分析代码依赖关系的紧密性,以改进基于ir的可追溯性恢复

Hongyu Kuang, Jia Nie, Hao Hu, P. Rempel, Jian Lu, Alexander Egyed, Patrick Mäder
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引用次数: 21

摘要

信息检索(Information Retrieval, IR)基于软件构件之间的文本相似性来识别跟踪链接。然而,不同工件之间的词汇不匹配问题阻碍了基于ir的方法的性能。越来越多的工作通过将IR技术与代码依赖分析(如方法调用)相结合来解决这个问题。然而,到目前为止,组合方法的性能高度依赖于IR技术的正确性,并没有充分利用代码依赖分析。在本文中,我们将红外技术与密切度分析相结合,以提高基于红外的可追溯性恢复。具体地说,我们量化并利用两个类之间每个调用和数据依赖的“亲密度”,以提高可跟踪性候选列表的排名。基于三个现实世界系统的经验评估表明,我们的方法优于三个基线方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing closeness of code dependencies for improving IR-based Traceability Recovery
Information Retrieval (IR) identifies trace links based on textual similarities among software artifacts. However, the vocabulary mismatch problem between different artifacts hinders the performance of IR-based approaches. A growing body of work addresses this issue by combining IR techniques with code dependency analysis such as method calls. However, so far the performance of combined approaches is highly dependent to the correctness of IR techniques and does not take full advantage of the code dependency analysis. In this paper, we combine IR techniques with closeness analysis to improve IR-based traceability recovery. Specifically, we quantify and utilize the “closeness” for each call and data dependency between two classes to improve rankings of traceability candidate lists. An empirical evaluation based on three real-world systems suggests that our approach outperforms three baseline approaches.
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