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Discovering Program Topoi via Hierarchical Agglomerative Clustering

机译：通过分层聚集群集发现程序Topoi

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摘要

In long lifespan software systems, specification documents can be outdated or even missing. Developing new software releases or checking whether some user requirements are still valid becomes challenging in this context. This challenge can be addressed by extracting high-level observable capabilities of a system by mining its source code and the available source-level documentation. This paper presents feature extraction and traceability (FEAT), an approach that automatically extractsntopoin, which are summaries of the main capabilities of a program, given under the form of collections of code functions along with annindexn. FEAT acts in two steps: first,nclustering:nby mining the available source code, possibly augmented with code-level comments, hierarchical agglomerative clustering groups similar code functions. In addition, this process gathers an index for each function. Second,nentry point selection:nfunctions within a cluster are then ranked and presented to validation engineers as topoi candidates. We implemented FEAT on top of a general-purpose test management and optimization platform and performed an experimental study over 15 open-source software projects amounting to more than 1 M lines of codes proving that automatically discovering topoi is feasible and meaningful on realistic projects.
机译：在使用寿命长的软件系统中，规格文档可能会过时甚至丢失。在这种情况下，开发新的软件版本或检查某些用户要求是否仍然有效变得很困难。通过挖掘系统的源代码和可用的源代码级文档来提取系统的高级可观察功能，可以解决此难题。本文介绍了特征提取和可追溯性（FEAT），这是一种自动提取n topoin，它是程序主要功能的摘要，以代码功能集合的形式给出，并带有ann 索引 n。 FEAT分两个步骤执行：首先，n <斜体xmlns：mml =“ http://www.w3.org/1998/Math/MathML” xmlns：xlink =“ http://www.w3.org/1999/xlink “>群集：通过挖掘可用的源代码（可能在代码级别的注释中进行扩展），将分层的聚集性聚类对相似的代码功能进行分组。此外，此过程还会为每个功能收集一个索引。其次，n 入口点选择：然后，将群集中的功能排序并作为拓扑候选者呈现给验证工程师。我们在通用测试管理和优化平台上实施了FEAT，并对15个开源软件项目进行了实验研究，这些项目总计超过100万行代码，证明自动发现topoi在现实项目中是可行且有意义的。

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