Paper Info
Title
Batch-Optimistic Test-Cases Generation Using Genetic Algorithms
Abstract
This paper proposes a dynamic software testing framework, which is able to analyse the source code of a program, create the necessary data structures for automatic testing, such as control flow graphs, and generate a near to optimum set of test cases with reference to a test coverage criterion. The framework consists of two sub-systems: The first is a program analysis system that identifies the type of statements and the complexity of conditions, performs analysis of variables, extracts code paths and creates the control flow graph (CFG) of the program under testing. The second is a test system that uses the CFG for automatically generating test data based on evolutionary computing. The latter system utilises a specially designed genetic algorithm to produce the set of test cases satisfying the selected coverage criterion. The efficacy and performance of the proposed testing approach is assessed and validated using a variety of sample programs.
Year
DOI
Venue
2007
10.1109/ICTAI.2007.64
ICTAI (1)
Keywords
Field
DocType
genetic algorithms,test system,latter system,program analysis system,test data,batch-optimistic test-cases generation,control flow graph,dynamic software testing framework,test coverage criterion,proposed testing approach,automatic testing,test case,data structure,software testing,source coding,evolutionary computing,test coverage,genetic algorithm,source code,satisfiability,program analysis
Test suite,Code coverage,Data mining,Test Management Approach,Computer science,White-box testing,Artificial intelligence,Data-driven testing,Basis path testing,Algorithm,Test data,Test case,Machine learning
Conference
ISSN
ISBN
Citations 
1082-3409
0-7695-3015-X
2
PageRank 
References 
Authors
0.37
13
2
Name
Order
Citations
PageRank
Anastasis A. Sofokleous1658.90
Andreas S. Andreou221636.65