Effect of Technical and Social Factors on Pull Request Quality for the NPM Ecosystem
暂无分享,去创建一个
[1] Chanchal Kumar Roy,et al. An insight into the pull requests of GitHub , 2014, MSR 2014.
[2] Daniel M. Germán,et al. Peer Review on Open-Source Software Projects: Parameters, Statistical Models, and Theory , 2014, TSEM.
[3] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[4] J. Deeks. When can odds ratios mislead? , 1998 .
[5] J. Herbsleb,et al. Two case studies of open source software development: Apache and Mozilla , 2002, TSEM.
[6] Chetan Bansal,et al. Predicting pull request completion time: a case study on large scale cloud services , 2019, ESEC/SIGSOFT FSE.
[7] Marco Aurélio Gerosa,et al. Almost There: A Study on Quasi-Contributors in Open-Source Software Projects , 2018, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).
[8] Georgios Gousios,et al. Work Practices and Challenges in Pull-Based Development: The Integrator's Perspective , 2014, ICSE.
[9] Audris Mockus,et al. Effectiveness of code contribution: from patch-based to pull-request-based tools , 2016, SIGSOFT FSE.
[10] Gang Yin,et al. Reviewer recommendation for pull-requests in GitHub: What can we learn from code review and bug assignment? , 2016, Inf. Softw. Technol..
[11] Hadley Wickham,et al. ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .
[12] Douglas G Altman,et al. Odds ratios should be avoided when events are common , 1998, BMJ.
[13] James D. Herbsleb,et al. Social coding in GitHub: transparency and collaboration in an open software repository , 2012, CSCW.
[14] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[15] Daniel M. Germán,et al. Will my patch make it? And how fast? Case study on the Linux kernel , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).
[16] James D. Herbsleb,et al. Influence of social and technical factors for evaluating contribution in GitHub , 2014, ICSE.
[17] Plotting regression surfaces with plotmo , 2019 .
[18] Audris Mockus,et al. World of Code: An Infrastructure for Mining the Universe of Open Source VCS Data , 2019, 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR).
[19] Audris Mockus,et al. Are Software Dependency Supply Chain Metrics Useful in Predicting Change of Popularity of NPM Packages? , 2018, PROMISE.
[20] Premkumar T. Devanbu,et al. Wait for It: Determinants of Pull Request Evaluation Latency on GitHub , 2015, 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories.
[21] Jia-Huan He,et al. Who should comment on this pull request? Analyzing attributes for more accurate commenter recommendation in pull-based development , 2017, Inf. Softw. Technol..
[22] Georgios Gousios,et al. Work practices and challenges in pull-based development: the contributor's perspective , 2015, ICSE.
[23] Leonardo Gresta Paulino Murta,et al. Acceptance factors of pull requests in open-source projects , 2015, SAC.
[24] Minghui Zhou,et al. Be careful of when: an empirical study on time-related misuse of issue tracking data , 2018, ESEC/SIGSOFT FSE.
[25] Stephan Diehl,et al. Small patches get in! , 2008, MSR '08.
[26] Audris Mockus,et al. Representation of Developer Expertise in Open Source Software , 2020, 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE).
[27] Audris Mockus,et al. Patterns of Effort Contribution and Demand and User Classification based on Participation Patterns in NPM Ecosystem , 2019, PROMISE.
[28] Audris Mockus,et al. A Methodology for Measuring FLOSS Ecosystems , 2019, Towards Engineering Free/Libre Open Source Software (FLOSS) Ecosystems for Impact and Sustainability.
[29] Georgios Gousios,et al. Automatically Prioritizing Pull Requests , 2015, 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories.
[30] Audris Mockus,et al. Deriving a usage-independent software quality metric , 2020, Empirical Software Engineering.
[31] Eleni Constantinou,et al. On the Impact of Security Vulnerabilities in the npm Package Dependency Network , 2018, 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR).
[32] Audris Mockus,et al. Impact of Triage: A Study of Mozilla and Gnome , 2013, 2013 ACM / IEEE International Symposium on Empirical Software Engineering and Measurement.
[33] Takashi Ishio,et al. Towards Smoother Library Migrations: A Look at Vulnerable Dependency Migrations at Function Level for npm JavaScript Packages , 2018, 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME).
[34] Audris Mockus,et al. Detecting and Characterizing Bots that Commit Code , 2020, 2020 IEEE/ACM 17th International Conference on Mining Software Repositories (MSR).
[35] Gang Yin,et al. Who Should Review this Pull-Request: Reviewer Recommendation to Expedite Crowd Collaboration , 2014, 2014 21st Asia-Pacific Software Engineering Conference.
[36] Audris Mockus,et al. An Exploratory Study of Bot Commits , 2020, ICSE.
[37] Audris Mockus,et al. Modeling Relationship between Post-Release Faults and Usage in Mobile Software , 2018, PROMISE.
[38] Gang Yin,et al. Reviewer Recommender of Pull-Requests in GitHub , 2014, 2014 IEEE International Conference on Software Maintenance and Evolution.
[39] Michael W. Godfrey,et al. The Secret Life of Patches: A Firefox Case Study , 2012, 2012 19th Working Conference on Reverse Engineering.
[40] Audris Mockus,et al. A Dataset and an Approach for Identity Resolution of 38 Million Author IDs extracted from 2B Git Commits , 2020, MSR.