GA-based Automatic Test Data Generation for UML State Diagrams with Parallel Paths

C. Doungsa-ard, K. Dahal, A. Hossain, T. Suwannasart

Research output: Chapter in Book/Report/Conference proceedingChapter

12 Citations (Scopus)

Abstract

Automatic test data generation from a software specification prepares test cases for software developers before their code development phase. Having test cases before coding, helps the developers to control their code to conform to the specification. In state-based specifications, paths from the initial state to the final state may be varied, this is called parallel paths. In order to generate test data which cover all behaviors in the specification, test data for each parallel path should be generated. This paper proposes an enhanced genetic algorithm(GA)-based approach to resolve the parallel paths from the UML state machine diagram. The proposed approach is improved from our previous study which uses a GA based test data generation method for only one path. The approach identifies parallel paths to cover all transitions. GA evolves a number of suitable test data sets - one test data set for each parallel path. The best test data for each parallel path are picked and used for calculating the overall coverage test data. The experimental results show improved coverage results with the enhanced approach for a number of case studies with parallel paths.
Original languageEnglish
Title of host publicationADVANCED DESIGN AND MANUFACTURE TO GAIN A COMPETITIVE EDGE
EditorsX.T. Yan, C. Jiang, B. Eynard
PublisherSpringer-Verlag
Pages147-156
ISBN (Print)978-1-84800-240-1
DOIs
Publication statusPublished - 2008

Keywords

  • Test data generation
  • UML state machine diagram
  • Genetic algorithm

Cite this

Doungsa-ard, C., Dahal, K., Hossain, A., & Suwannasart, T. (2008). GA-based Automatic Test Data Generation for UML State Diagrams with Parallel Paths. In X. T. Yan, C. Jiang, & B. Eynard (Eds.), ADVANCED DESIGN AND MANUFACTURE TO GAIN A COMPETITIVE EDGE (pp. 147-156). Springer-Verlag. https://doi.org/10.1007/978-1-84800-241-8_16