Improving bug localization using structured information retrieval

By Ripon K. Saha, Matthew Lease, Sarfraz Khurshid, Dewayne E. Perry. International Conference on Automated Software Engineering, 2013

433 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/1604de41ea8bc82aac0502daa309d3fed3f8495e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Lingming Zhang
2. Chun Xia
3. Satish Chandra
4. Yihao Li
5. Ruizhi Gao
6. W. E. Wong
7. Franz Wotawa
8. Rui Abreu

In Information Systems

1. Michael Pradel
2. Yinlin Deng
3. Chun Xia
4. Soren Dunn
5. Lingming Zhang
6. Satish Chandra
7. Yihao Li
8. Ruizhi Gao

In Software

1. Yinlin Deng
2. Chun Xia
3. Lingming Zhang
4. Soren Dunn
5. Simone Stumpf
6. Michael Pradel
7. Todd Kulesza
8. Margaret M. Burnett

Citations in the corpus

• Scaffle: bug localization on millions of files (2020, methodology): “For example, as shown in Figure 6(a), the top-5 predictions have a higher hit rate than the heuristic baseline.”
• A Survey on Software Fault Localization (2016, result): “Recently, some studies [85], [234], [298], [315], [425] have applied information retrieval techniques to software fault localization.”
• You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems (2014, background): “Recently, in work including information retrieval-based bug localization [50] and test generation [22], [25], the use of artificial intelligence and machine learning techniques has become a popular research topic, and a major aspect of endeavors to deal with increasing software complexity [23].”
• Agentless: Demystifying LLM-based Software Engineering Agents (2024, methodology): “Such IR-based techniques [87, 82, 88] formulate FL as a search problem and compare the textual similarity between code elements and the bug report (i.e., query).”
• From Word Embeddings to Document Similarities for Improved Information Retrieval in Software Engineering (2016, methodology): “MAP and MRR are standard evaluation metrics in IR, and were used previously in related work on bug localization [38, 46, 47].”
• Learning to rank relevant files for bug reports using domain knowledge (2014, methodology): “Compared to our method, the approach from [41] assumes all features are equally important and ignores the lexical gap between bug reports and source code (cid:12)les.”
• SHERLOC: Structured Diagnostic Localization for Code Repair Agents (2026, unknown): “BugLoca-Q tor [Zhou et al., 2012] ranks files using textual similarity and prior bug reports; BLUiR [Saha et al., 2013] incorporates structured source-code information; AmaLgam [Wang and Lo, 2014] et al., 2024, Yu et al., 2025, Chen et al., 2025].”