Joint Embedding of Semantic and Statistical Features for Effective Code Search
| Autor: | Xianglong Kong, Supeng Kong, Ming Yu, Chengjie Du |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Applied Sciences, Vol 12, Iss 19, p 10002 (2022) |
| Druh dokumentu: | article |
| ISSN: | 12191000 2076-3417 |
| DOI: | 10.3390/app121910002 |
| Popis: | Code search is an important approach to improve effectiveness and efficiency of software development. The current studies commonly search target code based on either semantic or statistical information in large datasets. Semantic and statistical information have hidden relationships between them since they describe code snippets from different perspectives. In this work, we propose a joint embedding model of semantic and statistical features to improve the effectiveness of code annotation. Then, we implement a code search engine, i.e., JessCS, based on the joint embedding model. We evaluate JessCS on more than 1 million lines of code snippets and corresponding descriptions. The experimental results show that JessCS performs more effective than UNIF-based approach, with at least 13% improvements on the studied metrics. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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