Browsing by Subject "Intelligent collaboration tools"

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    BFSig: leveraging file significance in bus factor estimation
    (Association for Computing Machinery, Inc., 2023) Haratian, Vahid; Evtikhiev, M.; Derakhshanfar, P.; Tüzün, Eray; Kovalenko, V.
    Software projects experience the departure of developers due to various reasons. As developers are one of the main sources of knowl edge in software projects, their absence will inevitably result in a certain degree of knowledge depletion. Bus Factor (BF) is a met ric to evaluate how this knowledge loss can affect the project’s continuity
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    Bus factor explorer
    (IEEE, 2023-11-08) Klimov, E.; Ahmed, Muhammad Umair; Sviridov, N.; Derakhshanfar, P.; Tüzün, Eray; Kovalenko, V.
    Bus factor (BF) is a metric that tracks knowledge distribution in a project. It is the minimal number of engineers that have to leave for a project to stall. Despite the fact that there are several algorithms for calculating the bus factor, only a few tools allow easy calculation of bus factor and convenient analysis of results for projects hosted on Git-based providers. We introduce Bus Factor Explorer, a web application that provides an interface and an API to compute, export, and explore the Bus Factor metric via treemap visualization, simulation mode, and chart editor. It supports repositories hosted on GitHub and enables functionality to search repositories in the interface and process many repositories at the same time. Our tool allows users to identify the files and subsystems at risk of stalling in the event of developer turnover by analyzing the VCS history. The application and its source code are publicly available on GitHub at https://github.com/JetBrains-Research/bus-factor-explorer. The demonstration video can be found on YouTube: https://youtu.be/uIoV79N14z8
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    Leveraging file significance in bus factor estimation
    (2025-01) Haratian, Vahid
    Software projects often face developer turnover for various reasons. Since develop-ers are key sources of knowledge in these projects, their absence inevitably leads to some degree of knowledge loss. The Bus Factor (BF) is a metric used to assess the impact of this knowledge loss on a project’s continuity. Traditionally, BF is defined as the smallest group of developers whose departure would result in a loss of more than half of the project’s knowledge. Current state-of-the-art methods calculate developers’ knowledge based on the number of files they have authored, using data from version control systems (VCS). However, numerous studies have highlighted that not all files in software projects hold the same level of significance. In this study, we investigate the impact of weighting files based on their significance on the performance of two widely used BF estimators. Significance scores are calculated using five established graph metrics derived from the project’s De-pendency Graph: PageRank, In-/Out-/All-Degree, and Betweenness Centralities. Additionally, we introduce BFSig, a prototype implementing our approach. Lastly, we present a new dataset featuring BF scores reported by software practitioners from five prominent GitHub repositories. Our findings show that BFSig surpasses the baseline methods, achieving up to an 18% reduction in Normalized Mean Absolute Error (NMAE). Additionally, BFSig reduces False Negatives by 18%when identifying potential risks linked to low BF. Furthermore, our respondents validated BFSig’s versatility, highlighting its capability to evaluate the BF of individual project subfolders. In conclusion, we believe that when estimating BF from authorship, software components of greater significance should be given higher weight.