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Enhancing example-based code search with functional semantics
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DOI：10.1016/j.jss.2020.110568.png)
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Abstract
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As the quality and quantity of open source code increase, effective and efficient search for code implementing certain semantics, or semantics-based code search, has become an emerging need for software developers to retrieve and reuse existing source code. Previous techniques in semantics-based code search encode the semantics of loop-free Java code snippets as constraints and utilize an SMT solver to find encoded snippets that match an input/output (IO) query. We present in this article the Quebio approach to semantics-based search for Java methods. Quebio advances the state-of-the-art by supporting important language features like invocation to library APIs and enabling the search to handle more data types like array/List, Set, and Map. Compared with existing approaches, Quebio also integrates a customized keyword-based search that uses as the input a textual, behavioral summary of the desired methods to quickly prune the methods to be checked against the IO examples. To evaluate the effectiveness and efficiency of Quebio, we constructed a repository of 14,792 methods from 723 open source Java projects hosted on GitHub and applied the approach to resolve 47 queries extracted from Stack-Overflow. Quebio was able to find methods correctly implementing the specified IO behaviors for 43 of the queries, significantly outperforming the existing semantics-based code search techniques. The average search time with Quebio was 213.2 seconds for each query. (C) 2020 Elsevier Inc. All rights reserved.
Keywords:
Semantics-based code search
Symbolic analysis
SMT solver
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