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首页> 外文会议>IEEE/ACM 6th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering >CodeCatch: Extracting Source Code Snippets from Online Sources
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CodeCatch: Extracting Source Code Snippets from Online Sources

机译：CodeCatch：从在线资源中提取源代码片段

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摘要

Nowadays, developers rely on online sources to find example snippets that address the programming problems they are trying to solve. However, contemporary API usage mining methods are not suitable for locating easily reusable snippets, as they provide usage examples for specific APIs, thus requiring the developer to know which library to use beforehand. On the other hand, the approaches that retrieve snippets from online sources usually output a list of examples, without aiding the developer to distinguish among different implementations and without offering any insight on the quality and the reusability of the proposed snippets. In this work, we present CodeCatch, a system that receives queries in natural language and extracts snippets from multiple online sources. The snippets are assessed both for their quality and for their usefulness/preference by the developers, while they are also clustered according to their API calls to allow the developer to select among the different implementations. Preliminary evaluation of CodeCatch in a set of indicative programming problems indicates that it can be a useful tool for the developer.
机译：如今，开发人员依靠在线资源来找到示例片段，以解决他们试图解决的编程问题。但是，现代的API使用情况挖掘方法不适合查找易于重用的代码段，因为它们提供了特定API的使用示例，因此要求开发人员事先知道要使用哪个库。另一方面，从在线资源中检索代码段的方法通常会输出示例列表，而不帮助开发人员在不同的实现之间进行区分，也不会提供所建议代码段的质量和可重用性的任何见解。在这项工作中，我们介绍CodeCatch，这是一个以自然语言接收查询并从多个在线资源中提取摘要的系统。开发人员不仅要评估代码片段的质量，还要评估其有用性/偏好，同时还根据其API调用对代码片段进行聚类，以允许开发人员在不同的实现中进行选择。对一组指示性编程问题中的CodeCatch的初步评估表明，它可以对开发人员有用。

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