Search for Compatible Source Code

Author:

Cai Fuqi1,Wang Changjing1,Huang Qing1,Zuo Zhengkang1,Liao Yunyan1

Affiliation:

1. School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, P. R. China

Abstract

Third-party libraries always evolve and produce multiple versions. Lucene, for example, released ten new versions (from version 7.7.0 to 8.4.0) in 2019. These versions confuse the existing code search methods to retrieve the source code that is not compatible with local programming language. To solve this issue, we propose DCSE, a deep code search model based on evolving information (i.e. evolved code tokens and evolution description). DCSE first deeply excavates evolved code tokens and evolution description in the code evolution process; then it takes evolved code tokens and evolution description as one feature of source code and code description, respectively. With such fuller representation, DCSE embeds source code and its code description into a high-dimensional shared vector space, and makes the cosine distance of their vectors closer. For the ever-evolving third-party libraries like Lucene, the experimental results show that DCSE could retrieve the source code that is compatible with local programming language, it outperforms the state-of-the-art methods (e.g. CODEnn) by 56.9–60.9[Formula: see text] in RFVersion. For the rarely-evolving third-party libraries, DCSE outperforms the state-of-the-art methods (e.g. CODEnn) by 4–11[Formula: see text] in Precision.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Software

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Survey of Source Code Search: A 3-Dimensional Perspective;ACM Transactions on Software Engineering and Methodology;2024-06-28

2. Answering Uncertain, Under-Specified API Queries Assisted by Knowledge-Aware Human-AI Dialogue;IEEE Transactions on Software Engineering;2023

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