Abstract
Generation of Test cases in software testing is an important and a complex activity as it deals with diversified range of inputs. Fundamentally, test case generation is considered to be a multi-objective problem as it aims to cover many targets. Deriving test cases for the Web Applications has become critical to the most of the enterprises. In this paper, a solution for generating test cases for web applications is proposed; the solution uses the System Graph (consisting of links and data dependencies) considering that test cases were based on a combination of input values and data dependencies. Pairwise testing is used to derive the test cases to be executing from entire test cases and then a genetic algorithm is proposed to generate test cases specific to functional testing. The proposed approach was tested through two distinct experiments by measuring the code coverage at every generation and results show that genetic algorithm used increased the fitness value and code coverage. Overall, the results of the paper validate the proposed approach and algorithm, having potential in further construct an automated integrated solution for generating test cases for the entire process.
Highlights
The key point regarding the usage of Soft Computing in testing is towards maximizing the quality of software testing and to automate the test generation process
Genetic Programming (GP) [1] is a type of Evolutionary Algorithm which is simulated by biological growth to search programs that perform certain user-defined tasks
Test case generation as a Single objective optimization [12] aims at achieving maximum fitness value such that the test suit derived will have the high probability of generating good code coverage
Summary
The key point regarding the usage of Soft Computing in testing is towards maximizing the quality of software testing and to automate the test generation process. Genetic Programming (GP) [1] is a type of Evolutionary Algorithm which is simulated by biological growth to search programs that perform certain user-defined tasks This programming technique has been successfully applied to many fatigue problems present in software testing such as instinctive design, pattern recognition, and test suit generation [2].This suit of algorithms helps to automate the generation of basic test paths which includes several problems like data generation, sequence generation, test case derivation, and optimization. Test automation comes with its own challenges which include reusable scripts generation, recompiling the test scripts with modifications for different runs and rapid test development with least amount of development time and effort Traditional methods such as randomized approaches, goal aligned techniques involve human intervention, development effort, cost. To overcome the mentioned challenges test case generation methods needs enhanced algorithms
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