Yazar "Bugayenko, Yegor" seçeneğine göre listele

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    Automatically Prioritizing Tasks in Software Development
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Bugayenko, Yegor; Farina, Mirko; Kruglov, Artem; Pedrycz, Witold; Plaksin, Yaroslav; Succi, Giancarlo
    Within the domain of managing software development teams, effective task prioritization is a critical responsibility that should not be underestimated, particularly for larger organizations with significant backlogs. Current approaches primarily rely on predicting task priority without considering information about other tasks, potentially resulting in inaccurate priority predictions. This paper presents the benefits of considering the entire backlog when prioritizing tasks. We employ an iterative approach using Particle Swarm Optimization to optimize a linear model with various preprocessing methods to determine the optimal model for task prioritization within a backlog. The findings of our study demonstrate the usefulness of constructing a task prioritization model based on complete information from the backlog. The method proposed in our study can serve as a valuable resource for future researchers and can also facilitate the development of new tools to aid IT management teams.
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    Qualitative Clustering of Software Repositories Based on Software Metrics
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Bugayenko, Yegor; Daniakin, Kirill; Farina, Mirko; Kholmatova, Zamira; Kruglov, Artem; Pedrycz, Witold; Succi, Giancarlo
    Software repositories contain a wealth of information about the aspects related to software development process. For this reason, many studies analyze software repositories using methods of data analytics with a focus on clustering. Software repository clustering has been applied in studying software ecosystems such as GitHub, defect and technical debt prediction, software remodularization. Although some interesting insights have been reported, the considered studies exhibited some limitations. The limitations are associated with the use of individual clustering methods and manifesting in the shortcomings of the obtained results. In this study, to alleviate the existing limitations we engage multiple cluster validity indices applied to multiple clustering methods and carry out consensus clustering. To our knowledge, this study is the first to apply the consensus clustering approach to analyze software repositories and one of the few to apply the consensus clustering to software metrics. Intensive experimental studies are reported for software repository metrics data consisting of a number of software repositories each described by software metrics. We revealed seven clusters of software repositories and relate them to developers' activity. It is advocated that the proposed clustering environment could be useful for facilitating the decision making process for business investors and open-source community with the help of the Gartner's hype cycle.