Genetic Algorithm-based Test Generation for Software Product Line with the Integration of Fault Localization Techniques

  • Xuelin Li
  • W. Eric Wong
  • Ruizhi Gao
  • Linghuan Hu
  • Shigeru Hosono
Article

DOI: 10.1007/s10664-016-9494-9

Cite this article as:
Li, X., Wong, W.E., Gao, R. et al. Empir Software Eng (2017). doi:10.1007/s10664-016-9494-9
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Abstract

In response to the highly competitive market and the pressure to cost-effectively release good-quality software, companies have adopted the concept of software product line to reduce development cost. However, testing and debugging of each product, even from the same family, is still done independently. This can be very expensive. To solve this problem, we need to explore how test cases generated for one product can be used for another product. We propose a genetic algorithm-based framework which integrates software fault localization techniques and focuses on reusing test specifications and input values whenever feasible. Case studies using four software product lines and eight fault localization techniques were conducted to demonstrate the effectiveness of our framework. Discussions on factors that may affect the effectiveness of the proposed framework is also presented. Our results indicate that test cases generated in such a way can be easily reused (with appropriate conversion) between different products of the same family and help reduce the overall testing and debugging cost.

Keywords

Software product line Genetic algorithm Test generation Debugging/fault localization Coverage EXAM score 

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Texas at DallasRichardsonUSA
  2. 2.Service Business Development DivisionNEC CorporationTokyoJapan

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