Evolutionary Testing Using an Extended Chaining Approach
针对进化测试中适应度函数因忽略数据依赖而搜索不足的问题，提出将进化测试与扩展链式方法结合，通过识别数据依赖链并引导搜索，成功找到单独进化测试无法发现的测试用例。
Fitness functions derived from certain types of white-box test goals can be inadequate for evolutionary software test data generation (Evolutionary Testing), due to a lack of search guidance to the required test data. Often this is because the fitness function does not take into account data dependencies within the program under test, and the fact that certain program statements may need to have been executed prior to the target structure in order for it to be feasible. This paper proposes a solution to this problem by hybridizing Evolutionary Testing with an extended Chaining Approach. The Chaining Approach is a method which identifies statements on which the target structure is data dependent, and incrementally develops chains of dependencies in an event sequence. By incorporating this facility into Evolutionary Testing, and by performing a test data search for each generated event sequence, the search can be directed into potentially promising, unexplored areas of the test object's input domain. Results presented in the paper show that test data can be found for a number of test goals with this hybrid approach that could not be found by using the original Evolutionary Testing approach alone. One such test goal is drawn from code found in the publicly available libpng library.