Heuristic search-based approach for automated test data generation: a survey

Malhotra, Ruchika; Khari, Manju

doi:10.1504/ijbic.2013.053045

Cited by 27 publications

(10 citation statements)

References 45 publications

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“…The field of automated test data generation was first developed in the early 1970s. Clarke's (1976) [21] was considered the first research on automated test data generation. Parther (1987) [21] presented a new concept for test data generation called the path prefix method.…”

Section: Related Workmentioning

confidence: 99%

Object-Oriented Test Case Generation Using Teaching Learning-Based Optimization (TLBO) Algorithm

Al-Masri

Al-Sorori

2022

IEEE Access

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Researchers are currently seeking effective methods for automated software testing to reduce time, avoid test case redundancy, and create comprehensive test cases to cover (paths, benches, conditions, and statements). Generating a minimum number of test cases and covering all code paths is challenging in automated test case generation. Therefore, the use of optimization algorithms has become a popular trend for generating test cases to achieve many goals. In this study, we used a teaching-learning-based optimization algorithm to generate the minimum number of test cases. We compared our results with those of other state-of-the-art methods based on the path coverage for ten Java programs. The motive for using this algorithm is to optimize the number of test cases that cover all code paths in the unit test. The results emphasize that the proposed algorithm generates the minimum number of test cases and covers all paths in the code at a full-coverage rate.INDEX TERMS Test suite generation, unit testing, object-oriented test case generation, coverage-based optimization.

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Section: Related Workmentioning

confidence: 99%

Object-Oriented Test Case Generation Using Teaching Learning-Based Optimization (TLBO) Algorithm

Al-Masri

Al-Sorori

2022

IEEE Access

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“…Das et al [36] has explored hybridization of PSO with DE applied to the design of digital filters. However, DE has not been applied for test data generation and optimization problem [25,27,37].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization and Differential Evolution based Test Data Generation Algorithm for Data-Flow Coverage using Neighbourhood Search Strategy

Varshney

Mehrotra

2018

IJCAI

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Meta-heuristic search techniques, mainly Genetic Algorithm (GA), have been widely applied for automated test data generation according to a structural test adequacy criterion. However, it remains a challenging task for more robust adequacy criterion such as data-flow coverage of a program. Now, focus is on the use of other highly-adaptive meta-heuristic search techniques such as Particle Swarm Optimization (PSO) and Differential Evolution (DE). In this paper, a hybrid (adaptive PSO and DE) algorithm is proposed to generate test data for data-flow dependencies of a program with a neighbourhood search strategy to improve the search capability of the hybrid algorithm. The fitness function is based on the concepts of dominance relations and branch distance. The measures considered are mean number of generations and mean percentage coverage. The performance of the hybrid algorithm is compared with that of DE, PSO, GA, and random search. Over several experiments on a set of benchmark programs, it is shown that the hybrid algorithm performed significantly better than DE, PSO, GA and random search in data-flow test data generation with respect to the measures collected. Povzetek: Razvit je nov algoritem kot kombinacija hibridnega roja delcev in diferenčne evolucije z uporabo sosednje iskalne strategije.

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“…There is a well-established standard way to calculate branch coverage based on approximation level and branch distance for a target branch from the program's CFG [2]. Data-flow coverage criterion, however, has not received the same attention [2,24,25] due to lack of publicly available tool support and difficulty in writing test cases that satisfy data-flow criteria. The measures that have been used to assess the cost of a search-based technique for test data generation problem are number of iterations, total number of fitness evaluations performed over all iterations to achieve the test adequacy criteria, search time and size of the optimal test suite [3].…”

Section: Related Workmentioning

confidence: 99%

“…Test data is generated according to a test adequacy criterion (encoded as a fitness function to be maximized or minimized) that is used to guide the search. Structural testing, so far, has been the main focus of SBST [3,24,25]. The adequacy criteria for structural testing can be statement coverage (every statement is executed at least once); or branch coverage (every possible outcome of all the predicates is exercised at least once); or path coverage (every possible path is traversed at least once).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive PSO-based Approach for Data Flow Coverage of a Program

Varshney¹,

Mehrotra²,

Saini³

2017

IJERT

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Software testing is an important and expensive activity of the software development life cycle. Software testing includes test data generation and application of a test adequacy criterion. There has been an extensive application of meta-heuristic search algorithms to generate software test data for branch coverage and path coverage test adequacy criteria. However, test data generation for data-flow coverage test adequacy criterion remains a challenging task. Genetic algorithm and its variants have been the choice of researchers for automated test data generation. In recent years, other highly-adaptive swarm intelligence techniques such as Particle Swarm Optimization has also been applied for automated test data generation. In this paper, Particle Swarm Optimization algorithm with adaptive inertia weight strategy is used to generate test data for data-flow dependencies of a program. The proposed approach is evaluated on a set of benchmark programs, the measures considered are mean number of generations and mean percentage coverage achieved. The performance of the proposed approach is compared with that of Genetic Algorithm and random search. Over several experiments, it is shown that the proposed approach performed significantly better than random search and Genetic Algorithm in data-flow test data generation and optimization with an increasing performance gap for more complex subject programs.

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Heuristic search-based approach for automated test data generation: a survey

Cited by 27 publications

References 45 publications

Object-Oriented Test Case Generation Using Teaching Learning-Based Optimization (TLBO) Algorithm

Object-Oriented Test Case Generation Using Teaching Learning-Based Optimization (TLBO) Algorithm

A Hybrid Particle Swarm Optimization and Differential Evolution based Test Data Generation Algorithm for Data-Flow Coverage using Neighbourhood Search Strategy

An Adaptive PSO-based Approach for Data Flow Coverage of a Program

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