# Improving IR-Based Bug Localization with Context-Aware Query Reformulation

arXiv: https://arxiv.org/abs/1808.00594 · HTML: https://mlsanity.com/paper/1808.00594/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2018.

## Built on by (most cited first, 11 of 11)

- [RLocator: Reinforcement Learning for Bug Localization](https://mlsanity.com/paper/2305.05586/) (2023)
- [SkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents](https://mlsanity.com/paper/2605.08386/) (2026)
- [The Forgotten Role of Search Queries in IR-based Bug Localization: An Empirical Study](https://mlsanity.com/paper/2108.05341/) (2021)
- [Improved IR-based Bug Localization with Intelligent Relevance Feedback](https://mlsanity.com/paper/2501.10542/) (2025)
- [A Systematic Review of Automated Query Reformulations in Source Code Search](https://mlsanity.com/paper/2108.09646/) (2021)
- [Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing](https://mlsanity.com/paper/2312.10448/) (2023)
- [SimClone: Detecting Tabular Data Clones using Value Similarity](https://mlsanity.com/paper/2407.12802/) (2024)
- [Reformulate, Retrieve, Localize: Agents for Repository-Level Bug Localization](https://mlsanity.com/paper/2512.07022/) (2025)
- [Improved Bug Localization with AI Agents Leveraging Hypothesis and Dynamic Cognition](https://mlsanity.com/paper/2601.12522/) (2026)
- [Ekka: Automated Diagnosis of Silent Errors in LLM Inference](https://mlsanity.com/paper/2606.04594/) (2026)
- [HyperFL: Query-Adaptive Representation Learning for Software Fault Localization](https://mlsanity.com/paper/2608.02967/) (2026)

## Abstract (arXiv)

Recent findings suggest that Information Retrieval (IR)-based bug localization techniques do not perform well if the bug report lacks rich structured information (eg relevant program entity names). Conversely, excessive structured information (eg stack traces) in the bug report might not always help the automated localization either. In this paper, we propose a novel technique--BLIZZARD-- that automatically localizes buggy entities from project source using appropriate query reformulation and effective information retrieval. In particular, our technique determines whether there are excessive program entities or not in a bug report (query), and then applies appropriate reformulations to the query for bug localization. Experiments using 5,139 bug reports show that our technique can localize the buggy source documents with 7%--56% higher Hit@10, 6%--62% higher MAP@10 and 6%--62% higher MRR@10 than the baseline technique. Comparison with the state-of-the-art techniques and their variants report that our technique can improve 19% in MAP@10 and 20% in MRR@10 over the state-of-the-art, and can improve 59% of the noisy queries and 39% of the poor queries.