GA-based multiple paths test data generator

Ahmed, Moataz; Hermadi, Irman

doi:10.1016/j.cor.2007.01.012

Cited by 121 publications

(82 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A. Ahmed and I. Hermadi attempted to generate test data for multiple paths using genetic algorithm [31].…”

Section: Related Workmentioning

confidence: 99%

An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

Malhotra¹,

Garg²

2011

Journal of Information Processing Systems

View full text Add to dashboard Cite

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.

show abstract

“…A. Ahmed and I. Hermadi attempted to generate test data for multiple paths using genetic algorithm [31].…”

Section: Related Workmentioning

confidence: 99%

An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

Malhotra¹,

Garg²

2011

Journal of Information Processing Systems

View full text Add to dashboard Cite

show abstract

“…The mutation operator adds a random value to the components of the vector. That is, Ü Ü · Í´ ¼¼ ¼¼µ (13) where the probability distribution of these random values is a uniform distribution in the range ¼¼ ¼¼℄. However, not all the components of the individual are perturbed, only half of them are.…”

Section: Details Of the Mono-objective Algorithmsmentioning

confidence: 99%

“…Some examples are the presence of flags in conditions [8], the coverage of loops [9], the existence of internal states [10], and the presence of possible exceptions [11]. In addition, several evolutionary algorithms have been used as the search engine like scatter search [12], genetic algorithms [13,14], simulated annealing [15], and tabu search [16].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithms for the multi‐objective test data generation problem

2011

View full text Add to dashboard Cite

SUMMARYAutomatic Test Data Generation is a very popular domain in the field of Search Based Software Engineering. Traditionally, the main goal has been to maximize coverage. However, other objectives can be defined, like the oracle cost, which is the cost of executing the entire test suite and the cost of checking the system behaviour. Indeed, in very large software systems, the cost spent to test the system can be an issue, and then it makes sense considering two conflicting objectives: maximizing the coverage and minimizing the oracle cost. This is what we do in this paper. We mainly compare two approaches to deal with the Multi-Objective Test Data Generation Problem: a direct multi-objective approach and a combination of a mono-objective algorithm together with multi-objective test case selection optimization. Concretely, in this work we use four state-of-the-art multi-objective algorithms and two mono-objective evolutionary algorithms followed by a multi-objective test case selection based on Pareto efficiency. The experimental analysis compares these techniques on two different benchmarks. The first one is composed by 800 java programs created through a program generator. The second benchmark is composed by 13 real programs extracted from the literature. In the direct multi-objective approach, the results indicate that the oracle cost can be properly optimized; however the full branch coverage of the system poses a great challenge. Regarding the monoobjective algorithms, although they need a second phase of test case selection for reducing the oracle cost, they are very effective maximizing the branch coverage.

show abstract

“…Micheal et al [22], Levin and Yehudai [25], Joachim et al [27] indicated that GA outperforms other SBTDG methods e.g. local search or random testing.However eventhough they can generate test data with appropriate fault-prone ability [4,5], they fail to produce them quickly due to their slowly evolutionary speed. Recently, as a swarm intelligence technique, Particle Swarm Optimization (PSO) [6,7,8] has become a hot research topic in the area of intelligent computing.…”

Section: Introduction *mentioning

confidence: 99%

Generating Test Data for Software Structural Testing using Particle Swarm Optimization

Thi

2017

JCSCE

View full text Add to dashboard Cite

Search-based test data generation is a very popular domain in the field of automatic test data generation. However, existing search-based test data generators suffer fromsome problems. By combining static program analysis and search-based testing, our proposed approach overcomesone of these problems. Considering the automatic ability and the path coverage as the test adequacycriterion, this paper proposes using Particle Swarm Optimization, an alternative search technique, for automating the generation of test data for evolutionary structural testing. Experimental results demonstrate that our test data generator can generate suitable test data with higher path coverage than the previous one.

show abstract

GA-based multiple paths test data generator

Cited by 121 publications

References 8 publications

An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

An Adequacy Based Test Data Generation Technique Using Genetic Algorithms

Evolutionary algorithms for the multi‐objective test data generation problem

Generating Test Data for Software Structural Testing using Particle Swarm Optimization

Contact Info

Product

Resources

About