Too Few Bug Reports? Exploring Data Augmentation for Improved Changeset-based Bug Localization

TL;DR

This paper investigates the use of data augmentation techniques to generate synthetic training data for transformer-based deep learning models in bug localization, aiming to improve performance with limited real bug reports.

Contribution

It introduces novel data augmentation operators and a data balancing strategy to enhance bug localization models trained on scarce bug report data.

Findings

Synthetic data improves bug localization accuracy

Data augmentation helps models generalize better across code bases

Proposed methods outperform baseline models without augmentation

Abstract

Modern Deep Learning (DL) architectures based on transformers (e.g., BERT, RoBERTa) are exhibiting performance improvements across a number of natural language tasks. While such DL models have shown tremendous potential for use in software engineering applications, they are often hampered by insufficient training data. Particularly constrained are applications that require project-specific data, such as bug localization, which aims at recommending code to fix a newly submitted bug report. Deep learning models for bug localization require a substantial training set of fixed bug reports, which are at a limited quantity even in popular and actively developed software projects. In this paper, we examine the effect of using synthetic training data on transformer-based DL models that perform a more complex variant of bug localization, which has the goal of retrieving bug-inducing changesets for each bug report. To generate high-quality synthetic data, we propose novel data augmentation operators that act on different constituent components of bug reports. We also describe a data balancing strategy that aims to create a corpus of augmented bug reports that better reflects the entire source code base, because existing bug reports used as training data usually reference a small part of the code base.