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  • 1.
    Bjarnason, Elizabeth
    et al.
    Lund University, Sweden.
    Borg, Markus
    RISE., Swedish ICT, SICS, Security Lab.
    Aligning Requirements and Testing - Working Together Toward the Same Goal2017Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 34, nr 1, s. 20-23, artikel-id 7819382Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    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.

  • 2.
    Bjarnason, Elizabeth
    et al.
    Lund University, Sweden.
    Borg, Markus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Aligning Requirements and Testing: Working Together toward the Same Goal2017Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 34, nr 1, s. 20-23, artikel-id 7819382Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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. © 2017 IEEE.

  • 3.
    Mellegård, Niklas
    et al.
    RISE - Research Institutes of Sweden, ICT, Viktoria.
    Burden, Håkan
    RISE - Research Institutes of Sweden, ICT, Viktoria.
    Levin, Daniel
    Volvo Car Corporation, Sweden.
    Lind, Kenneth
    HiMinds Göteborg AB, Sweden.
    Magazinius, Ana
    RISE - Research Institutes of Sweden, ICT, Viktoria.
    Contrasting Big Bang with Continuous Integration Through Defect Reports2018Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Continuous integration promises earlier defect detection, quality improvements and more customer value delivered faster. But what evidence is there? In this longitudinal case study we examined the development of software for the advanced safety and driver support component of a Swedish vehicle manufacturer in two consecutive projects, where the first was developed in a big bang fashion, typical to a traditional waterfall process, while the second project utilized continuous integration. By contrasting the two projects, we evaluated the introduction of continuous integration and supplement earlier claims based on interview studies with a quantitative analysis of defect reports.

  • 4.
    Trubiani, Catia
    et al.
    Gran Sasso Science Institute, Italy.
    Jamshidi, Pooyan
    University of South Carolina, USA.
    Cito, Jurgen
    Massachusetts Institute Technology, USA.
    Shang, Weiyi
    Concordia University, Canada.
    Jiang, Zhen Ming
    York University, Canada.
    Borg, Markus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Performance Issues?: Hey DevOps, Mind the Uncertainty!2019Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 36, nr 2, s. 110-117, artikel-id 8501933Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    DevOps is a novel trend that aims to bridge the gap between software development and operation teams. When applied to the performance evaluation process, it brings new challenges since developers need to be aware of the deployment settings and application runtime characteristics. At the operational stage, several uncertainties, e.g., workload fluctuations and resource availability, may affect the performance analysis. The goal of this paper is to identify the uncertain parameters and quantify their propagation to performance analysis results, in order to bring upfront the main system criticisms. To this end, we make use of a popular big data system showing that the sources of uncertainty may span on different characteristics and the performance analysis results can be heavily affected by these uncertainties. The paper contributes as an experience report aiming to better identify performance uncertainties through a case study. It provides a step-by-step guide to practitioners for controlling system uncertainties.

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