Automated Test Case Generation and Its Optimization for Path Testing Using Genetic Algorithm and Sampling

Mohapatra, Debasis; Bhuyan, Prachet; Mohapatra, Durga Prasad

doi:10.1109/icie.2009.22

Cited by 10 publications

(7 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hill-climbing (HC), Genetic Algorithms (GA) and the evolution methods (ET) Particle Swarm Optimization (PSO) [2,6,7] are better methods based on the search results.…”

Section: A Influence Over Algorithms Of Test Data Generationnitsmentioning

confidence: 99%

A program segmentation method for testing data generating based on path coverage

Wang

2010

2010 IEEE International Conference on Software Engineering and Service Sciences

View full text Add to dashboard Cite

For a program with many statements and complex structures, the testing have a lot of trouble, such as program analysis difficulties and need complex test cases,etc. This paper propose a method of dividing the program under test (PUT) into a several program segments with small cyclomatic complexity. This paper analyzes the relationship between the input variables and the fragments, gives the approach of processing the split point and illustrates the benefits of the method for generating test data both theoretically and experimentally. For each fragment, the method decreases the complexity of large program while structural testing, increases the efficiency of the test data generation.

show abstract

“…Hill-climbing (HC), Genetic Algorithms (GA) and the evolution methods (ET) Particle Swarm Optimization (PSO) [2,6,7] are better methods based on the search results.…”

Section: A Influence Over Algorithms Of Test Data Generationnitsmentioning

confidence: 99%

A program segmentation method for testing data generating based on path coverage

Wang

2010

2010 IEEE International Conference on Software Engineering and Service Sciences

View full text Add to dashboard Cite

show abstract

“…For example, Andreou et al [1] construct data flow graphs from source codes, and generate test data from the graphs to satisfy the coverage criterion called All-DU-Paths. Mohapatra et al [7] generate test data to cover the control flow graphs of program modules. Kalaji et al [6] generate test data of feasible test paths from extended finite state machines that represent the behavior of SUT.…”

Section: Introductionmentioning

confidence: 99%

Back-to-Back Testing Framework Using a Machine Learning Method

Takagi

Utsumi

Furukawa

2013

Studies in Computational Intelligence

View full text Add to dashboard Cite

Abstract. In back-to-back testing of software, expected outputs (test oracles) are generated from software that is similar to SUT (software under test), and are compared with test outputs from the SUT in order to reveal faults. The advantages of back-to-back testing are that one can automatically perform the creation of expected outputs that is one of the most costly processes in software testing, and one can obtain detailed expected outputs that are not limited to a specific aspect, such as state transitions. However, it is not easy to automatically classify the differences between the test outputs and the expected outputs into two groups, that is, one resulting from failures of the SUT and another resulting from intended functional differences between the SUT and the similar software. The manual classification is too costly and back-to-back testing can hardly be applied unless the functions of the similar software are exactly equal to the intended functions of the SUT. To solve this costly classification problem, this paper proposes a novel back-to-back testing framework in which a SVM (support vector machine) classifies them automatically.

show abstract

“…Stratified sampling [2] and category-partition are the same things but category-partition is used in categorizing all the functions according to their cohesiveness. Stratified sampling demonstrates the samples that populate certain functional domain.…”

Section: Stratified Samplingmentioning

confidence: 99%

“…Population ranking is done and then every chromosome is assigned a fitness value determined by this ranking. The worst individual will get the least fitness value and the best individual gets the best fitness value [2].…”

Section: Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Software Testing for Discrete Testsuite using Genetic Algorithm and Sampling Technique

PrasadaTripathy¹,

Kanhar²

2013

IJCA

View full text Add to dashboard Cite

Though Software Testing plays a vital role to produce better quality products, but it is time consuming and incurs expenditure. The more test will be conducted, products will be finer. Hence, testing is must for software development. Another side of testing is spending much money on it because people will work rigorously to generate the test suite and executing it. As we know that, no software is bug free software; we cannot assure that the testing which has been done for particular software is sufficient. To get a cost effective testing strategy, one should go for optimization of testsuite. This paper uses heuristic algorithm with sampling techniques used to optimize the test suite. Genetic algorithm may play a major role to have a sound weight on optimization of testsuite. If we go for sampling techniques then it usually gives more optimum result.

show abstract

Automated Test Case Generation and Its Optimization for Path Testing Using Genetic Algorithm and Sampling

Cited by 10 publications

References 1 publication

A program segmentation method for testing data generating based on path coverage

A program segmentation method for testing data generating based on path coverage

Back-to-Back Testing Framework Using a Machine Learning Method

Optimization of Software Testing for Discrete Testsuite using Genetic Algorithm and Sampling Technique

Contact Info

Product

Resources

About