An Investigation into Code Search Engines: The State of the Art Versus Developer Expectations

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Date
2022-07-15
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Virginia Tech
Abstract

An essential software development tool, code search engines are expected to provide superior accuracy, usability, and performance. However, prior research has neither (1) summarized, categorized, and compared representative code search engines, nor (2) analyzed the actual expectations that developers have for code search engines. This missing knowledge can empower developers to fully benefit from search engines, academic researchers to uncover promising research directions, and industry practitioners to properly marshal their efforts. This thesis fills the aforementioned gaps by drawing a comprehensive picture of code search engines, including their definition, standard processes, existing solutions, common alternatives, and developers' perspectives. We first study the state of the art in code search engines by analyzing academic papers, industry releases, and open-source projects. We then survey more than a 100 software developers to ascertain their usage of and preferences for code search engines. Finally, we juxtapose the results of our study and survey to synthesize a call-for-action for researchers and industry practitioners to better meet the demands software developers make on code search engines. We present the first comprehensive overview of state-of-the-art code search engines by categorizing and comparing them based on their respective search strategies, applicability, and performance. Our user survey revealed a surprising lack of awareness among many developers w.r.t. code search engines, with a high preference for using general-purpose search engines (e.g., Google) or code repositories (e.g., GitHub) to search for code. Our results also clearly identify typical usage scenarios and sought-after properties of code search engines. Our findings can guide software developers in selecting code search engines most suitable for their programming pursuits, suggest new research directions for researchers, and help programming tool builders in creating effective code search engine solutions.

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Code search engines, User survey, Domain analysis
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