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  • 1.
    Garousi, Vahid
    et al.
    Queen’s University Belfast, UK.
    Borg, Markus
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Oivo, Markku
    University of Oulu, Finland.
    Practical relevance of software engineering research: synthesizing the community’s voice2020In: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 25, no 3, p. 1687-1754Article in journal (Refereed)
    Abstract [en]

    Software engineering (SE) research should be relevant to industrial practice. There have been regular discussions in the SE community on this issue since the 1980’s, led by pioneers such as Robert Glass. As we recently passed the milestone of “50 years of software engineering”, some recent positive efforts have been made in this direction, e.g., establishing “industrial” tracks in several SE conferences. However, many researchers and practitioners believe that we, as a community, are still struggling with research relevance and utility. The goal of this paper is to synthesize the evidence and experience-based opinions shared on this topic so far in the SE community, and to encourage the community to further reflect and act on the research relevance. For this purpose, we have conducted a Multi-vocal Literature Review (MLR) of 54 systematically-selected sources (papers and non peer-reviewed articles). Instead of relying on and considering the individual opinions on research relevance, mentioned in each of the sources, the MLR aims to synthesize and provide the “holistic” view on the topic. The highlights of our MLR findings are as follows. The top three root causes of low relevance, discussed in the community, are: (1) Researchers having simplistic views (or wrong assumptions) about SE in practice; (2) Lack of connection with industry; and (3) Wrong identification of research problems. The top three suggestions for improving research relevance are: (1) Using appropriate research approaches such as action-research; (2) Choosing relevant (practical) research problems; and (3) Collaborating with industry. By synthesizing all the discussions on this important topic so far, this paper aims to encourage further discussions and actions in the community to increase our collective efforts to improve the research relevance. Furthermore, we raise the need for empirically-grounded and rigorous studies on the relevance problem in SE research, as carried out in other fields such as management science.

  • 2.
    Jonsson, Leif
    et al.
    Linköping University, Sweden; Ericsson AB, Sweden.
    Borg, Markus
    RISE, Swedish ICT, SICS, Security Lab. Lund University, Sweden.
    Broman, David
    KTH Royal Institute of Technology, Sweden; UC Berkeley, USA.
    Sandahl, Kristian
    Linköping University, Sweden.
    Eldh, Sigrid
    Ericsson AB, Sweden.
    Runeson, Per
    Lund University, Sweden.
    Automated Bug Assignment: Ensemble-based Machine Learning in Large Scale Industrial Contexts2016In: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 21, no 4, p. 1533-1578Article in journal (Refereed)
    Abstract [en]

    Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects. Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classification. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classifiers. We collect more than 50,000 bug reports from five development projects from two companies in different domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. We show that SG scales to large scale industrial application and that it outperforms the use of individual classifiers for bug assignment, reaching prediction accuracies from 50 % to 89 % when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment.

    Download full text (pdf)
    FULLTEXT01
  • 3.
    Olsson, Thomas
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Sweden.
    Jansen, Slinger
    LUT University, Finland.
    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.

    Download full text (pdf)
    fulltext
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