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Is operator-based mutant selection superior to random mutant selection?

Published: 01 May 2010 Publication History

Abstract

Due to the expensiveness of compiling and executing a large number of mutants, it is usually necessary to select a subset of mutants to substitute the whole set of generated mutants in mutation testing and analysis. Most existing research on mutant selection focused on operator-based mutant selection, i.e., determining a set of sufficient mutation operators and selecting mutants generated with only this set of mutation operators. Recently, researchers began to leverage statistical analysis to determine sufficient mutation operators using execution information of mutants. However, whether mutants selected with these sophisticated techniques are superior to randomly selected mutants remains an open question. In this paper, we empirically investigate this open question by comparing three representative operator-based mutant-selection techniques with two random techniques. Our empirical results show that operator-based mutant selection is not superior to random mutant selection. These results also indicate that random mutant selection can be a better choice and mutant selection on the basis of individual mutants is worthy of further investigation.

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cover image ACM Conferences
ICSE '10: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
May 2010
627 pages
ISBN:9781605587196
DOI:10.1145/1806799
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 May 2010

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  1. mutation testing
  2. test-adequacy criterion

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Cited By

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  • (2024)Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMsProceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI10.1145/3644815.3644946(150-159)Online publication date: 14-Apr-2024
  • (2024)Test Data Generation for Mutation Testing Based on Markov Chain Usage Model and Estimation of Distribution AlgorithmIEEE Transactions on Software Engineering10.1109/TSE.2024.335829750:3(551-573)Online publication date: Mar-2024
  • (2024)Efficient software mutation test by clustering the single-line redundant mutantsData Technologies and Applications10.1108/DTA-05-2023-015258:5(807-837)Online publication date: 25-Apr-2024
  • (2024)Set evolution based test data generation for killing stubborn mutantsJournal of Systems and Software10.1016/j.jss.2024.112121216(112121)Online publication date: Oct-2024
  • (2024)Enhancing Software Quality Assurance in Ubiquitous Learning Environments through Mutation Testing and Diverse Test OraclesComputers in Human Behavior10.1016/j.chb.2024.108493(108493)Online publication date: Nov-2024
  • (2024)A Cost-effective and Machine-learning-based method to identify and cluster redundant mutants in software mutation testingThe Journal of Supercomputing10.1007/s11227-024-06107-880:12(16711-16743)Online publication date: 16-Apr-2024
  • (2024)Effective Software Mutation-Test Using Program Instructions ClassificationJournal of Electronic Testing10.1007/s10836-023-06089-039:5-6(631-657)Online publication date: 9-Jan-2024
  • (2023)Enabling Efficient Assertion Inference2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE59848.2023.00039(623-634)Online publication date: 9-Oct-2023
  • (2023)Mutant Selection Strategies in Mutation Testing2023 International Conference on Code Quality (ICCQ)10.1109/ICCQ57276.2023.10114663(1-15)Online publication date: 22-Apr-2023
  • (2023)SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC57700.2023.00116(870-875)Online publication date: Jun-2023
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