Enhancing path-oriented test data generation using adaptive random testing techniques

Nikravan, Esmaeel; Feyzi, Farid; Parsa, Saeed

doi:10.1109/kbei.2015.7436097

Cited by 7 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the second step, a test case is picked from the selected sub domain and executed according to a uniform probability distribution. Suppose that the sub domain is selected, then the probability that any test case in the sub domain is picked is 1…”

Section: Proposed Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Performance of Adaptive Random Partitioning Testing by Unifying the ARPT-1 and ARPT-2 Strategies

Dhivya*,

Meenakshi

2020

IJITEE

View full text Add to dashboard Cite

The software testing is considered as the most powerful and important phase. Effective testing process will leads to more accurate and reliable results and high quality software products. Random testing (RT) is a major software testing strategy and their effortlessness makes them conceivable as the most efficient testing strategies concerning the time required for experiment determination, its significant drawback of RT is defect detection efficacy. This draw back has been beat by Adaptive Testing (AT), however AT is enclosed of computational complexity. One most important method for improving RT is Adaptive random testing (ART). Another class of testing strategies is partition testing is one of the standard software program checking out strategies, which involves dividing the enter domain up into a set number of disjoint partitions, and selecting take a look at cases from inside every partition The hybrid approach is a combination of AT and RPT that is already existing called as ARPT strategy. In ARPT the random partitioning is improved by introducing different clustering algorithms solves the parameter space of problem between the target method and objective function of the test data. In this way random partitioning is improved to reduce the time conception and complexity in ARPT testing strategies. The parameters of enhanced ARPT testing approaches are optimized by utilizing different optimization algorithms. The computational complexity of Optimized Improved ARPT (OIARPT) testing strategies is reduced by selecting the best test cases using Support Vector Machine (SVM). In this paper the testing strategies of Optimized Improved ARPT with SVM are unified and named as Unified ARPT (UARPT) which enhances the testing performance and reduces the time complexity to test software.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

“…An approach [1] was developed to Generate path-based testing test data. The challenge faced by path coverage based testing was to build a test suite effectively which minimize the number of rejects.…”

Section: Literature Surveymentioning

confidence: 99%

Enhanced Performance of Adaptive Random Partitioning Testing by Unifying the ARPT-1 and ARPT-2 Strategies

Dhivya*,

Meenakshi

2020

IJITEE

View full text Add to dashboard Cite

show abstract

“…Indhumathi and Sarala [200] used .NET Solution Manifest files to generate test case scenarios, each one producing at least one test case. Nikravan et al [201] applied the path constraints of input parameters to support ART. Nie et al [91] enhanced ART testing effectiveness through the use of I/O relations.…”

Section: Additional Information Used To Support Artmentioning

confidence: 99%

A Survey on Adaptive Random Testing

Huang

Sun

et al. 2021

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work.

show abstract

“…ART methods use a variety of distance calculations, with corresponding computational overhead. Newly proposed methods like partitioning-based ART [15][16][17][18][19][20][21] try to decrease the computational overhead while maintaining the performance. For example, the authors of [21] proposed a new ART approach with the aim of decreasing the distance calculation computational overhead while distributing test cases evenly.…”

Section: Improvements In Rtmentioning

confidence: 99%

Path-oriented random testing through iterative partitioning (IP-PRT)

Nikravan¹,

Parsa²

2019

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

Path-oriented random testing aims at generating a uniformly distributed sequence of test data from a program input domain space to traverse a desired execution path of the program. To this aim, this article proposes a new algorithm to refine a program inputs domain space from invalid subdomains not covering the path. The validity of the subdomains is checked by a constraint propagation method against the path constraints (PCs). The proposed algorithm uses a divideand-conquer technique to iteratively split the inputs domain into subdomains and each time refutes those subdomains that are inconsistent with the PCs. The remaining shrunken subdomains provide all possible test data covering the desired path. Obviously, the more accurate the input domain is, the more effective test data will result. Experiments show the proposed method outperformed other related methods on a set of classical benchmark programs.

show abstract

Enhancing path-oriented test data generation using adaptive random testing techniques

Cited by 7 publications

References 23 publications

Enhanced Performance of Adaptive Random Partitioning Testing by Unifying the ARPT-1 and ARPT-2 Strategies

Enhanced Performance of Adaptive Random Partitioning Testing by Unifying the ARPT-1 and ARPT-2 Strategies

A Survey on Adaptive Random Testing

Path-oriented random testing through iterative partitioning (IP-PRT)

Contact Info

Product

Resources

About