Support for traceability management of software artefacts using Natural Language Processing

2016 Moratuwa Engineering Research Conference (MERCon) Pub Date : 2016-04-05 DOI:10.1109/MERCON.2016.7480109

A. Arunthavanathan, S. Shanmugathasan, S. Ratnavel, V. Thiyagarajah, I. Perera, D. Meedeniya, D. Balasubramaniam

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引用次数: 16

Abstract

One of the major problems in software development process is managing software artefacts. While software evolves, inconsistencies between the artefacts do evolve as well. To resolve the inconsistencies in change management, a tool named “Software Artefacts Traceability Analyzer (SAT-Analyzer)” was introduced as the previous work of this research. Changes in software artefacts in requirement specification, Unified Modelling Language (UML) diagrams and source codes can be tracked with the help of Natural Language Processing (NLP) by creating a structured format of those documents. Therefore, in this research we aim at adding an NLP support as an extension to SAT-Analyzer. Enhancing the traceability links created in the SAT-analyzer tool is another focus due to artefact inconsistencies. This paper includes the research methodology and relevant research carried out in applying NLP for improved traceability management. Tool evaluation with multiple scenarios resulted in average Precision 72.22%, Recall 88.89% and F1 measure of 78.89% suggesting high accuracy for the domain.

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使用自然语言处理支持软件工件的可追溯性管理

软件开发过程中的主要问题之一是软件工件的管理。在软件发展的同时，工件之间的不一致性也在发展。为了解决变更管理中的不一致性，作为本研究的前期工作，引入了一个名为“软件工件可追溯性分析器(SAT-Analyzer)”的工具。在自然语言处理(NLP)的帮助下，可以通过创建这些文档的结构化格式来跟踪需求规范、统一建模语言(UML)图和源代码中的软件工件的变化。因此，在本研究中，我们的目标是增加一个NLP支持作为SAT-Analyzer的扩展。由于工件不一致，增强在sat分析器工具中创建的可跟踪性链接是另一个重点。本文包括研究方法和应用NLP改进可追溯性管理的相关研究。多个场景下的工具评估结果显示，平均精密度为72.22%，召回率为88.89%，F1测量值为78.89%，表明该领域具有较高的准确率。

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2016 Moratuwa Engineering Research Conference (MERCon)

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