 million source lines of code would be $2.391 billion (out of an estimated $9.176 billion), a 26.1% cost savings. The original study, reported here, had a follow-on study to validate and further refine the estimated cost savings.

  • 6.
    Jiang, Yuning
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Dynamic and Automatic Vulnerability Assessment for Cyber-Physical System2019Conference paper (Other academic)
    Abstract [en]

    Assessing vulnerabilities supports analytics-based decision-making processes to protect Critical Infrastructures (CIs), in order to focus on specific risks rising from threat-exploitability with varying degrees of impact-severity. The notion of risk remains elusive, as evidenced by the increasing investigations on CIs security operations centres (SOCs) where analysts employ various detection, assessment, and defence mechanisms to monitor security events. Normally, SOCs involve advances of multiple automated security tools such as network vulnerability scanners and Common Vulnerability Scoring System (CVSS), combined with analysis of data contained and produced by cyber-physical system (CPS) as well as alarms retrieved from vulnerability repositories such as Common Vulnerability Exposure (CVE). The security operators need further to forecast the match between these vulnerabilities and the state of intricate CIs layer networks, while prioritising patching investments using vulnerability-scoring mechanisms. This process shows the central role of security operators in SOCs and their need for support to keep pace with dynamically evolving vulnerability-alert repositories. Recent advances in data analytics also prompt dynamic data-driven vulnerability assessments whereby data contained and produced by CPS include hidden traces of vulnerability fingerprints. However, the huge volume of scanned data requires high capability of information processing and analytical reasoning, which could not be satisfied considering the imprecise nature of manual vulnerability assessment.

    A knowledge-base system that consolidates both sides into empirical rules appears to be missing, yet it promises to offer a suitable level of decision-support. In our research, we propose a dynamic and automated vulnerability-assessment approach. The proposed streamlined approach employs computational intelligence techniques to analyse data retrieved from vulnerability-alert repositories and CPS layer networks within an innovative accurate and automatic scoring system, away from traditional manual and highly subjective mechanisms. Our approach suggests to substitute offline, costly, error-prone and pure subjective vulnerability assessment processes with an automatic, accurate and data-evidenced approach, to improve situation awareness and to support security decision making. In doing so, we investigate judicious computational-intelligence techniques such as fuzzy-logic, machine learning and data mining, applied to vulnerability assessment problems.

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  • 7.
    Jiang, Yuning
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Vulnerability Analysis for Critical Infrastructures2022Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The rapid advances in information and communication technology enable a shift from diverse systems empowered mainly by either hardware or software to cyber-physical systems (CPSs) that are driving Critical infrastructures (CIs), such as energy and manufacturing systems. However, alongside the expected enhancements in efficiency and reliability, the induced connectivity exposes these CIs to cyberattacks exemplified by Stuxnet and WannaCry ransomware cyber incidents. Therefore, the need to improve cybersecurity expectations of CIs through vulnerability assessments cannot be overstated. Yet, CI cybersecurity has intrinsic challenges due to the convergence of information technology (IT) and operational technology (OT) as well as the crosslayer dependencies that are inherent to CPS based CIs. Different IT and OT security terminologies also lead to ambiguities induced by knowledge gaps in CI cybersecurity. Moreover, current vulnerability-assessment processes in CIs are mostly subjective and human-centered. The imprecise nature of manual vulnerability assessment operations and the massive volume of data cause an unbearable burden for security analysts. Latest advances in machine-learning (ML) based cybersecurity solutions promise to shift such burden onto digital alternatives. Nevertheless, the heterogeneity, diversity and information gaps in existing vulnerability data repositories hamper accurate assessments anticipated by these ML-based approaches. Therefore, a comprehensive approach is envisioned in this thesis to unleash the power of ML advances while still involving human operators in assessing cybersecurity vulnerabilities within deployed CI networks.Specifically, this thesis proposes data-driven cybersecurity indicators to bridge vulnerability management gaps induced by ad-hoc and subjective auditing processes as well as to increase the level of automation in vulnerability analysis. The proposed methodology follows design science research principles to support the development and validation of scientifically-sound artifacts. More specifically, the proposed data-driven cybersecurity architecture orchestrates a range of modules that include: (i) a vulnerability data model that captures a variety of publicly accessible cybersecurity-related data sources; (ii) an ensemble-based ML pipeline method that self-adjusts to the best learning models for given cybersecurity tasks; and (iii) a knowledge taxonomy and its instantiated power grid and manufacturing models that capture CI common semantics of cyberphysical functional dependencies across CI networks in critical societal domains. This research contributes data-driven vulnerability analysis approaches that bridge the knowledge gaps among different security functions, such as vulnerability management through related reports analysis. This thesis also correlates vulnerability analysis findings to coordinate mitigation responses in complex CIs. More specifically, the vulnerability data model expands the vulnerability knowledge scope and curates meaningful contexts for vulnerability analysis processes. The proposed ML methods fill information gaps in vulnerability repositories using curated data while further streamlining vulnerability assessment processes. Moreover, the CI security taxonomy provides disciplined and coherent support to specify and group semanticallyrelated components and coordination mechanisms in order to harness the notorious complexity of CI networks such as those prevalent in power grids and manufacturing infrastructures. These approaches learn through interactive processes to proactively detect and analyze vulnerabilities while facilitating actionable insights for security actors to make informed decisions.

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  • 8.
    Jiang, Yuning
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Atif, Yacine
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    An Approach to Discover and Assess Vulnerability Severity Automatically in Cyber-Physical Systems2020In: Proceedings of the 13th International Conference on Security of Information and Networks: November 4-6, 2020, virtual, Istanbul, Turkey / [ed] Berna Örs, Atilla Elçi, New York, NY, USA: Association for Computing Machinery (ACM), 2020, article id 9Conference paper (Refereed)
    Abstract [en]

    Current vulnerability scoring mechanisms in complex cyber-physical systems (CPSs) face challenges induced by the proliferation of both component versions and recurring scoring-mechanism versions. Different data-repository sources like National Vulnerability Database (NVD), vendor websites as well as third party security tool analysers (e.g. ICS CERT and VulDB) may provide conflicting severity scores. We propose a machine-learning pipeline mechanism to compute vulnerability severity scores automatically. This method also discovers score correlations from established sources to infer and enhance the severity consistency of reported vulnerabilities. To evaluate our approach, we show through a CPS-based case study how our proposed scoring system automatically synthesises accurate scores for some vulnerability instances, to support remediation decision-making processes. In this case study, we also analyse the characteristics of CPS vulnerability instances. 

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  • 9.
    Jiang, Yuning
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Atif, Yacine
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Ding, Jianguo
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Multi-Level Vulnerability Modeling of Cyber-Physical Systems2018Conference paper (Refereed)
    Abstract [en]

    Vulnerability is defined as ”weakness of an asset or control that can be exploited by a threat” according to ISO/IEC 27000:2009, and it is a vital cyber-security issue to protect cyber-physical systems (CPSs) employed in a range of critical infrastructures (CIs). However, how to quantify both individual and system vulnerability are still not clear. In our proposed poster, we suggest a new procedure to evaluate CPS vulnerability. We reveal a vulnerability-tree model to support the evaluation of CPS-wide vulnerability index, driven by a hierarchy of vulnerability-scenarios resulting synchronously or propagated by tandem vulnerabilities throughout CPS architecture, and that could be exploited by threat agents. Multiple vulnerabilities are linked by boolean operations at each level of the tree. Lower-level vulnerabilities in the tree structure can be exploited by threat agents in order to reach parent vulnerabilities with increasing CPS criticality impacts. At the asset-level, we suggest a novel fuzzy-logic based valuation of vulnerability along standard metrics. Both the procedure and fuzzy-based approach are discussed and illustrated through SCADA-based smart power-grid system as a case study in the poster, with our goal to streamline the process of vulnerability computation at both asset and CPS levels.

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  • 10.
    Jiang, Yuning
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Atif, Yacine
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Ding, Jianguo
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Wang, Wei
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    A Semantic Framework With Humans in the Loop for Vulnerability-Assessment in Cyber-Physical Production Systems2020In: Risks and Security of Internet and Systems: 14th International Conference, CRiSIS 2019, Hammamet, Tunisia, October 29–31, 2019, Proceedings / [ed] Slim Kallel, Frédéric Cuppens, Nora Cuppens-Boulahia, Ahmed Hadj Kacem, Springer, 2020, Vol. 12026, p. 128-143Conference paper (Refereed)
    Abstract [en]

    Criticalmanufacturingprocessesinsmartnetworkedsystems such as Cyber-Physical Production Systems (CPPSs) typically require guaranteed quality-of-service performances, which is supported by cyber- security management. Currently, most existing vulnerability-assessment techniques mostly rely on only the security department due to limited communication between di↵erent working groups. This poses a limitation to the security management of CPPSs, as malicious operations may use new exploits that occur between successive analysis milestones or across departmental managerial boundaries. Thus, it is important to study and analyse CPPS networks’ security, in terms of vulnerability analysis that accounts for humans in the production process loop, to prevent potential threats to infiltrate through cross-layer gaps and to reduce the magnitude of their impact. We propose a semantic framework that supports the col- laboration between di↵erent actors in the production process, to improve situation awareness for cyberthreats prevention. Stakeholders with dif- ferent expertise are contributing to vulnerability assessment, which can be further combined with attack-scenario analysis to provide more prac- tical analysis. In doing so, we show through a case study evaluation how our proposed framework leverages crucial relationships between vulner- abilities, threats and attacks, in order to narrow further the risk-window induced by discoverable vulnerabilities.

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  • 11.
    Jiang, Yuning
    et al.
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Ding, Jianguo
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Atif, Yacine
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Jeusfeld, Manfred
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Andler, Sten
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Lindström, Birgitta
    University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment.
    Brax, Christoffer
    Combitech, Sweden.
    Haglund, Daniel
    Combitech, Sweden.
    Complex Dependencies Analysis: Technical Description of Complex Dependencies in Critical Infrastructures, i.e. Smart Grids. Work Package 2.1 of the ELVIRA Project2018Report (Other academic)
    Abstract [en]

    This document reports a technical description of ELVIRA project results obtained as part of Work-package 2.1 entitled “Complex Dependencies Analysis”. In this technical report, we review attempts in recent researches where connections are regarded as influencing factors to  IT systems monitoring critical infrastructure, based on which potential dependencies and resulting disturbances are identified and categorized. Each kind of dependence has been discussed based on our own entity based model. Among those dependencies, logical and functional connections have been analysed with more details on modelling and simulation techniques.

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    ELVIRA_2.1-HS-IIT-TR-18-003.Complex-Dependencies-Analysis
  • 12.
    Köks, Tönu
    et al.
    University of Skövde, Department of Industrial Management.
    Nordqvist, Anders R.
    University of Skövde, Department of Industrial Management.
    Using discrete event simulation to analyse production capacity utilization2003In: ICOM 2003: International Conference on Mechatronics / [ed] R. M. Parkin; A. Al-Haibeh; M. R. Jackson, Professional Engineering Publishing, 2003, p. 117-122Conference paper (Refereed)
    Abstract [en]

    Computer simulation, because it can be applied to operational problems that are too difficult to model and solve analytically, is an especially effective tool to help analyse Supply/Demand Chain (S/DC) [1] capacity utilization issues [2]. As part of the ongoing research project daisy [3] has a case study for analysing the benefit of interfacing an Enterprise Resource planning system (ERP) with a system for computer simulation. Connecting simulation tools to systems for ERP in order to import necessary input data to simulate production plans would be a proper way to apply computer simulation tools to this type of problems. Simulating production plans could have different purposes, e.g. minimizing waste of resources or setting accurate delivery dates. Technically the connection between the two systems, i.e. the simulation system and the ERP system, could be made in an uncomplicated manner, for instance using files or network-based solutions. Different purposes will require different response time between the two systems involved and thereby different solutions for the connection. The main question in this study is not the technical connection but rather which data to import and the needed quality of these data in order to analyse capacity utilisation. For those ho have built real life simulation models lack of realistically data is a well-known fact. This is due to obsolete or inaccurate product data in ERP systems.

  • 13.
    Laurell, Isak
    et al.
    University of Skövde, School of Engineering Science.
    Sjöholm, Linus
    University of Skövde, School of Engineering Science.
    System för insamling av väderdata2017Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
    Abstract [sv]

    På försvarets materielverk (FMV) i Karlsborg utförs prover av militära vapensystem. Ofta när prov genomförs samlas också väderdata in för att veta om yttre faktorer kan påverka en träffbild. Insamling av väderdata är en manuell process som FMV önskar att automatisera. Målet är att skapa ett system med hjälp av en befintlig vädersensor som kan visa väderparametrar i realtid på en skärm samt spara dem vid rätt tillfälle på ett flyttbart media. Vid utveckling av system för insamling av väderdata ska det väljas ut lämplig hårdvara där även mjukvaran ska formas. Den teoretiska referensramen och litteraturstudierna utgjorde en bra grund för hela projektet genom att få en förståelse om alla de delar som måste tas hänsyn till. FMV har som krav att hårdvaran ska vara baserad på en integrerad krets. Det innebär kortfattat att det inte finns något operativsystem utan att det består endast av en källkod. Den befintliga vädersensorn som ska användas kommunicerar via seriell kommunikation. Det gjordes också ett val mellan två utvecklingsmodeller där en av dem senare ska användas som ett hjälpmedel för utvecklingsprocessen av hela systemet. Den utvecklingsmodell som var mest lämplig för projektet var V-modellen. Genom att använda V-modellen togs det fram en bra struktur för hur arbetet skulle läggas upp gällande planering, verkställande och testning. Med en detaljerad planering underlättade det verkställandet genom att det fanns en tydlig bild av vad som skulle göras. Den noga planeringen gjorde att fel och brister i systemet vid testerna var få. En nyckelfaktor för utvecklingsprocessen var också att ha ett bra samarbete med FMV:s personal vilket gjorde att utvecklingen följde deras vision om systemet. När utvecklingsprocessen och arbetet med V-modellen var genomfört var också ett system för insamling av väderdata framtaget och godkänt av FMV. Systemet generar en minskad hantering av data eftersom det utför det arbete som tidigare har varit manuellt. Det generar också stabilare mätvärden eftersom tidpunkten för loggningen blir mer exakt än tidigare.

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  • 14.
    Liebel, Grischa
    et al.
    Software Engineering Division, Chalmers / University of Gothenburg, Sweden.
    Marko, Nadja
    Virtual Vehicle Research Center, Graz, Austria.
    Tichy, Matthias
    Software Engineering Division, Chalmers / University of Gothenburg, Sweden.
    Leitner, Andrea
    Virtual Vehicle Research Center, Graz, Austria.
    Hansson, Jörgen
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Assessing the state-of-practice of model-based engineering in the embedded systems domain2014In: Model-Driven Engineering Languages and Systems: 17th International Conference, MODELS 2014, Valencia, Spain, September 28 – October 3, 2014. Proceedings / [ed] Juergen Dingel, Wolfram Schulte, Isidro Ramos, Silvia Abrahão, Emilio Insfran, Springer, 2014, p. 166-182Conference paper (Refereed)
    Abstract [en]

    Abstract Model-Based Engineering (MBE) aims at increasing the effectiveness of engineering by using models as key artifacts in the development process. While empirical studies on the use and the effects of MBE in industry exist, there is only little work targeting the embedded systems domain. We contribute to the body of knowledge with a study on the use and the assessment of MBE in that particular domain. We collected quantitative data from 112 subjects, mostly professionals working with MBE, with the goal to assess the ...

  • 15.
    Mellin, Jonas
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Andler, Sten F.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    The effect of optimizing engine control on fuel consumption and roll amplitude in ocean-going vessels: An experimental study2015Report (Other academic)
    Abstract [en]

    We use data-generated models based on data from experiments of an ocean-going vessel to study the effect of optimizing fuel consumption. The optimization is an add-on module to the existing diesel-engine fuel-injection control built by Q-TAGG R&D AB. The work is mainly a validation of knowledge-based models based on a priori knowledge from physics. The results from a simulation-based analysis of the predictive models built on data agree with the results based on knowledge-based models in a companion study. This indicates that the optimization algorithm saves fuel. We also address specific problems of adapting data to existing machine learning methods. It turns out that we can simplify the problem by ignoring the auto-correlative effects in the time series by employing low-pass filters and resampling techniques. Thereby we can use mature and robust classification techniques with less requirements on the data to demonstrate that fuel is saved compared to the full-fledged time series analysis techniques which are harder to use. The trade-off is the accuracy of the result, that is, it is hard to tell exactly how much fuel is saved. In essence, however, this process can be automated due to its simplicity. 

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  • 16.
    Rana, Rakesh
    et al.
    Department of Computer Science and Engineering, Chalmers, University of Gothenburg, Gothenburg, Sweden.
    Staron, Miroslaw
    Department of Computer Science and Engineering, Chalmers, University of Gothenburg, Gothenburg, Sweden.
    Hansson, Jörgen
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Nilsson, Martin
    Volvo Car Group, Gothenburg, Sweden.
    Meding, Wilhelm
    Ericsson, Gothenburg, Sweden.
    Software defect prediction in automotive and telecom domain: A life-cycle approach2015In: Software Technologies: 9th International Joint Conference, ICSOFT 2014, Vienna, Austria, August 29-31, 2014, Revised Selected Papers / [ed] Andreas Holzinger, Jorge Cardoso, José Cordeiro, Therese Libourel, Leszek A. Maciaszek, Marten van Sinderen, Cham: Springer, 2015, p. 217-232Chapter in book (Refereed)
    Abstract [en]

    Embedded software is playing an ever increasing role in providing functionality and user experience. At the same time, size and complexity of this software is also increasing which bring new challenges for ensuring quality and dependability. For developing high quality software with superior dependability characteristics requires an effective software development process with greater control. Methods of software defect predictions can help optimize the software verification and validation activities by providing useful information for test resource allocation and release planning decisions. We review the software development and testing process for two large companies from the automotive and telecom domain and map different defect prediction methods and their applicability to their lifecycle phases. Based on the overview and current trends we also identify possible directions for software defect prediction techniques and application in these domains. © Springer International Publishing Switzerland 2015.

  • 17.
    Riveiro, Maria
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Lebram, Mikael
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Warston, Håkan
    Saab Electronic Defence Systems, Gothenburg, Sweden.
    On visualizing threat evaluation configuration processes: A design proposal2014In: 17th International Conference on Information Fusion (FUSION), 2014, IEEE conference proceedings, 2014, p. 1-8Conference paper (Refereed)
    Abstract [en]

    Threat evaluation is concerned with estimating the intent, capability and opportunity of detected objects in relation to our own assets in an area of interest. To infer whether a target is threatening and to which degree is far from a trivial task. Expert operators have normally to their aid different support systems that analyze the incoming data and provide recommendations for actions. Since the ultimate responsibility lies in the operators, it is crucial that they trust and know how to configure and use these systems, as well as have a good understanding of their inner workings, strengths and limitations. To limit the negative effects of inadequate cooperation between the operators and their support systems, this paper presents a design proposal that aims at making the threat evaluationprocess more transparent. We focus on the initialization, configuration and preparation phases of thethreat evaluation process, supporting the user in the analysis of the behavior of the system considering the relevant parameters involved in the threat estimations. For doing so, we follow a known design process model and we implement our suggestions in a proof-of-concept prototype that we evaluate with military expert system designers.

  • 18.
    Ujvari, Sandor
    et al.
    University of Skövde, Department of Engineering Science.
    Eriksson, Patric
    Research Division, Prosolvia Systems AB, Vänersborg, Sweden.
    Moore, Philip
    Mechatronics Research Group, Faculty of Computing Sciences and Engineering, De Montfort University, Leicester, UK.
    Pu, Junsheng
    Mechatronics Research Group, Faculty of Computing Sciences and Engineering, De Montfort University, Leicester, UK.
    Simulation and emulation of sensor systems for intelligent vehicles1998In: Mechatronics '98: Proceedings of the 6th UK Mechatronics Forum International Conference, Skövde, Sweden, 9-11 September 1998 / [ed] Josef Adolfsson; Jeanette Karlsén , Pergamon Press, 1998, no 6th UK Mechatronics Forum International Conference, p. 385-390Conference paper (Refereed)
    Abstract [en]

    Simulation of sensor systems for mobile robots are described in this paper. By simulation of smart sensor systems, the performance of semi-autonomous vehicles / mobile robots can be enhanced. Smart sensor systems used in the field of mobile robotics can utilise adaptive algorithms. e. g. artificial neural nets, fuzzy logic or hybrid variants of these systems. The development, training and evaluation of adaptive algorithms for sensor systems can be done within a virtual environment in which graphical models are built to simulate an intelligent vehicle, its sensors, and its environment. The virtual sensors are validated by comparing the characteristics of the virtual sensors with those of the real devices.

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