On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
Combinatorial interaction testing is a practical approach aims to detect defects due to unwanted and faulty interactions. Here, a set of sampled test cases is generated based on t-way covering problem (where t indicates the interaction strength). Often, the generation process is based on a particula...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publishing Network (ARPN)
2015
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/18126/ http://umpir.ump.edu.my/id/eprint/18126/ http://umpir.ump.edu.my/id/eprint/18126/1/04.%20ARPN1015_2825.pdf |
| Summary: | Combinatorial interaction testing is a practical approach aims to detect defects due to unwanted and faulty interactions. Here, a set of sampled test cases is generated based on t-way covering problem (where t indicates the interaction strength). Often, the generation process is based on a particular t-way strategy ensuring that each t-way interaction is covered at least once. Much useful progress has been achieved as plethora of t-way strategies have been developed in the literature in the last 30 years. Recently, in line with the upcoming field called Search based Software Engineering (SBSE), many newly strategies have been developed adopting specific optimization algorithm (e.g. Genetic Algorithm (GA), Ant Colony (AC), Simulated Annealling (SA), Particle Swarm Optimization, and Harmony Search Algorithm (HS) as their basis in an effort to generate the most optimal solution. Although useful, strategies based on the aforementioned optimization algorithms are not without limitation. Specifically, these algorithms require extensive tuning before optimal solution can be obtained. In many cases, improper tuning of specific parameters undesirably yields suboptimal solution. Addressing this issue, this paper proposes the adoption of parameter free optimization algorithms as the basis of future t-way strategies. In doing so, this paper reviews two existing parameter free optimization algorithms involving Teaching Learning Based Optimization (TLBO) and Fruitfly Optimization Algorithm (FOA) in an effort to promote their adoption for CIT. |
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