TY  - JOUR
EP  - 199
PB  - Springer Verlag
SN  - 21945357
N1  - cited By 4; Conference of 8th Computer Science On-line Conference, CSOC 2019 ; Conference Date: 24 April 2019 Through 27 April 2019; Conference Code:225859
TI  - Hybrid Artificial Bee Colony Algorithm for t-Way Interaction Test Suite Generation
SP  - 192
AV  - none
JF  - Advances in Intelligent Systems and Computing
A1  - Alazzawi, A.K.
A1  - Rais, H.M.
A1  - Basri, S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065868448&doi=10.1007%2f978-3-030-19807-7_19&partnerID=40&md5=d6f564165203649a56e7e2a9d6ed2511
VL  - 984
Y1  - 2019///
N2  - The very large number of test cases and time consumption for a test, it is becoming hard to perform exhaustive testing for any software fault detection. For this reason, combinatorial testing (CT) also known as t-way testing, is one of the well-known methods that are used for fault detections to many software systems. Various existing research works are available in the literature to minimize the number of test cases, and the time to obtain an optimal test suite or competitive test suite. However, the interaction strength of the existing research works are supports up to t = 2 or t = 3, where t is the strength of parameterâ??s interaction. The major purpose of this research is to suggest a new t-way strategy to minimize the test cases. This is called hybrid artificial bee colony (HABC) strategy, which is based on hybridize of an artificial bee colony (ABC) algorithm with a particle swarm optimization (PSO) algorithm. This is to provide a high-interaction strength combinatorial test suite up to t = 6. From experimental results, HABC strategy performed best when compared with existing methods in terms of generating the optimum test case. © 2019, Springer Nature Switzerland AG.
ID  - scholars12170
KW  - Computer testing; Fault detection; Particle swarm optimization (PSO); Well testing
KW  -  Artificial bee colonies; Artificial bee colony algorithms; Artificial bee colony algorithms (ABC); Combinatorial testing; Meta heuristics; Optimization algorithms; Software fault detection; T-way testing
KW  -  Software testing
ER  -