TroBo: A Novel Deep Transfer Model for Enhancing Cross-Project Bug Localization

Ziye Zhu, Yu Wang, Yun Li

Abstract: Bug localization, which aims to locate buggy files in the software project by leveraging bug reports, plays an important role in software quality control. Recently, many automatic bug localization methods based on historical bug-fix data (i.e., bug reports labeled with corresponding buggy code files) have been proposed. However, the lack of bug-fix data for software projects in the early stages of development limits the performance of most existing supervised learning methods. To address this issue, we propose a deep transfer bug localization model called TroBo, which can transfer shared knowledge from label-rich source project to the target project. Specifically, we accomplish the knowledge transfer on both the bug report and code file. For processing bug reports, which belong to informal text data, we design a soft attention-based module to alleviate the noise problem. For processing code files, we apply an adversarial strategy to learn the project-shared features, and additionally extract project-exclusive features for each project. Furthermore, a project-aware classifier is introduced in TroBo to avoid redundancy between shared and exclusive features. Extensive experiments on four large-scale real-world projects demonstrate that our model significantly outperforms the state-of-the-art techniques.