Comparative Analysis of Neighborhood based Meta-heuristic Algorithms for MC/DC Test Data Generation
Structural testing is one of the most important activities within software testing. Ideally, to achieve 100% coverage of every conditions and decisions, tester must take an exhaustive approach. However, exhaustive testing is costly and time consuming. Addressing the aforementioned issues, researcher...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/14070/ http://umpir.ump.edu.my/id/eprint/14070/1/Comparative%20Analysis%20of%20Neighborhood%20based.pdf |
| Summary: | Structural testing is one of the most important activities within software testing. Ideally, to achieve 100% coverage of every conditions and decisions, tester must take an exhaustive approach. However, exhaustive testing is costly and time consuming. Addressing the aforementioned issues, researchers advocate the use of Multiple Condition/Decision Coverage (MC/DC) criteria for sampling of the test cases[1]. Owing the popularity of Search based Software Engineering; many researchers have recently treated MC/DC compliant test case generation as optimization problem. As a result, many meta-heuristic based strategy implementations have appeared in the literature. Most implementations have been focused on neighborhood-based meta-heuristics. In order to help test engineers to make informed decision on the best neighborhood based implementations, this paper investigates the size and time performance of two MC/DC test strategies re-implementation based on Simulated Annealing against two newly developed strategies based on Great Deluge and Late Acceptance Hill Climbing algorithms respectively. Experimental results demonstrate the strength and weakness of the algorithms, change of their behavior on different types of predicates, etc. |
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