Automated test data generation using a scatter search approach

Abstract

The techniques for the automatic generation of test cases try to efficiently find a small set of cases that allow a given adequacy criterion to be fulfilled, thus contributing to a reduction in the cost of software testing. In this paper we present and analyze two versions of an approach based on the Scatter Search metaheuristic technique for the automatic generation of software test cases using a branch coverage adequacy criterion. The first test case generator, called TCSS, uses a diversity property to extend the search of test cases to all branches of the program under test in order to generate test cases that cover these. The second, called TCSS-LS, is an extension of the previous test case generator which combines the diversity property with a local search method that allows the intensification of the search for test cases that cover the difficult branches. We present the results obtained by our generators and carry out a detailed comparison with many other generators, showing a good performance of our approach.

Program Test Data Generation for Branch Coverage with Genetic Algorithm: Comparative Evaluation of A Maximization and Minimization Approach

“…Benchmark programs chosen for the experiments have been taken from [11,24]. These programs have a number of features such as real inputs, equality conditions with the AND operator and deeply nested predicates that make them suitable for testing different approaches for test data generation.…”

“…Amongst these several have addressed the issue of test data generation with program-based criteria [10]and in particular the branch coverage criterion [10,11,12,13,14,15,16,17,18].Further, [12,13,14,19,20] have formulated the problem as a minimization problem.…”

Search based Software Testing Technique for Structural Test Case Generation

“…So meta-heuristic search methods are employed, e.g. evolutionary algorithms, simulated annealing, or scatter search [5,6,13]. The robustness and suitability of evolutionary algorithms for the solution of different test tasks has already been proven in previous work [10].…”

Minimal Test Case Generation for Effective Program Test using Control Structure Methods and Test Effectiveness Ratio

“…The goal is to automatically obtain branch coverage. In scatter search [16] test case generator uses the control flow graph in order to determine the covered branches. Each node has a solution set and the algorithm will try to make the sets as diverse as possible, using a diversity function to generate solutions that can cover different branches of the program, whereas the proposed tool uses the control flow graph to generate test case and test data in order to cover all branches, statements, paths and decisions by visiting all the nodes and the edges.…”