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Exploiting Parts-of-Speech for Effective Automated Requirements Traceability

Title:

Exploiting Parts-of-Speech for Effective Automated Requirements Traceability

Ali, Nasir, Cai, Haipeng, Hamou-Lhadj, Abdelwahab and Hassine, Jameleddine (2018) Exploiting Parts-of-Speech for Effective Automated Requirements Traceability. Information and Software Technology . ISSN 09505849 (In Press)

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Official URL: http://dx.doi.org/10.1016/j.infsof.2018.09.009

Abstract

Context: Requirement traceability (RT) is defined as the ability to describe and follow the life of a requirement. RT helps developers ensure that relevant requirements are implemented and that the source code is consistent with its requirement with respect to a set of traceability links called trace links. Previous work leverages Parts Of Speech (POS) tagging of software artifacts to recover trace links among them. These studies work on the premise that discarding one or more POS tags results in an improved accuracy of Information Retrieval (IR) techniques. Objective: First, we show empirically that excluding one or more POS tags could negatively impact the accuracy of existing IR-based traceability approaches, namely the Vector Space Model (VSM) and the Jensen Shannon Model (JSM). Second, we propose a method that improves the accuracy of IR-based traceability approaches. Method: We developed an approach, called ConPOS, to recover trace links using constraint-based pruning. ConPOS uses major POS categories and applies constraints to the recovered trace links for pruning as a filtering process to significantly improve the effectiveness of IR-based techniques. We conducted an experiment to provide evidence that removing POSs does not improve the accuracy of IR techniques. Furthermore, we conducted two empirical studies to evaluate the effectiveness of ConPOS in recovering trace links compared to existing peer RT approaches. Results: The results of the first empirical study show that removing one or more POS negatively impacts the accuracy of VSM and JSM. Furthermore, the results from the other empirical studies show that ConPOS provides 11%-107%, 8%-64%, and 15%-170% higher precision, recall, and mean average precision (MAP) than VSM and JSM. Conclusion: We showed that ConPosout
performs existing IR-based RT approaches that discard some POS tags from the input documents.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Article
Refereed:Yes
Authors:Ali, Nasir and Cai, Haipeng and Hamou-Lhadj, Abdelwahab and Hassine, Jameleddine
Journal or Publication:Information and Software Technology
Date:27 September 2018
Digital Object Identifier (DOI):10.1016/j.infsof.2018.09.009
Keywords:Requirements Traceability (RT); Parts of Speech (POS); Information Retrieval (IR); Trace links
ID Code:984566
Deposited By: MONIQUE LANE
Deposited On:04 Oct 2018 18:32
Last Modified:04 Oct 2018 18:32

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