Yazar "Succi, Giancarlo" 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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    How social interactions can affect Modern Code Review
    (Frontiers Media Sa, 2023) Ciancarini, Paolo; Kruglov, Artem; Malikova, Aygul; Pedrycz, Witold; Succi, Giancarlo
    IntroductionModern Code Review (MCR) is a multistage process where developers evaluate source code written by others to enhance the software quality. Despite the numerous studies conducted on the effects of MCR on software quality, the non-technical issues in the MCR process have not been extensively studied. This study aims to investigate the social problems in the MCR process and to find possible ways to prevent them and improve the overall quality of the MCR process. MethodologyTo achieve the research objectives, we applied the grounded theory research shaped by GQM approach to collect data on the attitudes of developers from different teams toward MCR. We conducted interviews with 25 software developers from 13 companies to obtain the information necessary to investigate how social interactions affect the code reviewing process. ResultsOur findings show that interpersonal relationships within the team can have significant consequences on the MCR process. We also received a list of possible strategies to overcome these problems. DiscussionOur study provides a new perspective on the non-technical issues in the MCR process, which has not been extensively studied before. The findings of this study can help software development teams to address the social problems in the MCR process and improve the overall quality of their software products. ConclusionThis study provides valuable insights into the non-technical issues in the MCR process and the possible ways to prevent them. The findings of this study can help software development teams to improve the MCR process and the quality of their software products. Future research could explore the effectiveness of the identified strategies in addressing the social problems in the MCR process.
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    Linguistic Models: Optimization With the Use of Conditional Fuzzy C-Means
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Jing, TaiLong; Pedrycz, Witold; Zhu, XiuBin; Succi, Giancarlo; Li, ZhiWu
    Most fuzzy models are just numeric. In this study, we revisit, explore and augment a concept of linguistic models, viz., fuzzy models producing results that are information granules, and, specifically, intervals or fuzzy sets. The proposed architecture is formed by constructing a network of linked fuzzy sets (information granules) ininput and output spaces with the aid of a context-based Fuzzy C-Means clustering method. The user centricity of such clustering method is implied by the explicit formulation of fuzzy sets in the output space. The resulting information granules constructed in the input space are conditioned by the corresponding fuzzy sets in the output space. This arrangement can increase the interpretability of the model and represent the model as a collection of logically arranged associations among information granules. The model's overall design process is discussed along with a detailed algorithmic structure. Its experimental evaluations are provided by using both synthetic and publicly datasets. For the former, the model brings the performance improvement ranging from 91% to 250% over the models with information granules uniformly distributed in output space. For the latter, such improvement ranges from 6% to 94%. Finally, a thorough discussion is provided together with guidelines on how to develop such a linguistic model in different contexts.
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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.