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CHOMK: Concurrent Higher-Order Mutants Killing Using Genetic Algorithm.

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Title: CHOMK: Concurrent Higher-Order Mutants Killing Using Genetic Algorithm.
Authors: Ghiduk, Ahmed S.1,2 asaghiduk@tu.edu.sa, El-Zoghdy, S. F.3 elzoghdy@yahoo.com
Source: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Dec2018, Vol. 43 Issue 12, p7907-7922. 16p.
Subjects: Mutation testing of computer software, Genetic algorithms, Combinatorial optimization
Abstract: Higher-order subtle mutants are faults that are hard to detect or kill by the existing test set used for killing all the first-order mutants of the given program. Recently, some techniques have been proposed to construct higher-order subtle concurrency mutants that are not represented by the first-order mutants. To the best of our knowledge, there is no test-input generation technique proposed to kill this type of mutants. This paper proposes a search-based technique for generating a set of test inputs to kill higher-order subtle concurrency mutants. The proposed technique utilizes genetic algorithms in generating the set of test inputs. The performance of the proposed technique is evaluated and compared with that of the random-based test-data generation technique. The obtained results demonstrate the effectiveness of the proposed technique as it outperforms the random technique in terms of the killing ratio for the generated set of subtle concurrency mutants and the size of test suite. In the range of tested set of subtle concurrency mutants, the proposed technique approximately killed 91.4% of all mutants using 79 test cases compared to 82.8% using 128 test cases for the random technique. [ABSTRACT FROM AUTHOR]
Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Higher-order subtle mutants are faults that are hard to detect or kill by the existing test set used for killing all the first-order mutants of the given program. Recently, some techniques have been proposed to construct higher-order subtle concurrency mutants that are not represented by the first-order mutants. To the best of our knowledge, there is no test-input generation technique proposed to kill this type of mutants. This paper proposes a search-based technique for generating a set of test inputs to kill higher-order subtle concurrency mutants. The proposed technique utilizes genetic algorithms in generating the set of test inputs. The performance of the proposed technique is evaluated and compared with that of the random-based test-data generation technique. The obtained results demonstrate the effectiveness of the proposed technique as it outperforms the random technique in terms of the killing ratio for the generated set of subtle concurrency mutants and the size of test suite. In the range of tested set of subtle concurrency mutants, the proposed technique approximately killed 91.4% of all mutants using 79 test cases compared to 82.8% using 128 test cases for the random technique. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s13369-018-3226-y
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        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Combinatorial optimization
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              Text: Dec2018
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