Search‐based software test data generation: a survey

  title={Search‐based software test data generation: a survey},
  author={Phil McMinn},
  journal={Software Testing},
  • P. McMinn
  • Published 1 June 2004
  • Computer Science
  • Software Testing
The use of metaheuristic search techniques for the automatic generation of test data has been a burgeoning interest for many researchers in recent years. [] Key Method Metaheuristic search techniques are highlevel frameworks, which utilise heuristics to seek solutions for combinatorial problems at a reasonable computational cost.

Automatic Test Data Generation Using a Genetic Algorithm

This paper presents a new evolutionary approach for automated test data generation for structural testing that uses a newly defined program modeling allowing an easy program manipulation, and defines a crossover operator allowing to effectively improving individuals.

Generation of Search Based Test Data on Acceptability Testing Principle

The results suggest that the acceptability based algorithm is better than the reliability based path testing and condition testing techniques in both of these categories, and may significantly reduce the time of search based test data generation significantly outperforms Random testing.


Two approaches which employ an Estimation of Distribution Algorithm as the metaheuristic technique are explained, and it is concluded that this is a promising option that can be used to enhance the test data generation process.

Search based software test data generation for structural testing: a perspective

This study provides an overview of the various techniques that have been applied for structural test data generation and presents the open areas, challenges and future directions in the field of search based software testing with an emphasis on test datageneration for structural testing.

Genetic Model based Testing : a Framework and a Case Study

A framework for genetic model based testing is proposed, under which a graph-based model of the system under test is built using a genetic algorithm and test data is derived from the resulting model using (possibly) metaheuristic search techniques to provide the desired level of coverage.

Handling Constraints for Search Based Software Test Data Generation

  • R. SagarnaX. Yao
  • Computer Science
    2008 IEEE International Conference on Software Testing Verification and Validation Workshop
  • 2008
A constraint-handling point of view overcomes this limitation and opens the door to new designs and search strategies that, to the best of the knowledge, have not been considered yet.

Automated Software Test Data Generation for Data Flow Dependencies using Genetic Algorithm

A novel approach based on genetic algorithm to generate test data for a program is proposed and it is shown that the proposed approach outperforms random testing in test data generation and optimization.

A systematic review of search-based testing for non-functional system properties

Test case generation for transition-pair coverage using Scatter Search

An approach based on the metaheuristic technique Scatter Search for the automatic test case generation of BPEL business processes using a transition-pair coverage criterion and the results indicate that TCSS-LS-for-BPEL can be used in the generation of test cases for BPel business processes.

A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation

The intent is to aid future researchers doing empirical studies in SBST by providing an unbiased view of the body of empirical evidence and by guiding them in performing well-designed and executed empirical studies.



Formulating software engineering as a search problem

The aim of the paper is to stimulate greater interest in metaheuristic search as a tool of optimisation of software engineering problems and to encourage the investigation and exploitation of these technologies in finding near optimal solutions to the complex constraint-based scenarios which arise so frequently in software engineering.

Computer aided software testing using genetic algorithms

The approach described in this paper uses the ideas of Genetic Algorithms (GAs) to automatically develop a set of test data to achieve a level of coverage (branch coverage in this case) and neatly sidesteps many of the problems encountered by other systems in attempting to automatically generate test data.

Test‐data generation using genetic algorithms

This paper presents a technique that uses a genetic algorithm for automatic test‐data generation, a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem.

Automatic structural testing using genetic algorithms

Genetic algorithms have been used to generate test sets automatically by searching the domain of the software for suitable values to satisfy a predefined testing criterion, and have been applied successfully to several problems, varying in complexity from a quadratic equation solver to a generic sort module that comprises several procedures.

Generating Software Test Data by Evolution

The implementation of the GA-based system is described and the effectiveness of this approach on a number of programs, one of which is significantly larger than those for which results have previously been reported in the literature, are examined.

The Dynamic Domain Reduction Procedure for Test Data Generation: Design and Algorithms

A new procedure for automatically generating test data that incorporates ideas from symbolic evaluation, constraint-based testing, and dynamic test data generation, and incorporates an intelligent search technique based on bisection is presented.

The Automatic Generation Of Software Test Data Sets Using Adaptive Search Techniques

Test sets which cover all branches of a library of five procedures which solve the triangle problem, have been produced automatically using genetic algorithms. The tests are derived from both the

Structural and Functional Sequence Test of Dynamic and State-Based Software with Evolutionary Algorithms

For automatic sequence testing, a fitness function for the application of ET will be introduced, which allows the optimization of input sequences that reach a high coverage of the software under test.

Dynamic method for software test data generation

  • B. Korel
  • Computer Science
    Softw. Test. Verification Reliab.
  • 1992
In this approach, the path selection stage is eliminated and test data are derived based on the actual execution of the program under test and function minimization methods.

Fitness Function Design To Improve Evolutionary Structural Testing

Research results are presented on suggested modifications to the fitness function leading to the improvement of evolutionary testability by achieving higher coverage with less resources.