Volume 8, Issue 1, June 2019, Page: 1-7
A Feature Based Methodology for Variable Requirements Reverse Engineering
Anas Alhamwieh, Faculty of Information Technology, Research Laboratory on Bio-inspired Software Engineering, Philadelphia University, Amman, Jordan
Said Ghoul, Faculty of Information Technology, Research Laboratory on Bio-inspired Software Engineering, Philadelphia University, Amman, Jordan
Received: Feb. 12, 2019;       Accepted: Mar. 18, 2019;       Published: Apr. 3, 2019
DOI: 10.11648/j.ajsea.20190801.11      View  26      Downloads  12
In the past years, software reverse engineering dealt with source code understanding. Nowadays, it is levered to software requirements abstract level, supported by feature model notations, language independent, and simpler than the source code reading. The recent relevant approaches face the following insufficiencies: lack of a complete integrated methodology, adapted feature model, feature patterns recognition, and Graph based slicing. This work aims to provide some solutions to the above challenges through an integrated methodology. The following results are unique. Elementary and configuration features are specified in a uniform way by introducing semantics specific attributes. The reverse engineering supports feature pattern recognition and requirements feature model graph-based slicing. The slicing criteria are rich enough to allow answering questions of software requirements maintainers. A comparison of this proposed methodology, based on effective criteria, with the similar works, seems to be valuable and competitive: the enrichment of the feature model and feature pattern recognition were never approached and the proposed slicing technique is more general, effective, and practical.
Requirements Engineering, Reverse Engineering, Requirements Variability, Feature Model, Pattern Recognition, Graph-Based Slicing
To cite this article
Anas Alhamwieh, Said Ghoul, A Feature Based Methodology for Variable Requirements Reverse Engineering, American Journal of Software Engineering and Applications. Vol. 8, No. 1, 2019, pp. 1-7. doi: 10.11648/j.ajsea.20190801.11
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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