Name
Abstract | ||
|---|---|---|
Evolutionary Testing (ET) is a kind of efficient method of automatically test case generation. ET uses a kind of meta-heuristic search technique, the Genetic Algorithm, to convert the task of test case generation into an optimal problem. Nowadays, ET has been widely researched in many areas, especially in the GA configuration problem. In this paper, we suggest two strategies for the Genetic Algorithm configuration, to improve the performance of ET. One is Annealing Genetic Algorithm (AGA), which alters the mutation probability dynamically, and the other is Restricted Genetic Algorithm (RGA), which adds restrictions into fitness functions. The two strategies made ET hit the global optimal solution in fewer generations, and most offspring genes located in the legal domain. |
Year | Venue | Keywords |
|---|---|---|
2005 | COMPSAC (2) | test case generation,fewer generation,evolutionary testing,configuration strategy,Genetic Algorithm,efficient method,GA configuration problem,Evolutionary Testing,optimal problem,global optimal solution,case generation,Genetic Algorithm configuration |
DocType | ISSN | ISBN |
Conference | 0730-3157 | 0-7695-2413-3 |
Citations | PageRank | References |
3 | 0.44 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoyuan Xie | 1 | 535 | 26.69 |
| Xu, Baowen | 2 | 2476 | 165.27 |
| Changhai Nie | 3 | 379 | 26.44 |
| Liang Shi | 4 | 298 | 34.96 |
| Lei Xu | 5 | 124 | 18.82 |