CodeHow: Effective Code Search Based on API Understanding and Extended Boolean Model (E)

Lv, Fei; Zhang, Hongyu; Lou, Jian–Guang; Wang, Shaowei; Zhang, Dongmei; Zhao, Jianjun

doi:10.1109/ase.2015.42

Cited by 225 publications

(191 citation statements)

References 37 publications

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“…Note that only the results, which both receive relevant feedback and need not to be modified subsequently, are labeled as relevant. Thus the precision of CodeHow and CS is lower than previous papers, such as ref [2], [10].…”

Section: Resultsmentioning

confidence: 46%

“…CodeHow is the latest query-expanded code search tool [2]. To reprogram it, we index the online MSDN 8 document as expansion library using Lucene.…”

Section: A Setupmentioning

confidence: 99%

“…Wang et al [5] proposed an active code search approach which incorporates user feedback as "synonyms" to refine the query. Fei et al [2] propose a latest code search technique that could understand the APIs a user query refers to and considers both text similarity and potential APIs. In addition, there are other query expansions either by using an appropriate ontology [20], natural language [14], or collaborative feedback [13].…”

Section: Related Workmentioning

confidence: 99%

“…But these approaches were impractical, because they required too little or too much specification. The current work supports query expansion to promise the better usability, such as CodeHow [2]. It considers the impact of the APIs and expands the query with the APIs.…”

Section: Introductionmentioning

confidence: 99%

“…Early code search engines, e.g., Google 1 , Krugle 2 and Koders 3 , offered only the keyword-based search with low precision. The later work did semantic search to enhance the accuracy of the search results, e.g., signature matching, type matching [15], [10].…”

Section: Introductionmentioning

confidence: 99%

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SnippetGen:Enhancing the Code Search via Intent Predicting

Huang

Wang

Yang

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-To enable the cod sarch results to run immediately without any subsequent modification, an intent-enhanced code search approach (IECS) is proposed. It has the ability of intent predicting to guess what else a user might do after obtaining the search results. Based on the intent-relevant semantic and structural matches, IECS improves the performance of code search by incorporating the intent for expansion. To perform IECS, the code search tool SnippetGen is implemented. Compared with CodeHow and Google Code Search (CS), SnippetGen outperforms them by 28.5% with a precision score of 0.846 (i.e., 84.6% of the first results are relevant).

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Section: Resultsmentioning

confidence: 46%

“…CodeHow is the latest query-expanded code search tool [2]. To reprogram it, we index the online MSDN 8 document as expansion library using Lucene.…”

Section: A Setupmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

SnippetGen:Enhancing the Code Search via Intent Predicting

Huang

Wang

Yang

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Multilayer self‐attention residual network for code search

Liu

Zhang

2023

Concurrency and Computation

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Software developers usually search existing code snippets in open source code repositories to modify and reuse them. Therefore, how to get the right code snippet from the open-source code repository quickly and accurately is the focus of current software development research. Nowadays, code search is one of the solutions. To improve the accuracy of source code feature information representation and the accuracy of code search. A multilayer self-attention residual network-based code search model (MSARN-CS) is proposed in this paper. In the MSARN-CS model, not only the weight of each word in the code sequence unit is considered but also the effect of embedding between code sequence units is calculated. In addition, an optimization model of residuals is introduced to compensate for the loss of information in the code sequences during the model training. To verify the search effectiveness of the MSARN-CS model, three other baseline models are compared on the basis of extensive source code data.The experimental results show that the MSARN-CS model has better search results compared with the baseline model. For parameter Recall@1, the experimental result of MSARN-CS model was 9.547, which as 100.90%, 73.87%, 60.37%, and 2.55% better compared to CODEnn, CRLCS, SAN-CS-and SAN-CS, respectively. For the parameter Recall@5, the results improved by 26.67%, 36.23%, 36.21%, and 1.63%, respectively, and for the parameter Recall@10, the results improved by 13.92%, 25.70%, 20.78%, and 2.23%, respectively. For the parameter mean reciprocal rank, the results improved by 52.89%, 76.17%, 63.38%, and 3.88%, respectively. For the parameter normalized discounted cumulative gain, the results improved by 54.22%, 60.55%, 50.28%, and 3.30%, respectively. The MSARN-CS model proposed in the paper can effectively improve the accuracy of code search and enhance the programming efficiency of developers.

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Combining bidirectional long short‐term memory and self‐attention mechanism for code search

Cao

Liu

2023

Concurrency and Computation

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With the wide application of deep learning in code search, especially the proposed code search model based on attention mechanism, the accuracy of code search has been greatly improved. However, the attention mechanism only captures the attention weight relationship between two words in the code fragment, without considering the contextual semantic relationship that exists between words in the code fragment, which can help improve the accuracy of code search. To address this problem, this paper proposes a model that combining bidirectional long short-term memory and self-attention mechanisms for code search (CBLSAM-CS). The model first captures the contextual semantic relationship of each word in the code fragment by long-short term memory network, and then uses the self-attention mechanism to extract deep-level features of the sequence. In order to verify the effectiveness of the proposed model, the paper has been conducted an experimental comparison with three other baseline models, CODEnn, CARLCS-CNN, and SAN-CS, on the basis of a public dataset containing 18 million code fragments. The experimental results show that the proposed model in this paper achieves 92.24% and 93.55% in mean reciprocal rank value and normalized discounted cumulative gain metrics, respectively, which are better than the baseline model. Therefore, it shows that the CBLSAM-CS model proposed in this paper can effectively improve the accuracy and efficiency of code search.

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CodeHow: Effective Code Search Based on API Understanding and Extended Boolean Model (E)

Cited by 225 publications

References 37 publications

SnippetGen:Enhancing the Code Search via Intent Predicting

SnippetGen:Enhancing the Code Search via Intent Predicting

Multilayer self‐attention residual network for code search

Combining bidirectional long short‐term memory and self‐attention mechanism for code search

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