Title | ||
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GWW metaheuristic to solve constraints on floating point numbers and application to test case generation. |
Abstract | ||
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We propose a population-based local search approach to generate automatically test cases for programs having floating point statements. The proposed approach, called GWW-MLFP (go with the winners using multi-precision arithmetic and local search for floating point constraints), adopts the GWW metaheutistic to solve constraints on floating point numbers which have a huge cardinality (e.g., 252 numbers in [0.5, 1.0]) and huge values (e.g., 10300): the search space is of exponential nature, motivating using an approximate approach. GWW-MLFP is a population-based metaheuristic which uses a set of candidates as a diversification mechanism. A set of B particles are maintained in each iteration. Consequently, different initial candidates are considered and different regions in the search space are explored. First, particles are randomly generated. Second, a dedicated local search algorithm on floating point numbers, called MLFP, is applied on each particle. In the following iterations of GWW-MLFP, the particles ... |
Year | Venue | Field |
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2016 | IJMNO | Population,Mathematical optimization,Exponential function,Floating point,Cardinality,Algorithm,Test case,Local search (optimization),Mathematics,Metaheuristic |
DocType | Volume | Issue |
Journal | 7 | 2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Mohamed Sayah | 1 | 0 | 0.34 |
Yahia Lebbah | 2 | 115 | 19.34 |