# Improved IR-based Bug Localization with Intelligent Relevance Feedback

arXiv: https://arxiv.org/abs/2501.10542 · HTML: https://mlsanity.com/paper/2501.10542/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 87 references that are in the map.
- First posted on arXiv 17 Jan 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://mlsanity.com/paper/2109.01247/) (2021)
- [LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models](https://mlsanity.com/paper/2305.13655/) (2023)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Prompting Large Language Models with Speech Recognition Abilities](https://mlsanity.com/paper/2307.11795/) (2023)
- [Unleashing the potential of prompt engineering for large language models](https://mlsanity.com/paper/2310.14735/) (2023)
- [Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models](https://mlsanity.com/paper/2304.09337/) (2023)
- [Prompting Is All You Need: Automated Android Bug Replay with Large Language Models](https://mlsanity.com/paper/2306.01987/) (2023)
- [NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval](https://mlsanity.com/paper/1810.12936/) (2018)
- [Fast Changeset-based Bug Localization with BERT](https://mlsanity.com/paper/2112.14169/) (2021)
- [RACK: Automatic API Recommendation using Crowdsourced Knowledge](https://mlsanity.com/paper/1807.02953/) (2018)
- [Improving IR-Based Bug Localization with Context-Aware Query Reformulation](https://mlsanity.com/paper/1808.00594/) (2018)
- [Deep Reinforced Query Reformulation for Information Retrieval](https://mlsanity.com/paper/2007.07987/) (2020)
- [RLocator: Reinforcement Learning for Bug Localization](https://mlsanity.com/paper/2305.05586/) (2023)
- [Improving Neural Language Models by Segmenting, Attending, and Predicting the Future](https://mlsanity.com/paper/1906.01702/) (2019)
- [The Forgotten Role of Search Queries in IR-based Bug Localization: An Empirical Study](https://mlsanity.com/paper/2108.05341/) (2021)
- [Improved Query Reformulation for Concept Location using CodeRank and Document Structures](https://mlsanity.com/paper/1807.04488/) (2018)
- [A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting)](https://mlsanity.com/paper/2310.04438/) (2023)

## Built on by (most cited first, 5 of 5)

- [How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests](https://mlsanity.com/paper/2601.17581/) (2026)
- [Knowledge-Graph-Driven Data Synthesis for Low-Resource Software Development: A HarmonyOS Case Study](https://mlsanity.com/paper/2512.00380/) (2025)
- [Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent](https://mlsanity.com/paper/2512.14990/) (2025)
- [Improved Bug Localization with AI Agents Leveraging Hypothesis and Dynamic Cognition](https://mlsanity.com/paper/2601.12522/) (2026)
- [BLAgent: Agentic RAG for File-Level Bug Localization](https://mlsanity.com/paper/2605.17965/) (2026)

## Abstract (arXiv)

Software bugs pose a significant challenge during development and maintenance, and practitioners spend nearly 50% of their time dealing with bugs. Many existing techniques adopt Information Retrieval (IR) to localize a reported bug using textual and semantic relevance between bug reports and source code. However, they often struggle to bridge a critical gap between bug reports and code that requires in-depth contextual understanding, which goes beyond textual or semantic relevance. In this paper, we present a novel technique for bug localization - BRaIn - that addresses the contextual gaps by assessing the relevance between bug reports and code with Large Language Models (LLM). It then leverages the LLM's feedback (a.k.a., Intelligent Relevance Feedback) to reformulate queries and re-rank source documents, improving bug localization. We evaluate BRaIn using a benchmark dataset, Bench4BL, and three performance metrics and compare it against six baseline techniques from the literature. Our experimental results show that BRaIn outperforms baselines by 87.6%, 89.5%, and 48.8% margins in MAP, MRR, and HIT@K, respectively. Additionally, it can localize approximately 52% of bugs that cannot be localized by the baseline techniques due to the poor quality of corresponding bug reports. By addressing the contextual gaps and introducing Intelligent Relevance Feedback, BRaIn advances not only theory but also improves IR-based bug localization.