Evolution of Influential Developer’s Communities in OSS and its Impact on Quality

Khan, Beenish; Mufti, Muhammad Rafiq; Habib, Asad; Afzal, Humaira; Zia, M.A.; Almas, Afshan; Hussain, Shahid; Ahmad, Bashir

doi:10.32604/iasc.2021.015034

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…However, there exist two classes of developers in large-scale OSS, including blockchain project; an external developer who voluntarily contribute patch code only but do not have direct commit right to the main control unit of the project, while a committer not only contributes but also have commit right and can directly edit and push code to the project. As different developer plays key functional roles in OSS projects [8,10,24], they must interact and evolve over time in the project [25][26][27]. These blockchain projects experience role migration phenomena [1,28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Information systems (IS) and related software engineering (SE) developer turnover literature mainly concentrated on creating behavioural theories and backing their evidence with empirical investigation [11,24,[26][27][28]. Moreover, an overwhelming majority of studies have concentrated on enhancing the efficiency of ML algorithms via archive record datasets and a particular ML algorithm [18,42,43].…”

Section: Comparative Analysismentioning

confidence: 99%

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Comparative Performance of Machine Learning Algorithms for Predicting Future Committer in Blockchain Projects

Alawiyah Abd Wahab,

Huda H. Ibrahim,

Shehu M. Sarkintudu

et al. 2023

ARASET

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The success of blockchain projects depends, to a large extent, on the developer's involvement. Source code-commit privilege to the project is typically restricted to a few developers known as committers. Project leaders continuously search for new committers to evolve into a high-quality blockchain. However, promoting developers into committers is risky, mainly when the promoted committers exhibit low involvement behaviour. Hence, the committer assessment process is critical to the successful evolution of blockchain projects. The phenomenon of developer induction as code committers has previously been explored in the literature. However, previous studies employed objective measures such as the number of bugs report and code patches to appraise the developers activities for promotion purposes. Although these approaches are significant, behavioural tendencies influencing developers' activities are ignored. There have, however, been comparatively few investigations on a subjective measure that evaluate developers' perception of their activities for promotion purposes. This study aims to appraise blockchain developers' perceptions for predicting future committers in blockchain projects. In this study, 173 blockchain developers’ perceptions were gathered. The study employed hybrid analyses such as Structural Equation Modelling (SEM) to first analyse the survey data and Machine Learning (ML) algorithms to predict future committers. The performance of various ML algorithms was evaluated compared to classification performance indicators such as the F1 score, accuracy, precision, and AUC. In addition, the study also investigates the most important factors that predict future committers. The results indicate that XGBoost algorithm achieves the best performance with an accuracy of 0.94. Moreover, the most important factors in predicting future committers in a blockchain project are project desertion, developer involvement, decision-right delegation and system integration.

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