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2014 (Vol. 7, Issue: 2)
Article Information:

Using a Rule-based Method for Detecting Anomalies in Software Product Line

Abdelrahman Osman Elfaki, Sim Liew Fong, P. Vijayaprasad, Md Gapar Md Johar and Murad Saadi Fadhil
Corresponding Author:  Abdelrahman Osman Elfaki 

Key words:  Domain engineering, software product line, variability, , , ,
Vol. 7 , (2): 275-281
Submitted Accepted Published
March 29, 2013 April 22, 2013 January 10, 2014
Abstract:

This study proposes a rule based method for detecting anomalies in SPL. By anomalies we mean false-optional features and wrong cardinality. Software Product Line (SPL) is an emerging methodology for software products development. Successful software product is highly dependent on the validity of a SPL. Therefore, validation is a significant process within SPL. Anomalies are well known problems in SPL. Anomiles in SPL means dead feature, redundancy, wrong-cardinality and false-option features. In the literature, the problem of false-option features and wrong cardinality did not take the signs of attentions as a dead feature and redundancy problems. The maturity of the SPL can be enhanced by detecting and removing the false-option features. Wrong cardinality can cause problems in developing software application by preventing configuration of variants from their variation points. The contributions of this study are First Order Logic (FOL) rules for deducing false-option features and wrong-cardinality. Moreover, we provide a new classification of the wrong cardinality. As a result, all cases of false-option features and wrong variability in the domain-engineering process are defined. Finally, experiments are conducted to prove the scalability of the proposed method.
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  Cite this Reference:
Abdelrahman Osman Elfaki, Sim Liew Fong, P. Vijayaprasad, Md Gapar Md Johar and Murad Saadi Fadhil, 2014. Using a Rule-based Method for Detecting Anomalies in Software Product Line.  Research Journal of Applied Sciences, Engineering and Technology, 7(2): 275-281.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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