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2013 17th European Conference on Software Maintenance and Reengineering
When and How Using Structural Information to Improve IR-Based Traceability Recovery
Genova, Italy Italy
March 05-March 08
ISBN: 978-1-4673-5833-0
Information Retrieval (IR) has been widely accepted as a method for automated traceability recovery based on the textual similarity among the software artifacts. However, a notorious difficulty for IR-based methods is that artifacts may be related even if they are not textually similar. A growing body of work addresses this challenge by combining IR-based methods with structural information from source code. Unfortunately, the accuracy of such methods is highly dependent on the IR methods. If the IR methods perform poorly, the combined approaches may perform even worse. In this paper, we propose to use the feedback provided by the software engineer when classifying candidate links to regulate the effect of using structural information. Specifically, our approach only considers structural information when the traceability links from the IR methods are verified by the software engineer and classified as correct links. An empirical evaluation conducted on three systems suggests that our approach outperforms both a pure IR-based method and a simple approach for combining textual and structural information.
Index Terms:
Empirical studies,Traceability Link Recovery
Citation:
Annibale Panichella, Collin McMillan, Evan Moritz, Davide Palmieri, Rocco Oliveto, Denys Poshyvanyk, Andrea De Lucia, "When and How Using Structural Information to Improve IR-Based Traceability Recovery," csmr, pp.199-208, 2013 17th European Conference on Software Maintenance and Reengineering, 2013
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