Code Commit Health (CCH): An Effective and Accessible Metric for Quantifying Development Code Contributions
DOI:
https://doi.org/10.54097/st5aea63Keywords:
Code Commit Health, Quantifying Development, Code of Value, Code Contribution AssessmentAbstract
The utilisation of Lines of Code (LOC) or Number of Commits (NOC) is an inadequate representation of the actual contributions of programmers. Furthermore, evaluation systems based on complex and difficult-to-obtain information, such as code structures, are inherently limited to small-scale applications and cannot be applied on a large scale. In order to enhance the precision of assessment while preserving simplicity, this paper puts forth a novel evaluation metric: Code Commit Health (CCH) is a new metric that combines two existing metrics: the number of lines of code and the number of commits. It is designed to provide a more comprehensive evaluation of programmers' code contributions by employing a reasonable formula. In the actual study, the validity of CCH was verified through an in-depth analysis of more than 300,000 commit records from ten renowned open-source projects and an empirical study of the commit data of 110 developers in an enterprise. The findings of the study demonstrate that CCH is an effective means of reflecting the actual contributions of programmers and has good applicability. It is our intention to provide a practical method for quantifying programmers' contributions for the open source community, enterprises and academia, and to promote the science and rationalisation of code contribution evaluation.
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[1] Viewing a project's contributors, GitHub, 2024, [Online]. Available: https://docs.github.com/en/repositories/viewing-activity-and-data-for-your-repository/viewing-a-projects-contributors. 2024.
[2] OpenHarmony main warehouse code contribution metrics rules, OpenHarmony, 2024, [Online]. Available: https://metrics. openharmony. Cn.
[3] Jinglei Ren, Hezheng Yin, Qingda Hu, Armando Fox, and Wojciech Koszek. 2018. Towards quantifying the development value of code contributions. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018). Association for Computing Machinery, New York, NY, USA, 775–779. https://doi.org/ 10. 1145/3236024.3264842.
[4] Tiobe. 2024. TIOBE Index for August 2024. https://www. tiobe.com/tiobe-index/.
[5] Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation Forest. Eighth IEEE International Conference on Data Mining, Pisa, Italy, 2008, pp. 413-422, doi: 10.1109/ ICDM. 2008. 17.
[6] GitHub. 2024. Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web. https:// github. com/vuejs/core/graphs/contributors.
[7] GitHub. 2024. Mirror of Apache Kafka. https://github. com/ apache/kafka.
[8] GitHub. 2024. The java implementation of Apache Dubbo. An RPC and microservice framework. https://github. com/ apache/ dubbo.
[9] GitHub. 2024. Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. https://github. com/ huggingface/transformers.
[10] Saaty T L .Decision making with the analytic hierarchy process. Int J Serv Sci[J].International Journal of Services Sciences, 2008, 1(1):83-98.
[11] Gitee. 2024. User Influence. https://portrait. gitee. com/ help/ articles/ 4383.
[12] Stackalytics. 2024. OpenStack community contribution in Caracal release. https://www.stackalytics.io.
[13] Bird, C., Nagappan, N., Gall, H., Murphy, B., & Devanbu, P. 2009. Does distributed development affect software quality? An empirical case study of Windows Vista.
[14] Tukey, J.W. 1962. The Future of Data Analysis. Annals of Mathematical Statistics, 33, 1-67.
[15] Ruppert, D. 2004. The elements of statistical learning: data mining, inference, and prediction.
[16] Ioffe, S., & Szegedy, C. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. JMLR.org.
[17] Han, J., & Moraga, C. 1995. The Influence of the Sigmoid Function Parameters on the Speed of Backpropagation Learning. International Work-Conference on Artificial and Natural Neural Networks.
[18] GitHub. 2024. Contribution to dubbo. https://github. com/ apache/dubbo/blob/3.2/CONTRIBUTING.md#code-conventions.
[19] Fawcett, T. 2006. An introduction to ROC analysis. Pattern Recognit. Lett., 27, 861-874.
[20] Bradley, A.P. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit., 30, 1145-1159.
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