Abstract
To improve code search, many query expansion (QE) approaches use APIs or crowd knowledge for expanding a query. However, these approaches may sometimes negatively impact the retrieval performance. This is because they can’t distinguish the relevant terms from the irrelevant ones among a large set of candidate expansion terms and expand a query with irrelevant terms. In this paper, we propose QREC, a query reformulation approach with evolving contexts that refer to new/deleted terms and dependent terms during the code evolution. By considering the new terms as the relevant and the deleted terms as the irrelevant, QREC could reformulate a query with appropriate expansion terms. The experimental results show that QREC outperforms the state-of-the-art QE approaches (e.g., CodeHow and QECK) by 9–11% and improves the precision of the code search algorithms IR, Portfolio and VF by up to 37–45%.
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Notes
The full qualified name (FQN),declaration, instantiation and the signatures of methods invoked and filed accessed, etc.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61902162, 61762049, 61872272, 61877031, 61802350, 61862033, 61772246, 61562042 and 61672470).
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Huang, Q., Wu, G. Enhance code search via reformulating queries with evolving contexts. Autom Softw Eng 26, 705–732 (2019). https://doi.org/10.1007/s10515-019-00263-5
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DOI: https://doi.org/10.1007/s10515-019-00263-5