The Need for a Fine-grained approach in Just-in-Time Defect Prediction
This work addresses the challenge of reducing inspection effort for software developers in defect prediction, but it appears incremental as it focuses on dataset creation and feature analysis without major methodological breakthroughs.
The paper tackles the problem of high inspection effort in just-in-time defect prediction by investigating gaps in features and presenting a new dataset linking features to actual commits.
With software system complexity leading to the rise of software defects, research efforts have been done on techniques towards predicting software defects and Just-in-time (JIT) defect prediction which predicts whether a code change is defective. While using features to determine potentially defective code change, inspection effort is still significant. As code change can impact several files, we investigate an open source project to identify potential gaps with features in JIT perspective. In addition, with a lack of publicly available JIT dataset that link the features with actual commits, we also present a new dataset that can be utilized in JIT and semantic analysis.