infeasible path

infeasible path

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This paper discusses the unique approach for data flow testing by applying evolutionary algorithms for the automatic generation of test paths and infeasible path detection by using data flow relations in a program.
Extension of our previous work [6] for test data generation for data flow testing by adding a new approach for infeasible path detection.
Extension of our previous implemented tool (ETODF) [24] for test data generation for data flow testing by adding a new component for infeasible path detection.
We have performed different experiments on programs ranging from small to medium level for test path generation and infeasible path detection using our proposed approaches.
The rest of the paper is organized as follows: Section II describes the proposed approach test path generation and infeasible path detection using genetic algorithm for data flow testing and section III represents the tool for ETODF.
Figure 3 depicts the high level flow of our proposed approach for infeasible path detection.
If the path followed in execution trace is different from the target path, then population counter (PC) checks, if PC is equal to the maximum number of iterations, then path is said to be infeasible and is stored in the file as infeasible path.
The infeasible path detection is through genetic algorithm by mutating the input data in every iteration by calculating the branch distance and approximation level [10].
Infeasible path detector detects the infeasible path using genetic algorithm.
For infeasible path detection, we have input different number of paths to ETODF and evaluate for infeasible detection.
However, in this study, Weyuker observed a large proportion of infeasible paths in data-flow testing.
One of the pitfalls of structural testing is the problem of infeasible paths.