Paper Info
Title
A Comparative Study of Artificial Neural Networks and Info-Fuzzy Networks as Automated Oracles in Software Testing
Abstract
Software quality is one of the main concerns of software users. Hence, software testing is an utterly important phase in the software development life cycle. Nevertheless, manual evaluation of program compliance with its specification may be prohibitively time consuming. As a remedy, several software testing systems are using an automatic oracle to confirm that the developed software complies with its specification and determine whether a given test case exposes faults. The use of artificial neural networks and info-fuzzy networks as automated oracles has been explored elsewhere. Nevertheless, there is not enough research comparing these two popular approaches to automated evaluation of the test outcome. This paper fills the gap and reports on a set of experiments designed to compare the two methods based on ROC curves, training time, and dispersion analysis.
Year
DOI
Venue
2012
10.1109/TSMCA.2012.2183590
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions
Keywords
Field
DocType
learning (artificial intelligence),neural nets,program testing,software quality,ROC curves,artificial neural networks,automated oracles,dispersion analysis,info-fuzzy networks,software development life cycle,software quality,software testing systems,training time,Black-box testing,clustering techniques,dispersion analysis,info-fuzzy networks (IFNs),neural networks,software testing
System integration testing,Computer science,Regression testing,Software reliability testing,Artificial intelligence,Software metric,Software construction,Software quality,Software verification and validation,Software sizing,Machine learning
Journal
Volume
Issue
ISSN
42
5
1083-4427
Citations 
PageRank 
References 
1
0.35
15
Authors
4
Name
Order
Citations
PageRank
Deepam Agarwal110.35
Dan E. Tamir27913.26
Mark Last358652.69
Abraham Kandel42145276.03