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2019 | OriginalPaper | Chapter

Evaluation of the Prediction-Based Approach to Cost Reduction in Mutation Testing

Authors : Joanna Strug, Barbara Strug

Published in: Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018

Publisher: Springer International Publishing

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Abstract

Mutation testing is the most effective technique for assessing the quality of test suites, but it is also very expensive in terms of computational costs. The cost arises from the need to generate and execute a large number of so called mutants. The paper presents and evaluates a machine learning approach to dealing with the issue of limiting the number of executed mutants. The approach uses classification algorithm to predict mutants execution results for a subset of the generated mutants without their execution. The evaluation of the approach takes into consideration two aspects: accuracy of the predicted results and stability of prediction. In the paper the details of the evaluation experiment and its results are presented and discussed. The approach is tested on four examples having different number of mutants ranging from 90 to over 300. The obtained results indicate that the predicted value of the mutation score is consistently higher then the actual one thus allowing for using the results with high confidence.

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previous chapter Computational Investigation of Probabilistic Learning Task with Use of Machine Learning
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Metadata
Title
Evaluation of the Prediction-Based Approach to Cost Reduction in Mutation Testing
Authors
Joanna Strug
Barbara Strug
Copyright Year
2019
Book
Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018

Print ISBN: 978-3-319-99995-1

Electronic ISBN: 978-3-319-99996-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-99996-8_31

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