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

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arxiv 2305.16430 v2 pith:LUU27W54 submitted 2023-05-25 cs.SE

Agnieszka Ciborowska , Kostadin Damevski This is my paper · ORCID
classification cs.SE
keywords datareportslocalizationtrainingcodemodelsaimsapplications
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing IR-based Fault Localization using Large Language Models

    cs.SE 2024-12 reject novelty 5.0 of 10

    GPT-4-generated queries, user-feedback reformulation, and a learning-to-rank model reportedly raise IR-based fault localization MRR to 0.677 on the Bench4BL dataset, beating seven prior techniques.

  2. When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey

    cs.SE 2025-04 conditional novelty 4.0 of 10

    A systematic literature survey of 61 deep-learning-based IRBL studies that proposes taxonomies and a performance overview, concluding that DL mitigates lexical gap, code structure, and cold-start issues while LLM-base...

  3. Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models

    cs.SE 2025-02 reject novelty 4.0 of 10

    A flaky-test classifier scores 8% higher on augmented copies of its training data than on independent test cases, but the comparison conflates augmentation with train-test overlap.

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