TY  - CONF
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781538647431
Y1  - 2018///
A1  - Alazzawi, A.K.
A1  - Md Rais, H.
A1  - Basri, S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057113273&doi=10.1109%2fICCOINS.2018.8510601&partnerID=40&md5=3ceb8e7e890edbe079960ea595065f69
AV  - none
TI  - Artificial Bee Colony Algorithm for t-Way Test Suite Generation
ID  - scholars9856
KW  - Software testing
KW  -  Artificial bee colonies; Artificial bee colony algorithms; Artificial bee colony algorithms (ABC); Exhaustive testing; Meta heuristics; Optimization algorithms; Optimization problems; T-way testing
KW  -  Optimization
N2  - Exhaustive testing is a very strenuous undertaking due to its numerous combinations. Consequently, searching for and sampling an optimal test suite from viable groups of test cases (TC) has turned out to be a central concern. To tackle this matter, the use of t-way testing (Where t represents the strength of the interaction) has become well known. In order to facilitate future development and summarize the realization so far, the major objective of this paper is to portray an important comparison of the introduced optimization algorithms (OA) as a base of the t-way test suite generation strategy, and to suggest a new strategy focusing on the Artificial Bee Colony (ABC) Algorithm, known as Artificial Bee Colony Strategy (ABCS). The experimental results showed that ABCS can compete against the existing (both AI-based and computational-based) strategies in terms of generating the optimum test case. © 2018 IEEE.
N1  - cited By 15; Conference of 4th International Conference on Computer and Information Sciences, ICCOINS 2018 ; Conference Date: 13 August 2018 Through 14 August 2018; Conference Code:141665
ER  -