.png){kind=icon}
返回
Code search engines for the next generation
.png){kind=icon}
OA
AI
DOI：10.1016/j.jss.2024.112065.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Given the abundance of software in open source repositories, code search engines are increasingly turning to big datatechnologies such as natural language processing and machine learning, to deliver more useful search results. However, like the syntax -based approaches traditionally used to analyze and compare code in the first generation of code search engines, big data technologies are essentially static analysis processes. When dynamic properties of software, such as run-time behavior (i.e., semantics) and performance, are among the search criteria, the exclusive use of static algorithms has a significant negative impact on the precision and recall of the search results as well as other key usability factors such as ranking quality. Therefore, to address these weaknesses and provide a more reliable and usable service, the next generation of code search engines needs to complement static code analysis techniques with equally large-scale, dynamic analysis techniques based on its execution and observation. In this paper we describe a new software platform specifically developed to achieve this by simplifying and largely automating the dynamic analysis (i.e., observation) of code at a large scale. We show how this platform can combine dynamically observed properties of code modules with static properties to improve the quality and usability of code search results.
Keyword:
Software reuse
Code search
Big data
Big code
Behavior
Dynamic
Semantics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.1
论文数:
5.5K
被引数:
8.4K
机构
引用论文
.png){kind=icon}
Orientation to sex pheromone in the American cockroach: Analysis of chemo-orientation mechanisms
PREAI
分享收藏Synthesizing Data Using Variational Autoencoders for Handling Class Imbalanced Deep Learning使用变分自编码器合成数据以处理类不平衡深度学习
PREAI
分享收藏
查看更多内容