A novel swarm intelligence based strategy to generate optimum test data in T-way testing

Khandakar Rabbi; Quazi Mamun; Md Rafiqul Islam

doi:10.1007/978-3-319-67071-3_31

A novel swarm intelligence based strategy to generate optimum test data in T-way testing

Khandakar Rabbi, Quazi Mamun, Md Rafiqul Islam

Research output: Book chapter/Published conference paper › Conference paper › peer-review

2 Citations (Scopus)

Abstract

The limitation of resources and the deadline of software and hardware projects inhibits the exhaustive testing of a system. The most effective way to overcome this problem is to generation of optimal test suite. Heuristic searches are used to optimize the test suite since 1992. Recently, the interest and activities is increasing in this area. In theory, the changes to the parameter interaction (the t) can significantly reduce the number data in the test suite. Using this principle many scientists and practitioners created some effective test suite generation strategies. The implementation of heuristic search in the generation of optimum and minimum test suite is the most effective. However, producing the optimum test data is a NP-hard problem (Non-deterministic polynomial). Thus, it is impossible for any strategy that can produce the optimum test suite in any circumstance. This paper represents a novel swarm intelligent based searching strategy (mSITG) to generate optimum test suite. The performances of the mSITG are analyzed and compared with other well-known strategies. Empirical result shows that the proposed strategy is highly acceptable in terms of the test data size.

Original language	English
Title of host publication	International Conference on Applications and Techniques in Cyber Security and Intelligence - Applications and Techniques in Cyber Security and Intelligence
Editors	Jemal Abawajy, Kim-Kwang Raymond Choo, Rafiqul Islam
Publisher	Springer-Verlag London Ltd.
Pages	247-255
Number of pages	9
Volume	580
ISBN (Electronic)	9783319670713
ISBN (Print)	9783319670706
DOIs	https://doi.org/10.1007/978-3-319-67071-3_31
Publication status	Published - 01 Jan 2018
Event	International Conference on Applications and Techniques in Cyber Security and Intelligence: ATCSI 2017 - Zhejiang Business Technology Institute, Ningbo, China Duration: 16 Jun 2017 → 18 Jun 2017 http://aibd.us/ (Conference website) https://www.springer.com/us/book/9783319670706 (Conference proceedings)

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	580
ISSN (Print)	2194-5357

Conference

Conference	International Conference on Applications and Techniques in Cyber Security and Intelligence
Country	China
City	Ningbo
Period	16/06/17 → 18/06/17
Other	The 2017 International Conference on Applications and Techniques in Cyber Intelligence (ATCI), building on the previous successes in Guangzhou, China (2016), Dallas, USA (2015), Beijing, China (2014), and Sydney, Australia (2013), is proud to be in the 5th consecutive conference year. Previously, the event is known as the International Workshop on Applications and Techniques in Cyber Security (ATCS 2016), held in conjunction with the International Conference on Security and Privacy in Communication Networks (SecureComm). The 2017 International Conference on Applications and Techniques in Cyber Security and Intelligence focuses on all aspects on techniques and applications in cyber and electronics security and intelligence research. The purpose of ATCI 2017 is to provide a forum for presentation and discussion of innovative ideas, cutting edge research results, and novel techniques, methods and applications on all aspects of cyber and electronics security and intelligence.
Internet address	http://aibd.us/ (Conference website) https://www.springer.com/us/book/9783319670706 (Conference proceedings)

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Rabbi, K., Mamun, Q., & Islam, M. R. (2018). A novel swarm intelligence based strategy to generate optimum test data in T-way testing. In J. Abawajy, K-K. R. Choo, & R. Islam (Eds.), International Conference on Applications and Techniques in Cyber Security and Intelligence - Applications and Techniques in Cyber Security and Intelligence (Vol. 580, pp. 247-255). (Advances in Intelligent Systems and Computing; Vol. 580). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-319-67071-3_31

Rabbi, Khandakar ; Mamun, Quazi ; Islam, Md Rafiqul. / A novel swarm intelligence based strategy to generate optimum test data in T-way testing. International Conference on Applications and Techniques in Cyber Security and Intelligence - Applications and Techniques in Cyber Security and Intelligence. editor / Jemal Abawajy ; Kim-Kwang Raymond Choo ; Rafiqul Islam. Vol. 580 Springer-Verlag London Ltd., 2018. pp. 247-255 (Advances in Intelligent Systems and Computing).

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abstract = "The limitation of resources and the deadline of software and hardware projects inhibits the exhaustive testing of a system. The most effective way to overcome this problem is to generation of optimal test suite. Heuristic searches are used to optimize the test suite since 1992. Recently, the interest and activities is increasing in this area. In theory, the changes to the parameter interaction (the t) can significantly reduce the number data in the test suite. Using this principle many scientists and practitioners created some effective test suite generation strategies. The implementation of heuristic search in the generation of optimum and minimum test suite is the most effective. However, producing the optimum test data is a NP-hard problem (Non-deterministic polynomial). Thus, it is impossible for any strategy that can produce the optimum test suite in any circumstance. This paper represents a novel swarm intelligent based searching strategy (mSITG) to generate optimum test suite. The performances of the mSITG are analyzed and compared with other well-known strategies. Empirical result shows that the proposed strategy is highly acceptable in terms of the test data size.",

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Rabbi, K, Mamun, Q & Islam, MR 2018, A novel swarm intelligence based strategy to generate optimum test data in T-way testing. in J Abawajy, K-KR Choo & R Islam (eds), International Conference on Applications and Techniques in Cyber Security and Intelligence - Applications and Techniques in Cyber Security and Intelligence. vol. 580, Advances in Intelligent Systems and Computing, vol. 580, Springer-Verlag London Ltd., pp. 247-255, International Conference on Applications and Techniques in Cyber Security and Intelligence, Ningbo, China, 16/06/17. https://doi.org/10.1007/978-3-319-67071-3_31

A novel swarm intelligence based strategy to generate optimum test data in T-way testing. / Rabbi, Khandakar; Mamun, Quazi ; Islam, Md Rafiqul.

International Conference on Applications and Techniques in Cyber Security and Intelligence - Applications and Techniques in Cyber Security and Intelligence. ed. / Jemal Abawajy; Kim-Kwang Raymond Choo; Rafiqul Islam. Vol. 580 Springer-Verlag London Ltd., 2018. p. 247-255 (Advances in Intelligent Systems and Computing; Vol. 580).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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AU - Rabbi, Khandakar

AU - Mamun, Quazi

AU - Islam, Md Rafiqul

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The limitation of resources and the deadline of software and hardware projects inhibits the exhaustive testing of a system. The most effective way to overcome this problem is to generation of optimal test suite. Heuristic searches are used to optimize the test suite since 1992. Recently, the interest and activities is increasing in this area. In theory, the changes to the parameter interaction (the t) can significantly reduce the number data in the test suite. Using this principle many scientists and practitioners created some effective test suite generation strategies. The implementation of heuristic search in the generation of optimum and minimum test suite is the most effective. However, producing the optimum test data is a NP-hard problem (Non-deterministic polynomial). Thus, it is impossible for any strategy that can produce the optimum test suite in any circumstance. This paper represents a novel swarm intelligent based searching strategy (mSITG) to generate optimum test suite. The performances of the mSITG are analyzed and compared with other well-known strategies. Empirical result shows that the proposed strategy is highly acceptable in terms of the test data size.

AB - The limitation of resources and the deadline of software and hardware projects inhibits the exhaustive testing of a system. The most effective way to overcome this problem is to generation of optimal test suite. Heuristic searches are used to optimize the test suite since 1992. Recently, the interest and activities is increasing in this area. In theory, the changes to the parameter interaction (the t) can significantly reduce the number data in the test suite. Using this principle many scientists and practitioners created some effective test suite generation strategies. The implementation of heuristic search in the generation of optimum and minimum test suite is the most effective. However, producing the optimum test data is a NP-hard problem (Non-deterministic polynomial). Thus, it is impossible for any strategy that can produce the optimum test suite in any circumstance. This paper represents a novel swarm intelligent based searching strategy (mSITG) to generate optimum test suite. The performances of the mSITG are analyzed and compared with other well-known strategies. Empirical result shows that the proposed strategy is highly acceptable in terms of the test data size.

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KW - Software testing

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KW - Test case generation

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Rabbi K, Mamun Q , Islam MR. A novel swarm intelligence based strategy to generate optimum test data in T-way testing. In Abawajy J, Choo K-KR, Islam R, editors, International Conference on Applications and Techniques in Cyber Security and Intelligence - Applications and Techniques in Cyber Security and Intelligence. Vol. 580. Springer-Verlag London Ltd. 2018. p. 247-255. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-67071-3_31

A novel swarm intelligence based strategy to generate optimum test data in T-way testing

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