A Systematic Review of Automated Query Reformulations in Source Code Search
Not on the map. Why?
Abstract (arXiv)
Fixing software bugs and adding new features are two of the major maintenance tasks. Software bugs and features are reported as change requests. Developers consult these requests and often choose a few keywords from them as an ad hoc query. Then they execute the query with a search engine to find the exact locations within software code that need to be changed. Unfortunately, even experienced developers often fail to choose appropriate queries, which leads to costly trials and errors during a code search. Over the years, many studies attempt to reformulate the ad hoc queries from developers to support them. In this systematic literature review, we carefully select 70 primary studies on query reformulations from 2,970 candidate studies, perform an in-depth qualitative analysis (e.g., Grounded Theory), and then answer seven research questions with major findings. First, to date, eight major methodologies (e.g., term weighting, term co-occurrence analysis, thesaurus lookup) have been adopted to reformulate queries. Second, the existing studies suffer from several major limitations (e.g., lack of generalizability, vocabulary mismatch problem, subjective bias) that might prevent their wide adoption. Finally, we discuss the best practices and future opportunities to advance the state of research in search query reformulations.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 215 references that are in the map. First posted on arXiv 22 Aug 2021.
Builds on
19 of 215 references.
References · 19
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2020Opportunities and Challenges in Code Search Toolscited by 22
- 2017Query Expansion Based on Crowd Knowledge for Code Searchcited by 18
- 2018RACK: Automatic API Recommendation using Crowdsourced Knowledgecited by 12
- 2018Improving IR-Based Bug Localization with Context-Aware Query Reformulationcited by 11
- 2015SWIM: Synthesizing What I Meancited by 10
- 2018Evaluating How Developers Use General-Purpose Web-Search for Code Retrievalcited by 6
- 2021The Forgotten Role of Search Queries in IR-based Bug Localization: An Empirical Studycited by 5
- 2018Improved Query Reformulation for Concept Location using CodeRank and Document Structurescited by 4
- 2018Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analyticscited by 4
- 2021Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflowcited by 4
7 more
- 2018On the Use of Context in Recommending Exception Handling Code Examplescited by 3
- 2018QUICKAR: Automatic Query Reformulation for Concept Location using Crowdsourced Knowledgecited by 3
- 2021Why are Some Bugs Non-Reproducible? An Empirical Investigation using Data Fusioncited by 3
- 2015Exploring Query Categorisation for Query Expansion: A Studycited by 2
- 2018STRICT: Information Retrieval Based Search Term Identification for Concept Locationcited by 2
- 2019Recommending Comprehensive Solutions for Programming Tasks by Mining Crowd Knowledgecited by 2
- 2018TextRank Based Search Term Identification for Software Change Taskscited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.