Reasoning with worlds and truth maintenance in a knowledge-based programming environment
Communications of the ACM
The category-partition method for specifying and generating fuctional tests
Communications of the ACM
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
An empirical study of regression test selection techniques
ACM Transactions on Software Engineering and Methodology (TOSEM)
Incorporating varying test costs and fault severities into test case prioritization
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Prioritizing Test Cases For Regression Testing
IEEE Transactions on Software Engineering
Test Case Prioritization: A Family of Empirical Studies
IEEE Transactions on Software Engineering
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Expert Systems
Fuzzy Sets Engineering
Effectively prioritizing tests in development environment
ISSTA '02 Proceedings of the 2002 ACM SIGSOFT international symposium on Software testing and analysis
Accuracy of software quality models over multiple releases
Annals of Software Engineering
Application of a Usage Profile in Software Quality Models
CSMR '99 Proceedings of the Third European Conference on Software Maintenance and Reengineering
Using Component Metacontent to Support the Regression Testing of Component-Based Software
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Fuzzy logic techniques for software reliability engineering
Fuzzy logic techniques for software reliability engineering
Putting Your Best Tests Forward
IEEE Software
The data mining approach to automated software testing
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
In regression testing selection when source code is not available
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
On the Use of Mutation Faults in Empirical Assessments of Test Case Prioritization Techniques
IEEE Transactions on Software Engineering
Is there a need for fuzzy logic?
Information Sciences: an International Journal
A model of computation and representation in the brain
Information Sciences: an International Journal
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An effective system testing activity for consecutive releases of very large software systems depends considerably on the selection of test cases for execution. From a practical point of view, it is not feasible to run all possible test cases because of the real-world constraints of limited time, human and monetary resources. While existing techniques for test case selection provide methods to limit the growth of the number of test cases with software evolution, many of them are based on the assumption that source code analysis is available. Very few works have explored the problem of test case selection given the constraint that source code analysis is not available. We propose fuzzy expert systems as an effective solution to the problem. Fuzzy expert systems have the ability to emulate fuzzy human reasoning and judgment processes. The proposed fuzzy expert system identifies potentially critical test cases for system test by correlating knowledge represented by one or more of the following: customer profile, analysis of past test case results, system failure rate, and change in system architecture. We piloted this fuzzy expert system in a large telecommunications system and the results show that test effectiveness and efficiency is significantly improved.