Developer Prioritization in Bug Repositories (1704.04764v1)

Abstract: Developers build all the software artifacts in development. Existing work has studied the social behavior in software repositories. In one of the most important software repositories, a bug repository, developers create and update bug reports to support software development and maintenance. However, no prior work has considered the priorities of developers in bug repositories. In this paper, we address the problem of the developer prioritization, which aims to rank the contributions of developers. We mainly explore two aspects, namely modeling the developer prioritization in a bug repository and assisting predictive tasks with our model. First, we model how to assign the priorities of developers based on a social network technique. Three problems are investigated, including the developer rankings in products, the evolution over time, and the tolerance of noisy comments. Second, we consider leveraging the developer prioritization to improve three predicted tasks in bug repositories, i.e., bug triage, severity identification, and reopened bug prediction. We empirically investigate the performance of our model and its applications in bug repositories of Eclipse and Mozilla. The results indicate that the developer prioritization can provide the knowledge of developer priorities to assist software tasks, especially the task of bug triage.

• The paper introduces a socio-technical model using social network analysis to rank developers based on their contributions within bug repositories.
• Applying the model to open-source projects showed that developer prioritization improved bug triage accuracy by up to 13% and is largely robust against noise from comments.
• The model's practical implication is streamlining bug tracking by aligning tasks with suitable developers, with future work suggested on task-specific prioritization for further enhancement.

Developer Prioritization in Bug Repositories: A Socio-Technical Approach

The paper conducted by Jifeng Xuan, He Jiang, Zhilei Ren, and Weiqin Zou at the Dalian University of Technology presents an innovative socio-technical model to address developer prioritization in bug repositories. This research diverges from traditional treatments of developers as equal entities, offering a method that ranks developers based on their contributions to bug tracking systems. The salient objectives of this paper are twofold: first, to model developer prioritization using social network techniques, and second, to exploit this model to enhance predictive tasks associated with bug repositories.

Methodology and Approach

Central to this paper is the utilization of social network analysis to model and rank developers in bug repositories. The researchers introduce a framework that assigns probability-based priorities to developers involved in bug reporting and fixing. This approach significantly focuses on three main aspects: determining developer rankings across products, analyzing evolutionary trends over time, and assessing the robustness against noise from superfluous comments.

The authors apply their model to the bug repositories of open-source projects such as Eclipse and Mozilla. The results demonstrate that developer prioritization plays a pivotal role in improving tasks like bug triage, severity identification, and predicting reopened bugs. Specifically, leveraging the model increased the accuracy of bug triage by up to 13%, a substantial improvement that underscores the potential of integrating social factors into software maintenance processes.

Empirical Findings and Results

This research involves extensive empirical testing on a data set comprising over 900,000 bug reports. The developer prioritization model effectively distinguishes between developers with varying levels of activity and impact within a project's lifecycle. The paper reveals that developer rankings differ substantially between the entire project and individual products, highlighting the dynamic roles developers play across various segments of a software project.

Further empirical analysis of the model’s robustness shows that it is largely insensitive to noise, particularly redundant or low-effort comments. This property enhances the reliability of the prioritization in managing real-world data, which often contains noisy artifacts.

Implications and Future Directions

The paper's implications are multifaceted, impacting both practical applications and theoretical advancements in AI and software engineering. Practically, the prioritization model can streamline processes in bug tracking systems, reducing time and resource expenditure by directing attention to more critical bug reports and aligning bug triage with the most suitable team members. Theoretically, the integration of socio-technical analytics within software engineering tasks presents an evolutionary step in understanding collaborative dynamics within development environments.

Looking forward, the researchers suggest investigating task-specific prioritizations. Tailoring the socio-technical model to suit different software maintenance tasks, such as focusing more on fixer priorities during bug triage, might yield even greater enhancements. This direction could refine prioritization further, making it more adaptable and precise within varied operational contexts.

Conclusion

This paper offers a compelling model for harnessing social dynamics within developer ecosystems, quantifying contributions, and adapting these insights to optimize bug management tasks. While further validation and refinement, especially through task-specific implementations, are proposed, the current findings advocate for broader adoption of socio-technical approaches in handling the complexities inherent in large-scale software projects. By ranking developer contributions, this research provides a strategic tool for enhancing efficiency and decision-making processes in software development and maintenance.