Paper Info
Title
Finding test data with specific properties via metaheuristic search
Abstract
For software testing to be effective the test data should cover a large and diverse range of the possible input domain. Boltzmann samplers were recently introduced as a systematic method to randomly generate data with a range of sizes from combinatorial classes, and there are a number of automated testing frameworks that serve a similar purpose. However, size is only one of many possible properties that data generated for software testing should exhibit. For the testing of realistic software systems we also need to trade off between multiple different properties or search for specific instances of data that combine several properties. In this paper we propose a general search-based framework for finding test data with specific properties. In particular, we use a metaheuristic, differential evolution, to search for stochastic models for the data generator. Evaluation of the framework demonstrates that it is more general and flexible than existing solutions based on random sampling.
Year
DOI
Venue
2013
10.1109/ISSRE.2013.6698888
Software Reliability Engineering
Keywords
Field
DocType
data structures,evolutionary computation,information retrieval,program testing,Boltzmann samplers,automated testing frameworks,combinatorial classes,data generator,data structures,differential evolution,general search-based framework,metaheuristic search,random sampling,realistic software systems,software testing,test data
Data structure,Data mining,Computer science,Evolutionary computation,All-pairs testing,Software system,Artificial intelligence,Test data,Test data generation,Machine learning,Search-based software engineering,Metaheuristic
Conference
ISSN
Citations 
PageRank 
1071-9458
16
0.89
References 
Authors
11
2
Name
Order
Citations
PageRank
Robert Feldt1965.77
Simon M. Poulding213610.72