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  • 1.
    Chatzipetrou, Panagiota
    et al.
    Software Research Engineering Lab (SERL), Blekinge Institute of Technology, Karlskrona, Sweden.
    Alégroth, Emil
    Software Research Engineering Lab (SERL), Blekinge Institute of Technology, Karlskrona, Sweden.
    Papatheocharous, Efi
    RISE SICS AB, Lund, Sweden.
    Borg, Markus
    RISE SICS AB, Lund, Sweden.
    Gorschek, Tony
    Software Research Engineering Lab (SERL), Blekinge Institute of Technology, Karlskrona, Sweden.
    Wnuk, Krzysztof
    Software Research Engineering Lab (SERL), Blekinge Institute of Technology, Karlskrona, Sweden.
    Component selection in Software Engineering: Which attributes are the most important in the decision process?2018Ingår i: 44th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2018: Proceedings / [ed] Bures, T; Angelis, L, IEEE conference proceedings , 2018, s. 198-205Konferensbidrag (Refereegranskat)
    Abstract [en]

    Component-based software engineering is a common approach to develop and evolve contemporary software systems where different component sourcing options are available: 1)Software developed internally (in-house), 2)Software developed outsourced, 3)Commercial of the shelf software, and 4) Open Source Software.

    However, there is little available research on what attributes of a component are the most important ones when selecting new components. The object of the present study is to investigate what matters the most to industry practitioners during component selection. We conducted a cross-domain anonymous survey with industry practitioners involved in component selection. First, the practitioners selected the most important attributes from a list. Next, they prioritized their selection using the Hundred-Dollar ($100) test. We analyzed the results using Compositional Data Analysis. The descriptive results showed that Cost was clearly considered the most important attribute during the component selection. Other important attributes for the practitioners were: Support of the component, Longevity prediction, and Level of off-the-shelf fit to product. Next, an exploratory analysis was conducted based on the practitioners' inherent characteristics. Nonparametric tests and biplots were used. It seems that smaller organizations and more immature products focus on different attributes than bigger organizations and mature products which focus more on Cost.

  • 2.
    Klotins, Eriks
    et al.
    Blekinge Institute of Technology Karlskrona, Sweden.
    Unterkalmsteiner, Michael
    Blekinge Institute of Technology Karlskrona, Sweden.
    Chatzipetrou, Panagiota
    Blekinge Institute of Technology Karlskrona, Sweden.
    Gorschek, Tony
    Blekinge Institute of Technology Karlskrona, Sweden.
    Prikladnicki, Rafael
    Pontificia Universidade Catolica do Rio Grande do Sul, Porto Alegre, Brazil.
    Tripathi, Nirnaya
    University of Oulu, Oulu, Finland.
    Pompermaier, Leandro Bento
    Pontificia Universidade Catolica do Rio Grande do Sul, Porto Alegre, Brazil.
    Exploration of technical debt in start-ups2018Ingår i: Proceedings - International Conference on Software Engineering, IEEE Computer Society , 2018, s. 75-84Konferensbidrag (Refereegranskat)
    Abstract [en]

    Context: Software start-ups are young companies aiming to build and market software-intensive products fast with little resources. Aiming to accelerate time-to-market, start-ups often opt for ad-hoc engineering practices, make shortcuts in product engineering, and accumulate technical debt.

    Objective: In this paper we explore to what extent precedents, dimensions and outcomes associated with technical debt are prevalent in start-ups.

    Method: We apply a case survey method to identify aspects of technical debt and contextual information characterizing the engineering context in start-ups.

    Results: By analyzing responses from 86 start-up cases we found that start-ups accumulate most technical debt in the testing dimension, despite attempts to automate testing. Furthermore, we found that start-up team size and experience is a leading precedent for accumulating technical debt: larger teams face more challenges in keeping the debt under control.

    Conclusions: This study highlights the necessity to monitor levels of technical debt and to preemptively introduce practices to keep the debt under control. Adding more people to an already difficult to maintain product could amplify other precedents, such as resource shortages, communication issues and negatively affect decisions pertaining to the use of good engineering practices.

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