DocumentCode :
2302543
Title :
Search-Based Software Testing: Past, Present and Future
Author :
McMinn, Phil
Author_Institution :
Dept. of Comput. Sci., Univ. of Sheffield, Sheffield, UK
fYear :
2011
fDate :
21-25 March 2011
Firstpage :
153
Lastpage :
163
Abstract :
Search-Based Software Testing is the use of a meta-heuristic optimizing search technique, such as a Genetic Algorithm, to automate or partially automate a testing task, for example the automatic generation of test data. Key to the optimization process is a problem-specific fitness function. The role of the fitness function is to guide the search to good solutions from a potentially infinite search space, within a practical time limit. Work on Search-Based Software Testing dates back to 1976, with interest in the area beginning to gather pace in the 1990s. More recently there has been an explosion of the amount of work. This paper reviews past work and the current state of the art, and discusses potential future research areas and open problems that remain in the field.
Keywords :
genetic algorithms; program testing; search problems; automatic test data generation; genetic algorithm; infinite search space; metaheuristic optimizing search technique; optimization process; problem-specific fitness function; search-based software testing; Databases; Genetic algorithms; Optimization; Search problems; Software; Software testing; Search-Based Software Engineering; Search-Based Software Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Software Testing, Verification and Validation Workshops (ICSTW), 2011 IEEE Fourth International Conference on
Conference_Location :
Berlin
Print_ISBN :
978-1-4577-0019-4
Electronic_ISBN :
978-0-7695-4345-1
Type :
conf
DOI :
10.1109/ICSTW.2011.100
Filename :
5954405
Link To Document :
بازگشت