Extending Developer Experience Metrics for Better Effort-Aware Just-In-Time Defect Prediction

Developers use defect prediction models to efficiently allocate limited resources for quality assurance and appropriately make a plan for software quality improvement activities. Traditionally, defect predictions are conducted at the module level, such as the class or file level. However, a more recent trend is to perform defect prediction for a single or consecutive commits to the repository, which is known as just-in-time (JIT) defect prediction. JIT defect prediction finds error-prone changes instead of error-prone modules, and as a result, the developer only needs to investigate error-prone changed lines instead of the entire module. When building JIT defect prediction models, researchers used various metrics, including developer experience metrics which measure the developer's experiences. Despite the fact that software defectiveness is likely to be affected by the experience of developers, developer metrics were understudied in the literature. In this work, we investigate the impact of various novel developer experience metrics and their combinations on JIT defect prediction. Our experimental results are positive. We found that it is possible to improve the cost-effectiveness of defect prediction models consistently and statistically significantly by using our new developer experience metrics.