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  • 1.
    Borg, Markus
    RISE, Swedish ICT, SICS, Security Lab. Lund University, Sweden.
    TuneR: A Framework for Tuning Software Engineering Tools with Hands-on Instructions in R2016In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 28, no 6, p. 427-459Article in journal (Refereed)
    Abstract [en]

    Numerous tools automating various aspects of software engineering have been developed, and many of the tools are highly configurable through parameters. Understanding the parameters of advanced tools often requires deep understanding of complex algorithms. Unfortunately, suboptimal parameter settings limit the performance of tools and hinder industrial adaptation, but still few studies address the challenge of tuning software engineering tools. We present TuneR, an experiment framework that supports finding feasible parameter settings using empirical methods. The framework is accompanied by practical guidelines of how to use R to analyze the experimental outcome. As a proof-of-concept, we apply TuneR to tune ImpRec, a recommendation system for change impact analysis in a software system that has evolved for more than two decades. Compared with the output from the default setting, we report a 20.9% improvement in the response variable reflecting recommendation accuracy. Moreover, TuneR reveals insights into the interaction among parameters, as well as nonlinear effects. TuneR is easy to use, thus the framework has potential to support tuning of software engineering tools in both academia and industry.

  • 2.
    Papatheocharous, Efi
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS. University of Cyprus, Cyprus.
    Bibi, Stamatia
    University of Western Macedonia, Greece.
    Stamelos, Ioannis
    Aristotle University, Greece.
    Andreou, Andreas S
    Cyprus University of Technology, Cyprus.
    An investigation of effort distribution among development phases: A four-stage progressive software cost estimation model2017In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 29, no 10, article id e1881Article in journal (Refereed)
    Abstract [en]

    Software cost estimation is a key process in project management. Estimations in the initial project phases are made with a lot of uncertainty that influences estimation accuracy which typically increases as the project progresses in time. Project data collected during the various project phases can be used in a progressive time-dependent fashion to train software cost estimation models. Our motivation is to reduce uncertainty and increase confidence based on the understanding of patterns of effort distributions in development phases of real-world projects. In this work, we study effort distributions and suggest a four-stage progressive software cost estimation model, adjusting the initial effort estimates during the development life-cycle based on newly available data. Initial estimates are reviewed on the basis of the experience gained as development progresses and as new information becomes available. The proposed model provides an early, a post-planning, a post-specifications, and a post-design estimate, while it uses industrial data from the ISBSG (R10) dataset. The results reveal emerging patterns of effort distributions and indicate that the model provides effective estimations and exhibits high explanatory value. Contributions in lessons learned and practical implications are also provided.

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