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  • 1.
    Olsson, Thomas
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Understanding and Supporting Quality Requirements Engineering in Software-intensive Product Development2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    [Background] Quality requirements deal with how well a product should perform the intended functionality. Failure to meet essential quality requirements can result in customer dissatisfaction, unusable products, or extra costs. [Objective] The aim is to identify challenges and needs in practice and design solutions for quality requirements engineering which can be applied in practice. [Results] In the two exploratory studies quality requirements engineering practices are investigated. I confirm that some quality requirements fulfillment is not simply being implemented or not, rather evaluated on a scale. Furthermore, some quality requirements are cross-functional. Also, the product lifecycle phase seems to influence both the prevalence and acceptance of quality requirements in the scope decision process. Lastly, relying on external stakeholders and upfront analysis seems to lead to long lead-times and an insufficient quality requirements scope. QREME is a conceptual quality requirements engineering model with a lifecycle perspective. It is built upon a construct with a strategic and tactical level, a product and data dimension to include data in the scope decision process, and a forward- and a feedback-loop to enable a data-driven scope decision process. QREME is validated with five companies in a multi-case study. QREME was able to capture the companies' ways of working and provide relevant improvement recommendations. Also, the presence of the underlying constructs was confirmed. [Conclusions] Quality requirements engineering should be integrated with the overall requirements process. The awareness of quality requirements on a strategic level and catering for the product and portfolio lifecycle are important for success. I conclude that there is potential in sources such as usage data, customer service data, and continuous experimentation to complement stakeholder analysis, expert input, and focus groups. However, there is a need to better understand challenges and needs in practice, especially from a lifecycle perspective. Furthermore, longitudinal studies are needed to evaluate quality requirements solutions over time -- to understand the impact, costs, and benefits.

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  • 2.
    Olsson, Thomas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. Dept. of Computer Science, Lund University, SWE.
    Berntsson Svensson, Richard
    Dept. of Computer Science, Lund.
    Regnell, Björn
    Dept. of Computer Science, Lund.
    An Investigation of How Quality Requirements are Specified in Industrial Practice2013In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 55, no 7, p. 1224-1236Article in journal (Refereed)
    Abstract [en]

    Context: This paper analyses a sub-contractor specification in the mobile handset domain. Objective: The objective is to understand how quality requirements are specified and which types of requirements exist in a requirements specification from industry. Method: The case study is performed in the mobile handset domain, where a requirements specification was analyzed by categorizing and characterizing the pertaining requirements. Results: The requirements specification is written in structured natural language with unique identifiers for the requirements. Of the 2178 requirements, 827 (38%) are quality requirements. Of the quality requirements, 56% are quantified, i.e., having a direct metric in the requirement. The variation across the different sub-domains within the requirements specification is large. Conclusion: The findings from this study suggest that methods for quality requirements need to encompass many aspects to comprehensively support working with quality requirements. Solely focusing on, for example, quantification of quality requirements might overlook important requirements since there are many quality requirements in the studied specification where quantification is not appropriate.

  • 3.
    Olsson, Thomas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. Fraunhofer Institute for Experimental Software Engineering, DEU.
    Doerr, Joerg
    Fraunhofer Institute for Experimental Software Engineering, DEU.
    Kerkow, Daniel
    Fraunhofer Institute for Experimental Software Engineering, DEU.
    Koenig, Tom
    Fraunhofer Institute for Experimental Software Engineering, DEU.
    Suzuki, Takeshi
    Ricoh Co., Tokyo, JPN.
    Non-functional requirements in industry: Three case studies adopting an experience-based NFR method2005In: Proceedings of the IEEE International Conference on Requirements Engineering 2005, IEEE Computer Society, 2005, p. 373-382Conference paper (Refereed)
    Abstract [en]

    Non-functional characteristics of products can be essential for business success and are a key differentiator between a company and its competitors. This paper presents the application of a systematic, experience-based method to elicit, document, and analyze non-functional requirements. The objective of the method is to achieve a minimal and sufficient set of measurable and traceable non-functional requirements. The method gives clear guidance for the requirements elicitation, using workshops for capturing the important quality aspects and eliciting the non-functional requirements. This paper shows its application in three different settings, reporting the experience and lessons learned from industrial case studies that applied our NFR method. As the case studies were applied in different domains and performed with companies of various maturity, and since different quality attributes were considered, a set of interesting results has emerged. Therefore, each case study tells its own story about how the elicitation of NFR in industry can work. The paper discusses the different settings and gives a comparison of the different lessons we learned from the case studies.

  • 4.
    Olsson, Thomas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. RISE Research Institutes of Sweden, SWE.
    Hell, Martin
    Dept. of Electrical and Information Technology, Lund .
    Host, Martin
    Dept. of Computer Science, Lund.
    Franke, Ulrik
    Software and Systems Engineering Lab, RISE Research Institutes of Sweden, Kista.
    Borg, Marcus
    Software and Systems Engineering Lab, RISE Research Institutes of Sweden, Lund.
    Sharing of Vulnerability Information among Companies: A Survey of Swedish Companies2019In: 45th Euromicro Conference on Software Engineering and Advanced Applications, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 284-291, article id 8906689Conference paper (Refereed)
    Abstract [en]

    Software products are rarely developed from scratch and vulnerabilities in such products might reside in parts that are either open source software or provided by another organization. Hence, the total cybersecurity of a product often depends on cooperation, explicit or implicit, between several organizations. We study the attitudes and practices of companies in software ecosystems towards sharing vulnerability information. Furthermore, we compare these practices to contemporary cybersecurity recommendations. This is performed through a questionnaire-based qualitative survey. The questionnaire is divided into two parts: The providers' perspective and the acquirers' perspective. The results show that companies are willing to share information with each other regarding vulnerabilities. Sharing is not considered to be harmful neither to the cybersecurity nor their business, even though a majority of the respondents consider vulnerability information sensitive. However, the companies, despite being open to sharing, are less inclined to proactively sharing vulnerability information. Furthermore, the providers do not perceive that there is a large interest in vulnerability information from their customers. Hence, the companies' overall attitude to sharing vulnerability information is passive but open. In contrast, contemporary cybersecurity guidelines recommend active disclosure and sharing among actors in an ecosystem. 

  • 5.
    Olsson, Thomas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Sentilles, Séverine
    Papatheocharou, Efi
    An SLR on empirical evidence for Quality RequirementsManuscript (preprint) (Other academic)
  • 6.
    Olsson, Thomas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. RISE SICS AB, SWE.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    QREME: Quality Requirements Management Model for Supporting Decision-Making2018In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing , 2018, p. 173-188Conference paper (Refereed)
    Abstract [en]

    [Context and motivation] Quality requirements (QRs) are inherently difficult to manage as they are often subjective, context-dependent and hard to fully grasp by various stakeholders. Furthermore, there are many sources that can provide input on important QRs and suitable levels. Responding timely to customer needs and realizing them in product portfolio and product scope decisions remain the main challenge.

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  • 7.
    Olsson, Thomas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. RISE Reserach Institutes of Sweden, SWE.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Jansen, Slinger
    LUT University, FIN.
    A validated model for the scoping process of quality requirements: a multi-case study2021In: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 26, no 2Article in journal (Refereed)
    Abstract [en]

    Quality requirements are vital to developing successful software products. However, there exist evidence that quality requirements are managed mostly in an “ad hoc” manner and down-prioritized. This may result in insecure, unstable, slow products, and unhappy customers. We have developed a conceptual model for the scoping process of quality requirements – QREME – and an assessment model – Q-REPM – for companies to benchmark when evaluating and improving their quality requirements practices. Our model balances an upfront forward-loop with a data-driven feedback-loop. Furthermore, it addresses both strategic and operational decisions. We have evaluated the model in a multi-case study at two companies in Sweden and three companies in The Netherlands. We assessed the scoping process practices for quality requirements and provided improvement recommendations for which practices to improve. The study confirms the existence of the constructs underlying QREME. The companies perform, in the median, 24% of the suggested actions in Q-REPM. None of the companies work data-driven with their quality requirements, even though four out of five companies could technically do so. Furthermore, on the strategic level, quality requirements practices are not systematically performed by any of the companies. The conceptual model and assessment model capture a relevant view of the quality requirements practices and offer relevant improvement proposals. However, we believe there is a need for coupling quality requirements practices to internal and external success factors to motive companies to change their ways of working. We also see improvement potential in the area of business intelligence for QREME in selecting data sources and relevant stakeholders.

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