On generating mutants for AspectJ programs
Mutation analysis has been widely used in research studies to evaluate the effectiveness of test suites and testing techniques. Faulty versions (i.e., mutants) of a program are generated such that each mutant contains one seeded fault. The mutation score provides a measure of effectiveness. We study...
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| Published in | Information and software technology Vol. 54; no. 8; pp. 900 - 914 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.08.2012
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0950-5849 1873-6025 |
| DOI | 10.1016/j.infsof.2011.12.001 |
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| Abstract | Mutation analysis has been widely used in research studies to evaluate the effectiveness of test suites and testing techniques. Faulty versions (i.e., mutants) of a program are generated such that each mutant contains one seeded fault. The mutation score provides a measure of effectiveness.
We study three problems with the use of mutation analysis for testing AspectJ programs:•The manual identification and removal of equivalent mutants is difficult and time consuming. We calculate the percentage of equivalent mutants generated for benchmark AspectJ programs using available mutation tools.•The generated mutants need to cover the various fault types described in the literature on fault models for AspectJ programs. We measure the distribution of the mutants generated using available mutation tools with respect to the AspectJ fault types.•We measure the difficulty of killing the generated mutants.
We propose the use of simple analysis of the subject programs to prevent the generation of some equivalent mutants.
We revised existing AspectJ fault models and presented a fault model that removes the problems in existing fault models, such as overlapping between fault types and missing fault types. We also defined three new fault types that occur due to incorrect data-flow interactions occurring in AspectJ programs. We used three mutation tools: AjMutator, Proteum/AJ, and MuJava on three AspectJ programs. To measure the difficulty of killing the mutants created using a mutation operator, we compared the average number of the mutants killed by 10 test suites that satisfy block coverage criterion.
A high percentage of the mutants are equivalent. The mutation tools do not cover all the fault types. Only 4 out of 27 operators generated mutants that were easy to kill.
Our analysis approach removed about 80% of the equivalent mutants. Higher order mutation is needed to cover all the fault types. |
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| AbstractList | Mutation analysis has been widely used in research studies to evaluate the effectiveness of test suites and testing techniques. Faulty versions (i.e., mutants) of a program are generated such that each mutant contains one seeded fault. The mutation score provides a measure of effectiveness.
We study three problems with the use of mutation analysis for testing AspectJ programs:•The manual identification and removal of equivalent mutants is difficult and time consuming. We calculate the percentage of equivalent mutants generated for benchmark AspectJ programs using available mutation tools.•The generated mutants need to cover the various fault types described in the literature on fault models for AspectJ programs. We measure the distribution of the mutants generated using available mutation tools with respect to the AspectJ fault types.•We measure the difficulty of killing the generated mutants.
We propose the use of simple analysis of the subject programs to prevent the generation of some equivalent mutants.
We revised existing AspectJ fault models and presented a fault model that removes the problems in existing fault models, such as overlapping between fault types and missing fault types. We also defined three new fault types that occur due to incorrect data-flow interactions occurring in AspectJ programs. We used three mutation tools: AjMutator, Proteum/AJ, and MuJava on three AspectJ programs. To measure the difficulty of killing the mutants created using a mutation operator, we compared the average number of the mutants killed by 10 test suites that satisfy block coverage criterion.
A high percentage of the mutants are equivalent. The mutation tools do not cover all the fault types. Only 4 out of 27 operators generated mutants that were easy to kill.
Our analysis approach removed about 80% of the equivalent mutants. Higher order mutation is needed to cover all the fault types. Mutation analysis has been widely used in research studies to evaluate the effectiveness of test suites and testing techniques. Faulty versions (i.e., mutants) of a program are generated such that each mutant contains one seeded fault. The mutation score provides a measure of effectiveness. The authors study three problems with the use of mutation analysis for testing AspectJ programs: The manual identification and removal of equivalent mutants is difficult and time consuming. The authors calculate the percentage of equivalent mutants generated for benchmark AspectJ programs using available mutation tools. The generated mutants need to cover the various fault types described in the literature on fault models for AspectJ programs. The authors measure the distribution of the mutants generated using available mutation tools with respect to the AspectJ fault types. The authors measure the difficulty of killing the generated mutants. A high percentage of the mutants are equivalent. The mutation tools do not cover all the fault types. Only 4 out of 27 operators generated mutants that were easy to kill. Mutation analysis has been widely used in research studies to evaluate the effectiveness of test suites and testing techniques. Faulty versions (i.e., mutants) of a program are generated such that each mutant contains one seeded fault. The mutation score provides a measure of effectiveness. We study three problems with the use of mutation analysis for testing AspectJ programs: times The manual identification and removal of equivalent mutants is difficult and time consuming. We calculate the percentage of equivalent mutants generated for benchmark AspectJ programs using available mutation tools. times The generated mutants need to cover the various fault types described in the literature on fault models for AspectJ programs. We measure the distribution of the mutants generated using available mutation tools with respect to the AspectJ fault types. times We measure the difficulty of killing the generated mutants. We propose the use of simple analysis of the subject programs to prevent the generation of some equivalent mutants. We revised existing AspectJ fault models and presented a fault model that removes the problems in existing fault models, such as overlapping between fault types and missing fault types. We also defined three new fault types that occur due to incorrect data-flow interactions occurring in AspectJ programs. We used three mutation tools: AjMutator, Proteum/AJ, and MuJava on three AspectJ programs. To measure the difficulty of killing the mutants created using a mutation operator, we compared the average number of the mutants killed by 10 test suites that satisfy block coverage criterion. A high percentage of the mutants are equivalent. The mutation tools do not cover all the fault types. Only 4 out of 27 operators generated mutants that were easy to kill. Our analysis approach removed about 80% of the equivalent mutants. Higher order mutation is needed to cover all the fault types. |
| Author | Ghosh, Sudipto Wedyan, Fadi |
| Author_xml | – sequence: 1 givenname: Fadi surname: Wedyan fullname: Wedyan, Fadi organization: Department of Software Engineering, Hashemite University, Zarka, Jordan – sequence: 2 givenname: Sudipto surname: Ghosh fullname: Ghosh, Sudipto email: ghosh@cs.colostate.edu organization: Department of Computer Science, Colorado State University, Fort Collins, CO, USA |
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| CitedBy_id | crossref_primary_10_1016_j_infsof_2014_05_020 crossref_primary_10_1016_j_jss_2019_04_031 crossref_primary_10_1016_j_infsof_2016_04_007 crossref_primary_10_1016_j_infsof_2020_106426 crossref_primary_10_3390_info13070327 crossref_primary_10_1016_j_infsof_2016_01_016 crossref_primary_10_1016_j_infsof_2014_11_008 crossref_primary_10_1016_j_jss_2019_07_100 crossref_primary_10_1186_s13173_015_0040_1 |
| Cites_doi | 10.1109/TSE.1984.5010301 10.1002/(SICI)1099-1689(199709)7:3<165::AID-STVR143>3.0.CO;2-U 10.1109/ICST.2008.37 10.1002/stvr.226 10.1109/ICSTW.2009.41 10.1002/stvr.4370040303 10.1016/0164-1212(94)00098-0 10.1145/1368088.1368136 10.1002/stvr.308 10.1109/TSE.2006.83 10.1145/1062455.1062530 10.1145/1808266.1808274 10.1049/iet-sen.2008.0038 10.1109/ICSE.2007.37 10.1109/COMPSAC.2007.159 10.1142/S0218194009004313 10.1109/ICST.2010.30 10.21236/ADA071795 10.1109/SCAM.2008.36 10.1145/1146374.1146380 10.1007/BF00625279 10.1109/HASE.2010.13 10.1109/C-M.1978.218136 10.1007/978-1-4757-5939-6_4 10.1002/(SICI)1099-1689(199912)9:4<233::AID-STVR191>3.0.CO;2-3 10.1109/ISSRE.2006.6 |
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| Keywords | Aspect-oriented programming Mutation testing Fault models AspectJ Test generation High order mutation |
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| SubjectTerms | Aspect-oriented programming AspectJ Blocking Computer programming Computer programs Effectiveness Equivalence Fault models Faults High order mutation Killing Mathematical models Mutation Mutation testing Mutations Operators Studies Systems development Test generation |
| Title | On generating mutants for AspectJ programs |
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