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arXiv 2006.05265cs.LGcs.SEstat.ML
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MISIM:一个基于上下文感知语义结构的神经代码语义相似度系统

MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure

Intel Labs(英特尔实验室);Georgia Institute of Technology(佐治亚理工学院);Intel(英特尔公司);MIT(麻省理工学院);University of Pennsylvania(宾夕法尼亚大学)
Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Niranjan Hasabnis, Paul Petersen, Timothy Mattson, Tim Kraska, Pradeep … 展开作者

Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Niranjan Hasabnis, Paul Petersen, Timothy Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich

摘要:

代码语义相似性可以用于许多任务,如代码推荐,自动软件缺陷校正和克隆检测。然而,这种系统的准确性尚未达到通用可靠性的水平。为了帮助解决这个问题,我们提出了机器推断代码相似性(MISIM),一个神经代码语义相似性系统,由两个核心组件组成:(i)MISIM使用一种新的上下文感知语义结构,这是专门为从代码语法中提升语义而构建的;(ii)MISIM使用一种可扩展的神经代码相似性评分算法,该算法可用于具有学习参数的各种神经网络架构。我们将MISIM与四个最先进的系统进行比较,包括两个额外的手工定制模型,超过328 K的程序由超过1800万行代码组成。我们的实验表明,MISIM具有8.08%的更好的准确性(使用MAP@R)相比,下一个表现最好的系统。

英文摘要:

Code semantics similarity can be used for many tasks such as code recommendation, automated software defect correction, and clone detection. Yet, the accuracy of such systems has not yet reached a level of general purpose reliability. To help address this, we present Machine Inferred Code Similarity (MISIM), a neural code semantics similarity system consisting of two core components: (i)MISIM uses a novel context-aware semantics structure, which was purpose-built to lift semantics from code syntax; (ii)MISIM uses an extensible neural code similarity scoring algorithm, which can be used for various neural network architectures with learned parameters. We compare MISIM to four state-of-the-art systems, including two additional hand-customized models, over 328K programs consisting of over 18 million lines of code. Our experiments show that MISIM has 8.08% better accuracy (using MAP@R) compared to the next best performing system.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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