Joint Embedding of Semantic and Statistical Features for Effective Code Search

Kong, Xianglong; Kong, Supeng; Yu, Ming; Du, Chengjie

doi:10.3390/app121910002

Cited by 2 publications

(5 citation statements)

References 39 publications

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“…The article uses four commonly used evaluation metrics to assess the effectiveness of the model, namely FRank, SuccessRate@k, Precision@k, Mean Reciprocal Rank (MRR) and Mean Search Time (MST). The above five metrics are widely used in code search research work 7,26–28 . Meanwhile, this article creates a new evaluation metric Mean Search Time (MST) to assess the efficiency of the model.…”

Section: Methodsmentioning

confidence: 99%

“…The FRank (also known as best hit rank 26 ) is the rank of the first hit result in the result list 27 . It is important as users scan the results from top to bottom.…”

Section: Methodsmentioning

confidence: 99%

“…The above five metrics are widely used in code search research work. 7,[26][27][28] Meanwhile, this article creates a new evaluation metric Mean Search Time (MST) to assess the efficiency of the model.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The FRank (also known as best hit rank 26 ) is the rank of the first hit result in the result list. 27 It is important as users scan the results from top to bottom. A smaller FRank implies lower inspection effort for finding the desired result.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Precision@k is important because developers often inspect multiple results of different usages to learn from. 27 A better code search engine should allow developers to inspect less noisy results. The higher the metric values, the better the code search performance.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Deep code search efficiency based on clustering

Liu,

2024

Concurrency and Computation

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The deep‐learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering‐based code search model (C‐DCS). C‐DCS uses the K‐Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C‐DCS first matches the query vector with the K center vectors to get the best matching center vector. After matching the center vector, C‐DCS matches the query vector with code vectors in the cluster corresponding to the best matching center vector one by one and then gets the best matching code snippet vector. To verify the efficiency of C‐DCS in the code search task, experimental analysis was built on a large dataset. The experimental results showed that C‐DCS saves 92.2% of the search time compared to the baseline model while remaining the accuracy. In the experimental evaluation section, we optimized the K‐Means algorithm to improve the code search efficiency of C‐DCS further, reducing the search time to 93.8% of the baseline model. Hence, C‐DCS reduces the code search time greatly with not affecting the accuracy, improving the efficiency of software development.

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Section: Methodsmentioning

confidence: 99%

“…The FRank (also known as best hit rank 26 ) is the rank of the first hit result in the result list 27 . It is important as users scan the results from top to bottom.…”

Section: Methodsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

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Deep code search efficiency based on clustering

Liu,

2024

Concurrency and Computation

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A Survey of Source Code Search: A 3-Dimensional Perspective

Sun,

Fang,

et al. 2024

ACM Trans. Softw. Eng. Methodol.

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(Source) code search is widely concerned by software engineering researchers because it can improve the productivity and quality of software development. Given a functionality requirement usually described in a natural language sentence, a code search system can retrieve code snippets that satisfy the requirement from a large-scale code corpus, e.g., GitHub. To realize effective and efficient code search, many techniques have been proposed successively. These techniques improve code search performance mainly by optimizing three core components, including query understanding component, code understanding component, and query-code matching component. In this paper, we provide a 3-dimensional perspective survey for code search. Specifically, we categorize existing code search studies into query-end optimization techniques, code-end optimization techniques, and match-end optimization techniques according to the specific components they optimize. These optimization techniques are proposed to enhance the performance of specific components, and thus the overall performance of code search. Considering that each end can be optimized independently and contributes to the code search performance, we treat each end as a dimension. Therefore, this survey is 3-dimensional in nature, and it provides a comprehensive summary of each dimension in detail. To understand the research trends of the three dimensions in existing code search studies, we systematically review 68 relevant literatures. Different from existing code search surveys that only focus on the query end or code end or introduce various aspects shallowly (including codebase, evaluation metrics, modeling technique, etc.), our survey provides a more nuanced analysis and review of the evolution and development of the underlying techniques used in the three ends. Based on a systematic review and summary of existing work, we outline several open challenges and opportunities at the three ends that remain to be addressed in future work.

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Joint Embedding of Semantic and Statistical Features for Effective Code Search

Cited by 2 publications

References 39 publications

Deep code search efficiency based on clustering

Deep code search efficiency based on clustering

A Survey of Source Code Search: A 3-Dimensional Perspective

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