Choose a Job You Love: Predicting Choices of GitHub Developers

Nielek, Radosław; Jarczyk, Oskar; Pawlak, Kamil; Bukowski, Leszek; Bartusiak, Roman; Wierzbicki, Adam

doi:10.1109/wi.2016.0037

Cited by 15 publications

(15 citation statements)

References 31 publications

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“…In a study predicting whether a developer would join a project in the future, the dataset was split into two different sets by time. In this way, the predicted result was verified with actual future data [25].…”

supporting

confidence: 56%

“…[5] 1700 22,000 0.07 [26] 1255 58,092 0.02 [27] 1070 1600 0.66 [25] 62,607 9447 0.15 [30] 62,607 9447 0.15 Our study 100 41,280 0.002…”

Section: Paper Id Number Of User Number Of Project Ratio (~)mentioning

confidence: 76%

“…In a study aiming to predict whether a user would join a project in the future, the metrics used included a developer's GitHub age (i.e., when their account was opened), the number of projects that they had joined, the programming languages of their commits, how many times a project was starred, the number of developers that joined a project, and the number of commits made to a project [25].…”

Section: Introductionmentioning

confidence: 99%

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New Developer Metrics for Open Source Software Development Challenges: An Empirical Study of Project Recommendation Systems

2021

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Software collaboration platforms where millions of developers from diverse locations can contribute to the common open source projects have recently become popular. On these platforms, various information is obtained from developer activities that can then be used as developer metrics to solve a variety of challenges. In this study, we proposed new developer metrics extracted from the issue, commit, and pull request activities of developers on GitHub. We created developer metrics from the individual activities and combined certain activities according to some common traits. To evaluate these metrics, we created an item-based project recommendation system. In order to validate this system, we calculated the similarity score using two methods and assessed top-n hit scores using two different approaches. The results for all scores with these methods indicated that the most successful metrics were binary_issue_related, issue_commented, binary_pr_related, and issue_opened. To verify our results, we compared our metrics with another metric generated from a very similar study and found that most of our metrics gave better scores that metric. In conclusion, the issue feature is more crucial for GitHub compared with other features. Moreover, commenting activity in projects can be equally as valuable as code contributions. The most of binary metrics that were generated, regardless of the number of activities, also showed remarkable results. In this context, we presented improvable and noteworthy developer metrics that can be used for a wide range of open-source software development challenges, such as user characterization, project recommendation, and code review assignment.

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supporting

confidence: 56%

“…[5] 1700 22,000 0.07 [26] 1255 58,092 0.02 [27] 1070 1600 0.66 [25] 62,607 9447 0.15 [30] 62,607 9447 0.15 Our study 100 41,280 0.002…”

Section: Paper Id Number Of User Number Of Project Ratio (~)mentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

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New Developer Metrics for Open Source Software Development Challenges: An Empirical Study of Project Recommendation Systems

2021

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“…In the past, scholars indicated that the higher the number of project's participants is, the higher probability of being collaboratively developed is [2]. Therefore, it is obvious that it is difficult to correctly predict the number of participants in the software development collaboration projects only based on those factors of software and project dimensions before the projects are put into the platform.…”

Section: Introductionmentioning

confidence: 99%

The Dynamic Prediction Model of Number of Participants in Software Crowd Sourcing Collaboration Development Project

Zheng¹,

Huang²,

Peng³

2018

JCC

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Many online platforms providing crowd with opportunities to participate in software development projects have been existed for a while. Meanwhile, many enterprises are using crowd source to collaboratively develop their software via these platforms in recent years. However, some software development projects in these platforms hardly attract users to join. Therefore, these project owners need a way to effectively predict the number of participants in their projects and accordingly well plan their software and project specifications, such as the program language and the size of the documentation, in order to attract more individuals to participant in the projects. Compared with the past prediction models, our proposed model can dynamically add the factors, such as number of participants in the initial stage of the project, within the project life cycle and make the adjustment to the prediction model. The proposed model was also verified by using cross validation method. The results show that: 1) The models with the factor "the number of user participation" is more accurate than the model without it. 2) The factors of crowd dimension are more influential on the prediction accuracy than those of software project and owner dimensions. It is suggested that the project owners not only just consider those factors of the software project dimension in the initial stage of the project life cycle but also those factors of crowd and interaction dimensions in the late stage to attract more participants in their projects.

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“…Therefore, recommender systems are commonly introduced and widely-used in GitHub. For example, in the past decade, many studies [12][13][14][15][16][17] proposed recommender systems for GitHub developers with different categories and purposes such as project recommendation, expert recommendation, PR reviewer recommendation, etc. We found that most research only focuses on historical activity and repository-textual data when it comes to recommending projects or experts.…”

Section: Motivationmentioning

confidence: 99%

Recommending GitHub Projects by Leveraging Developers' Social Networks and Genetic Algorithm

Wang¹

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In the uncommon and challenging year 2020, face-to-face collaboration is difficult.In particular, in the domain of software, online collaboration becomes more demanding than ever. As the leading platform of open-source software, GitHub, without a doubt, plays a vital role in hosting over 190 million of projects and fostering collaboration among developers. Developers in GitHub continuously seek new opportunities to contribute to new projects. So far, several researchers have used users' historical activities, textual descriptions of projects or starred items to analyze or infer the interests or programming expertise of developers to offer possible project recommendations. While some research utilized social connections to obtain developers' social importance, none of them have implemented such aspects for generating project recommendations. In this research, we use the latest GHTorrent dataset to construct and propose a GitHub project recommendation system by leveraging Parallel Genetic Algorithm and developers' social networks. To the best of our knowledge, this is the first application of Genetic Algorithm in the GitHub project recommendation area. iii I, Lance PoKai Wang, would like to express my sincere gratitude to my fantastic supervisor, Professor Olga Baysal, for her continuous guidance, advice, and friendly discussions throughout my thesis. Her precise insights and constant effort provided me valuable feedback and support, which made this work successful. I also thank Professor Frank Dehne for his advice and guidance on constructing parallel frameworks and also suggesting to work weights-distributed structure. I am also grateful to Adam Spriggs. He has contributed to this work by implementing the baseline RepoPal, and his work has speed up the model evaluation.I also thank my laboratory members, especially Norbert Eke and Khadija Osman, for providing insightful feedback for data collection and preprocessing. I also wish to express my deepest appreciation to all the thesis committee members for their valuable time, insightful comments and encouragement.Last but not least, I would like to thank my mother, LiChin, my brother, PoJun for their endless love and support in my entire life. v v Table of Contents vi List of Tables x List of Figures xi Nomenclature xiii List of Tables

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Choose a Job You Love: Predicting Choices of GitHub Developers

Cited by 15 publications

References 31 publications

New Developer Metrics for Open Source Software Development Challenges: An Empirical Study of Project Recommendation Systems

New Developer Metrics for Open Source Software Development Challenges: An Empirical Study of Project Recommendation Systems

The Dynamic Prediction Model of Number of Participants in Software Crowd Sourcing Collaboration Development Project

Recommending GitHub Projects by Leveraging Developers' Social Networks and Genetic Algorithm

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