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Enhancing Similarity Distances Using Mandatory and Optional for Early Fault Detection
- Source :
- Indonesian Journal of Electrical Engineering and Computer Science. 11:1194
- Publication Year :
- 2018
- Publisher :
- Institute of Advanced Engineering and Science, 2018.
-
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.
- Subjects :
- Control and Optimization
Computer Networks and Communications
Computer science
computer.software_genre
Feature model
Fault detection and isolation
Set (abstract data type)
Similarity (network science)
Hardware and Architecture
Signal Processing
Feature (machine learning)
Domain knowledge
Software system
Data mining
Electrical and Electronic Engineering
Software product line
computer
Information Systems
Subjects
Details
- ISSN :
- 25024760 and 25024752
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Indonesian Journal of Electrical Engineering and Computer Science
- Accession number :
- edsair.doi...........300a12e99c29b2d0744be93039536f9e
- Full Text :
- https://doi.org/10.11591/ijeecs.v11.i3.pp1194-1203