An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

Abstract

Abstract-As the complexity of software is increasing, generating an effective test data has become a necessity. This necessity has increased the demand for techniques that can generate test data effectively. This paper proposes a test data generation technique based on adequacy based testing criteria. Adequacy based testing criteria uses the concept of mutation analysis to check the adequacy of test data. In general, mutation analysis is applied after the test data is generated. But, in this work, we propose a technique that applies mutation analysis at the time of test data generation only, rather than applying it after the test data has been generated. This saves significant amount of time (required to generate adequate test cases) as compared to the latter case as the total time in the latter case is the sum of the time to generate test data and the time to apply mutation analysis to the generated test data. We also use genetic algorithms that explore the complete domain of the program to provide near-global optimum solution. In this paper, we first define and explain the proposed technique. Then we validate the proposed technique using ten real time programs. The proposed technique is compared with path testing technique (that use reliability based testing criteria) for these ten programs. The results show that the adequacy based proposed technique is better than the reliability based path testing technique and there is a significant reduce in number of generated test cases and time taken to generate test cases.

An Automated Approach to Generate Test Cases From Use Case Description Model

“…Its result is a ratio between the traversed edges and all edges in the graph. This criterion guarantees that each edge in the graph should traverse at least one [Malhotra and Garg (2011)]. One of the proposed approach targets is to optimize and prioritize the test cases by achieving maximum transaction coverage.…”

“…To do so an evolutionary algorithm (genetic algorithm) should be associated with the transition coverage's criteria. The Genetic Algorithm (GA) is a heuristic search algorithm used to solve various optimization problems based on the evolutionary ideas of natural selection and genetics [Malhotra and Garg (2011)]. In terms of inputs change, the genetic algorithms are more robust than other Artificial intelligence (AI) systems.…”

“…Mutation refers to a random change of a bit in an individual, for example, flipping 0 to 1 or vice versa [Malhotra and Garg (2011); Holland and Goldberg (1989)]. In order to connect this stage with the stage three, after running the proposed tool in stage three and generate the test paths, the Algorithm of Test Paths Optimization (ATPO) Fig.…”

A Genetic Algorithm-based System for Automatic Control of Test Data Generation

“…The use of genetic algorithms for test data generation has been the subject of many research papers, like [2], [4], [9], [10], [16] and [18]. The authors propose different fitness functions and compare the results of theirs algorithms with other evolutionary algorithms or with random test generators.…”

Genetic Algorithm-Based Approach for Adequate Test Data Generation

“…It is one of the most significant means to ensure the quality of the software [1]. That is why during the development it goes through the testing phase.…”