Software unit test coverage and adequacy
ACM Computing Surveys (CSUR)
Evolving Transformation Sequences using Genetic Algorithms
SCAM '04 Proceedings of the Source Code Analysis and Manipulation, Fourth IEEE International Workshop
Is mutation an appropriate tool for testing experiments?
Proceedings of the 27th international conference on Software engineering
When only random testing will do
Proceedings of the 1st international workshop on Random testing
Automated Unique Input Output Sequence Generation for Conformance Testing of FSMs
The Computer Journal
Applying Evolutionary Computation Methods to Formal Testing and Model Checking
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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The goal of this paper is to provide a method to generate efficient and short test suites for Finite State Machines (FSMs) by means of combining Genetic Algorithms (GAs) techniques and mutation testing. In our framework, mutation testing is used in various ways. First, we use it to produce (faulty) systems for the GAs to learn. Second, it is used to sort the intermediate tests with respect to the number of mutants killed. Finally, it is used to measure the fitness of our tests, therefore allowing to reduce redundancy. We present an experiment to show how our approach outperforms other approaches.