Large-Scale Identification and Analysis of Factors Impacting Simple Bug Resolution Times in Open Source Software Repositories
One of the most prominent issues the ever-growing open-source software community faces is the abundance of buggy code. Well-established version control systems and repository hosting services such as GitHub and Maven provide a checks-and-balances structure to minimize the amount of buggy code introduced. Although these platforms are effective in mitigating the problem, it still remains. To further the efforts toward a more effective and quicker response to bugs, we must understand the factors that affect the time it takes to fix one. We apply a custom traversal algorithm to commits made for open source repositories to determine when “simple stupid bugs” were first introduced to projects and explore the factors that drive the time it takes to fix them. Using the commit history from the main development branch, we are able to identify the commit that first introduced 13 different types of simple stupid bugs in 617 of the top Java projects on GitHub. Leveraging a statistical survival model and other non-parametric statistical tests, we found that there were two main categories of categorical variables that affect a bug’s life; Time Factors and Author Factors. We find that bugs are fixed quicker if they are introduced and resolved by the same developer. Further, we discuss how the day of the week and time of day a buggy code was written and fixed affects its resolution time. These findings will provide vital insight to help the open-source community mitigate the abundance of code and can be used in future research to aid in bug-finding programs.
Eiroa-Lledo, E.; Ali, R.H.; Pinto, G.; Anderson, J.; Linstead, E. Large-Scale Identification and Analysis of Factors Impacting Simple Bug Resolution Times in Open Source Software Repositories. Appl. Sci. 2023, 13, 3150. https://doi.org/10.3390/app13053150
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
This article was originally published in Applied Sciences, volume 13, in 2023. https://doi.org/10.3390/app13053150
This study used the ManySStuBs4J Dataset, which is available at https://zenodo.org/record/3653444 (accessed on 8 February 2020).