Continuously Learning Bug Locations
Paulina Stevia Nouwou Mindom, Leuson Da Silva, Amin Nikanjam and, Foutse Khomh

TL;DR
This paper explores the use of Continual Learning techniques to improve bug localization in software development, especially in non-stationary environments where code evolves, outperforming traditional deep learning methods significantly.
Contribution
It introduces a novel application of Continual Learning for bug localization, demonstrating superior performance and efficiency over existing deep learning approaches in dynamic settings.
Findings
CL techniques outperform DL methods by up to 61% in MRR
CL reduces training effort by up to 5x
CL effectively mitigates catastrophic forgetting
Abstract
Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based techniques show promising results by leveraging structural information from the code and learning links between changesets and bug reports. However, since source code associated with changesets evolves, the performance of such models tends to degrade over time due to concept drift. Aiming to address this challenge, in this paper, we evaluate the potential of using Continual Learning (CL) techniques in multiple sub-tasks setting for bug localization (each of which operates on either stationary or non-stationary data), comparing it against a bug localization technique that leverages the BERT model, a deep reinforcement learning-based technique that leverages the A2C algorithm, and a DL-based function-level interaction model for semantic bug…
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Taxonomy
TopicsData Mining Algorithms and Applications · Software Testing and Debugging Techniques · Machine Learning and Data Classification
