A Multi-Objective Approach to Search-Based Test Data Generation

by Mark Harman, Kiran Lakhotia, and Phil McMinn

Genetic and Evolutionary Computation Conference (GECCO 2007)



There has been a considerable body of work on search-based test data generation for branch coverage. However, hitherto, there has been no work on multi-objective branch coverage. In many scenarios a single-objective formulation is unrealistic; testers will want to find test sets that meet several objectives simultaneously in order to maximize the value obtained from the inherently expensive process of running the test cases and examining the output they produce. This paper introduces multi-objective branch coverage.The paper presents results from a case study of the twin objectives of branch coverage and dynamic memory consumption for both real and synthetic programs. ... [more]


Reference

Mark Harman, Kiran Lakhotia, and Phil McMinn. A Multi-Objective Approach to Search-Based Test Data Generation. Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 1098–1105, 2007


Bibtex Entry
@inproceedings{Harman2007,
  author    = "Harman, Mark and Lakhotia, Kiran and McMinn, Phil",
  title     = "A Multi-Objective Approach to Search-Based Test Data Generation",
  booktitle = "Genetic and Evolutionary Computation Conference (GECCO 2007)",
  pages     = "1098--1105",
  year      = "2007",
  publisher = "ACM"
}