research product . Other ORP type . 2017

mrstudyr: Retrospectively Studying the Effectiveness of Mutant Reduction Techniques

McCurdy, C.J.; McMinn, P.S.; Kapfhammer, G.M.;
Open Access English
  • Published: 16 Jan 2017
  • Publisher: IEEE
  • Country: United Kingdom
Abstract
Mutation testing is a well-known method for measuring\ud a test suite’s quality. However, due to its computational\ud expense and intrinsic difficulties (e.g., detecting equivalent mutants\ud and potentially checking a mutant’s status for each test),\ud mutation testing is often challenging to practically use. To control\ud the computational cost of mutation testing, many reduction\ud strategies have been proposed (e.g., uniform random sampling\ud over mutants). Yet, a stand-alone tool to compare the efficiency\ud and effectiveness of these methods is heretofore unavailable. Since\ud existing mutation testing tools are often complex and languagedependent,\ud thi...
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40 references, page 1 of 3

[1] K. J. Vicente, K. Kada-Bekhaled, G. Hillel, A. Cassano, and B. A. Orser, “Programming errors contribute to death from patient-controlled analgesia: Case report and estimate of probability,” Canadian Journal of Anesthesia, vol. 50, no. 4, 2003.

[2] G. M. Kapfhammer, “Regression testing,” in The Encyclopedia of Software Engineering, 2010.

[3] G. M. Kapfhammer, “Software testing,” in The Computer Science Handbook, 2004.

[4] R. Gopinath, A. Alipour, I. Ahmed, C. Jensen, and A. Groce, “Do mutation reduction strategies matter?” Oregon State University, Technical Report, 2015.

[5] Y. Jia and M. Harman, “An analysis and survey of the development of mutation testing,” Transactions on Software Engineering, vol. 37, no. 5, 2011.

[6] P. Ammann and A. J. Offutt, Introduction to Software Testing. Cambridge University Press, 2008.

[7] R. Just, G. M. Kapfhammer, and F. Schweiggert, “Using conditional mutation to increase the efficiency of mutation analysis,” in Proceedings of the 6th International Workshop on Automation of Software Test, 2011. [OpenAIRE]

[8] R. Gopinath, A. Alipour, I. Ahmed, C. Jensen, and A. Groce, “An empirical comparison of mutant selection approaches,” Oregon State University, Technical Report, 2015.

[9] R. Just, G. M. Kapfhammer, and F. Schweiggert, “Using non-redundant mutation operators and test suite prioritization to achieve efficient and scalable mutation analysis,” in Proceedings of the 23rd International Symposium on Software Reliability Engineering, 2012.

[10] A. J. Offutt, G. Rothermel, and C. Zapf, “An experimental evaluation of selective mutation,” in Proceedings of the 15th International Conference on Software Engineering, 1993.

[11] W. E. Wong and A. P. Mathur, “Reducing the cost of mutation testing: An empirical study,” Journal of Systems and Software, vol. 31, no. 3, 1995.

[12] A. J. Offutt and R. H. Untch, “Mutation 2000: Uniting the orthogonal,” in Mutation Testing for the New Century, 2001. [OpenAIRE]

[13] L. Zhang, S.-S. Hou, J.-J. Hu, T. Xie, and H. Mei, “Is operator-based mutant selection superior to random mutant selection?” in Proceedings of the 32nd International Conference on Software Engineering, 2010.

[14] L. Zhang, M. Gligoric, D. Marinov, and S. Khurshid, “Operator-based and random mutant selection: Better together,” in Proceedings of the 28th International Conference on Automated Software Engineering, 2013.

[15] A. P. Mathur and E. W. Wong, “An empirical comparison of data flow and mutation-based test adequacy criteria,” Software Testing, Verification and Reliability, vol. 4, no. 1, 1994.

40 references, page 1 of 3
Abstract
Mutation testing is a well-known method for measuring\ud a test suite’s quality. However, due to its computational\ud expense and intrinsic difficulties (e.g., detecting equivalent mutants\ud and potentially checking a mutant’s status for each test),\ud mutation testing is often challenging to practically use. To control\ud the computational cost of mutation testing, many reduction\ud strategies have been proposed (e.g., uniform random sampling\ud over mutants). Yet, a stand-alone tool to compare the efficiency\ud and effectiveness of these methods is heretofore unavailable. Since\ud existing mutation testing tools are often complex and languagedependent,\ud thi...
Download from
40 references, page 1 of 3

[1] K. J. Vicente, K. Kada-Bekhaled, G. Hillel, A. Cassano, and B. A. Orser, “Programming errors contribute to death from patient-controlled analgesia: Case report and estimate of probability,” Canadian Journal of Anesthesia, vol. 50, no. 4, 2003.

[2] G. M. Kapfhammer, “Regression testing,” in The Encyclopedia of Software Engineering, 2010.

[3] G. M. Kapfhammer, “Software testing,” in The Computer Science Handbook, 2004.

[4] R. Gopinath, A. Alipour, I. Ahmed, C. Jensen, and A. Groce, “Do mutation reduction strategies matter?” Oregon State University, Technical Report, 2015.

[5] Y. Jia and M. Harman, “An analysis and survey of the development of mutation testing,” Transactions on Software Engineering, vol. 37, no. 5, 2011.

[6] P. Ammann and A. J. Offutt, Introduction to Software Testing. Cambridge University Press, 2008.

[7] R. Just, G. M. Kapfhammer, and F. Schweiggert, “Using conditional mutation to increase the efficiency of mutation analysis,” in Proceedings of the 6th International Workshop on Automation of Software Test, 2011. [OpenAIRE]

[8] R. Gopinath, A. Alipour, I. Ahmed, C. Jensen, and A. Groce, “An empirical comparison of mutant selection approaches,” Oregon State University, Technical Report, 2015.

[9] R. Just, G. M. Kapfhammer, and F. Schweiggert, “Using non-redundant mutation operators and test suite prioritization to achieve efficient and scalable mutation analysis,” in Proceedings of the 23rd International Symposium on Software Reliability Engineering, 2012.

[10] A. J. Offutt, G. Rothermel, and C. Zapf, “An experimental evaluation of selective mutation,” in Proceedings of the 15th International Conference on Software Engineering, 1993.

[11] W. E. Wong and A. P. Mathur, “Reducing the cost of mutation testing: An empirical study,” Journal of Systems and Software, vol. 31, no. 3, 1995.

[12] A. J. Offutt and R. H. Untch, “Mutation 2000: Uniting the orthogonal,” in Mutation Testing for the New Century, 2001. [OpenAIRE]

[13] L. Zhang, S.-S. Hou, J.-J. Hu, T. Xie, and H. Mei, “Is operator-based mutant selection superior to random mutant selection?” in Proceedings of the 32nd International Conference on Software Engineering, 2010.

[14] L. Zhang, M. Gligoric, D. Marinov, and S. Khurshid, “Operator-based and random mutant selection: Better together,” in Proceedings of the 28th International Conference on Automated Software Engineering, 2013.

[15] A. P. Mathur and E. W. Wong, “An empirical comparison of data flow and mutation-based test adequacy criteria,” Software Testing, Verification and Reliability, vol. 4, no. 1, 1994.

40 references, page 1 of 3
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