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Filtering of false positives from IR-based traceability links among software artifacts

机译:从软件工件之间基于IR的可追溯性链接过滤误报

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Correlation among software artifacts (also known as traceability links) of object oriented software plays a vital role in its maintenance. These traceability links are being commonly identified through Information Retrieval (IR) based techniques. But, it has been found that the resulting links from IR contain many false positives and some complementary approaches have been suggested for the purpose. Still, it usually requires manual verification of links which is neither desirable nor reliable. This paper suggests a new technique which can automatically filter out the false positives links (between requirement and source code) from IR and thus can help in reducing dependence as well as incorrectness of manual verification process. The proposed approach works on the basis of finding correlations among classes using either structural or co-changed dependency or both. A threshold is selected as a cut off on computed dependency values, to accept the presence of structural and co-changed dependency each. Now the traceability links are verified using these dependencies. If atleast one of the structural or co-change information validates the link obtained from IR approach, then that link is selected as candidate link, otherwise removed. Different thresholds have been experimented and comparison of results obtained from IR and the proposed approach is done. The results show that precision increases for all values of thresholds. Further analysis of results indicates that threshold in the range of 0.3 to 0.5 give better results. Hence, the proposed approach can be used as complementary to other Improved IR approaches to filter out false positives.
机译:面向对象软件的软件工件(也称为可追溯性链接)之间的相关性在其维护中起着至关重要的作用。这些可追溯性链接通常通过基于信息检索(IR)的技术来标识。但是,已经发现,来自IR的结果链接包含许多误报,并且为此目的建议了一些补充方法。但是,通常仍需要手动验证链接,这既不理想也不可靠。本文提出了一种新技术,该技术可以自动从IR中过滤掉误报链接(需求和源代码之间),从而有助于减少依赖性以及手动验证过程的不正确性。所提出的方法是在使用结构或共同更改的依存关系或两者之间找到类之间的相关关系的基础上工作的。选择一个阈值作为计算的相关性值的临界值,以接受每个结构性和共同更改的相关性的存在。现在,使用这些依赖项验证了可追溯性链接。如果结构或协同更改信息中的至少一个验证了从IR方法获得的链接,则将该链接选择为候选链接,否则将其删除。实验了不同的阈值,并比较了从IR和提出的方法获得的结果。结果表明,对于所有阈值,精度都会提高。结果的进一步分析表明,在0.3到0.5范围内的阈值可提供更好的结果。因此,提出的方法可以用作其他改进IR方法的补充,以滤除误报。

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