2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · 2022 · 17 citations · 39 references
ComputingComputer ScienceComputational LinguisticsInformation RetrievalNatural Language ProcessingQuestion AnsweringWeb SearchRefinementSearches
In source code search, a common information-seeking strategy involves providing a short initial query with a broad meaning, and then iteratively refining the query using terms gleaned from the results of subsequent searches. This strategy requires programmers to spend time reading search results that are irrelevant to their development needs. In contrast, when programmers seek information from other humans, they typically refine queries by asking and answering clarifying questions. Clarifying questions have been shown to benefit general-purpose search engines, but have not been examined in the context of code search. We present a method for generating natural-sounding clarifying questions using information extracted from function names and comments. Our method outperformed a keyword-based method for single-turn refinement in synthetic studies, and was associated with shorter search durations in human studies.
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Relevance feedback in information retrieval
J. J. Rocchio · Medical Entomology and Zoology · 1971 · 2.6K citations
The vocabulary problem in human-system communication
George W. Furnas, Thomas K. Landauer, Louis M. Gomez et al. · Communications of the ACM · 1987 · 1.5K citations · Full text
Computing, Computer Science, Design +15
A Theoretical Framework for Conversational Search
Filip Radlinski, Nick Craswell · 2017 · 430 citations
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit et al. · arXiv (Cornell University) · 2019 · 422 citations · Full text