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https://dx.doi.org/10.48550/ar...
Article . 2021
License: CC BY
Data sources: Datacite
DBLP
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Data sources: DBLP
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A Systematic Review of Automated Query Reformulations in Source Code Search

Authors: Mohammad Masudur Rahman 0001; Chanchal K. Roy;

A Systematic Review of Automated Query Reformulations in Source Code Search

Abstract

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 have attempted 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, the 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.

Keywords

D.2.1, FOS: Computer and information sciences, Computer Science - Machine Learning, D.2.5, Computer Science - Neural and Evolutionary Computing, D.2.7, D.2.5; D.2.1; D.2.7; D.2.13, Computer Science - Information Retrieval, Machine Learning (cs.LG), Software Engineering (cs.SE), Computer Science - Software Engineering, D.2.13, Neural and Evolutionary Computing (cs.NE), Information Retrieval (cs.IR)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Top 10%
Top 10%
Top 10%
Green