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  • 1.
    Chatzimparmpas, Angelos
    et al.
    University of Western Macedonia, Greece.
    Bibi, Stamatia
    University of Western Macedonia, Greece.
    Maintenance process modeling and dynamic estimations based on Bayesian Networks and Association Rules2019In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 31, no 9, article id e2163Article in journal (Refereed)
    Abstract [en]

    Managing the maintenance process and estimating accurately the effort and duration required for a new release is considered to be a crucial task as it affects successful software project survival and progress over time. In this study, we propose the combination of two well-known machine learning (ML) techniques, Bayesian Networks (BNs), and Association Rules (ARs) for modeling the maintenance process by identifying the relationships among the internal and external quality metrics related to a particular project release to both the maintainability of the project and the maintenance process indicators (i.e., effort and duration). We also exploit Bayesian inference, to test the effect of certain changes in internal and external project factors to the maintainability of a project. We evaluate our approach through a case study on 957 releases of five open source JavaScript applications. The results show that the maintainability of a release, the changes observed between subsequent releases, and the time required between two releases can be accurately predicted from size, complexity, and activity metrics. The proposed combined approach achieves higher accuracy when evaluated against the BN model accuracy.

  • 2.
    Pahl, Claus
    et al.
    Free University of Bozen-Bolzano, Italy.
    Jamshidi, Pooyan
    Imperial College London, UK.
    Weyns, Danny
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Catholic University of Leuven, Belgium.
    Cloud architecture continuity: Change models and change rules for sustainable cloud software architectures2017In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 29, no 2, article id UNSP e1849Article in journal (Refereed)
    Abstract [en]

    Cloud systems provide elastic execution environments of resources that link application and infrastructure/platform components, which are both exposed to uncertainties and change. Change appears in 2 forms: the evolution of architectural components under changing requirements and the adaptation of the infrastructure running applications. Cloud architecture continuity refers to the ability of a cloud system to change its architecture and maintain the validity of the goals that determine the architecture. Goal validity implies the satisfaction of goals in adapting or evolving systems. Architecture continuity aids technical sustainability, that is, the longevity of information, systems, and infrastructure and their adequate evolution with changing conditions. In a cloud setting that requires both steady alignment with technological evolution and availability, architecture continuity directly impacts economic sustainability. We investigate change models and change rules for managing change to support cloud architecture continuity. These models and rules define transformations of architectures to maintain system goals: Evolution is about unanticipated change of structural aspects of architectures, and adaptation is about anticipated change of architecture configurations. Both are driven by quality and cost, and both represent multidimensional decision problems under uncertainty. We have applied the models and rules for adaptation and evolution in research and industry consultancy projects.

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