Universiti Teknologi Malaysia Institutional Repository

Enhancing similarity distances using mandatory and optional forearly fault detection

Abd. Razak, Safwan and Isa, Mohd. Adham and Jawawi, Dayang N. A. (2018) Enhancing similarity distances using mandatory and optional forearly fault detection. Indonesian Journal of Electrical Engineering and Computer Science, 11 (3). pp. 1194-1203. ISSN 2502-4752

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Official URL: http://ijeecs.iaescore.com/index.php/IJEECS/articl...

Abstract

Software Product Line (SPL) describes procedures, techniques, and tools in software engineering by using a common method of production for producing a group of software systems that identical from a shared set of software assets. In SPL, the similarity-based prioritization can resemble combinatorial interaction testing in scalable and efficient way by choosing and prioritize configurations that most dissimilar. However, the similarity distances in SPL still not so much cover the basic detail of feature models which are the notations. Plus, the configurations always have been prioritized based on domain knowledge but not much attention has been paid to feature model notations. In this paper, we proposed the usage of mandatory and optional notations for similarity distances. The objective is to improve the average percentage of faults detected (APFD). We investigate four different distances and make modifications on the distances to increase APFD value. These modifications are the inclusion of mandatory and optional notations with the similarity distances. The results are the APFD values for all the similarity distances including the original and modified similarity distances. Overall, the results shown that by subtracting the optional notation value can increase the APFD by 3.71% from the original similarity distance.

Item Type:Article
Uncontrolled Keywords:average percentage of faults detected (APFD), prioritization, similarity distances algorithms, software product lines
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:84557
Deposited By: Yanti Mohd Shah
Deposited On:27 Feb 2020 03:05
Last Modified:27 Feb 2020 03:05

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