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  • 1.
    Borg, Markus
    et al.
    RISE Research Institutes of Sweden AB, Lund, Sweden.
    Chatzipetrou, Panagiota
    Örebro universitet, Handelshögskolan vid Örebro Universitet. Blekinge Institute of Technology, Karlskrona, Sweden.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Karlskrona, Sweden.
    Alégroth, Emil
    Blekinge Institute of Technology, Karlskrona, Sweden.
    Gorschek, Tony
    Blekinge Institute of Technology, Karlskrona, Sweden.
    Papatheocharous, Efi
    RISE Research Institutes of Sweden AB, Lund, Sweden.
    Shah, Syed
    iZettle, Stockholm, Sweden.
    Axelsson, Jakob
    RISE Research Institutes of Sweden AB, Lund, Sweden.
    Selecting Software Component Sourcing Options: Detailed Survey Description and Analysis2018Rapport (Annet vitenskapelig)
    Abstract [en]

    Component-based software engineering (CBSE) is a common approach to develop and evolve contemporary software systems. When evolving a system based on components, make-or-buy decisions are frequent, i.e., whether to develop components internally or to acquire them fromexternal sources. In CBSE, several different sourcing options are available: 1) developing software in-house, 2) outsourcing development, 3) buying commercial-off-the-shelf software, and 4) integrating open source software components. Unfortunately, there is little available research on howorganizations select component sourcing options (CSO) in industry practice. In this work, we seek to contribute empirical evidence to CSO selection. Method: We conduct a cross-domain survey on CSO selection in industry, implemented as an online questionnaire. Based on 188 responses, we find that most organizations consider multiple CSOs during software evolution, and that the CSO decisions in industry are dominated by expert judgment. When choosing between candidate components, functional suitability acts as an initial filter, then reliability is the most important quality. We stress that future solution-oriented work on decision support has to account for the dominance of expert judgment in industry. Moreover, we identify considerable variation in CSO decision processes in industry. Finally, we encourage software development organizations to reflect on their decision processes when choosing whether to make or buy components, and we recommend using our survey for a first benchmarking.

  • 2.
    Chatzipetrou, Panagiota
    et al.
    Department of Informatics, Aristotle University of Thessaloniki, Greece.
    Papatheocharous, Efi
    Department of Computer Science, University of Cyprus, Nicosia, Cyprus; Swedish Institute of Computer Science (SICS), Kista, Stockholm, Sweden.
    Angelis, Lefteris
    Department of Informatics, Aristotle University of Thessaloniki, Greece.
    Andreou, Andreas S
    Department of Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus.
    A multivariate statistical framework for the analysis of software effort phase distribution2015Inngår i: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 59, s. 149-169Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Context: In software project management, the distribution of resources to various project activities is one of the most challenging problems since it affects team productivity, product quality and project constraints related to budget and scheduling.

    Objective: The study aims to (a) reveal the high complexity of modelling the effort usage proportion in different phases as well as the divergence from various rules-of-thumb in related literature, and (b) present a systematic data analysis framework, able to offer better interpretations and visualisation of the effort distributed in specific phases.

    Method: The basis for the proposed multivariate statistical framework is Compositional Data Analysis, a methodology appropriate for proportions, along with other methods like the deviation from rules-ofthumb, the cluster analysis and the analysis of variance. The effort allocations to phases, as reported in around 1500 software projects of the ISBSG R11 repository, were transformed to vectors of proportions of the total effort and were analysed with respect to prime project attributes.

    Results: The proposed statistical framework was able to detect high dispersion among data, distribution inequality and various interesting correlations and trends, groupings and outliers, especially with respect to other categorical and continuous project attributes. Only a very small number of projects were found close to the rules-of-thumb from the related literature. Significant differences in the proportion of effort spent in different phrases for different types of projects were found.

    Conclusion: There is no simple model for the effort allocated to phases of software projects. The data from previous projects can provide valuable information regarding the distribution of the effort for various types of projects, through analysis with multivariate statistical methodologies. The proposed statistical framework is generic and can be easily applied in a similar sense to any dataset containing effort allocation to phases.

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