Acceptance testing based test case prioritization

Geetha, U.; Sankar, Sharmila; Sandhya, M.

doi:10.1080/23311916.2021.1907013

Cited by 8 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…8a, it is seen that the proposed-SFLA achieved the maximum fault rate at n 2 th position in G best_sol that means at only 54% of the test suite execution, 43% of the faults are already detected. Optimized multi-walk [5] and PSO [24] also yielded a fault detection rate of 40% and 41%, which are near to the results of the proposed-SFLA, respectively. However, the nature shown by the memetic algorithm [23] in covering the potential faults at the earliest is not promising.…”

Section: Comparative Studysupporting

confidence: 61%

“…9a, the memetic approach [23] is again the worst performer since it retains only 70% of the coverage of test function when approximately 60% of the re-ordered test cases are executed. Optimized [5] and basic multi-walk [32] performed almost on the same scale, where the first secured the coverage rate of 75% and the latter 79%. PSO [24] appeared to be finer than these 3 algorithms since the coverage rate that it procured is 83% at n 2 th position of execution, yet proposed-SFLA outperformed with approximately 88% of attained test coverage at the same position.…”

Section: Comparative Studymentioning

confidence: 83%

“…8d and 8e, the significant gap could be detected before n 2 and at n 2 , respectively. It is also seen that the shieldedness of test cases had a severe impact on the performances of optimized multi-walk [5] and PSO [24]. The performance of basic multi-walk [32] is reasonably satisfactory compared with memetic; nevertheless, this algorithm's performance is also indirectly proportional to the shieldedness effect.…”

Section: Comparative Studymentioning

confidence: 94%

“…To answer RQ1 and RQ2, apart from test function 1, this study has considered 4 other test functions with high and medium-level complexities. The proposed-SFLA approach is compared with those algorithms that are highly stable in the domain of memetics or are not entirely approaching randomnesses, such as GA-infused-Multi-walk (Optimized Multi-walk) [5], Memetic algorithm [23], PSO [24], and Multi-walk [32].…”

Section: Comparative Studymentioning

confidence: 99%

“…Among these three, single criterion-based exclusion of test cases during test case selection (TCS) or minimization (TCM) sometimes results in insecure test executions. However, this isn't a scenario with TCP as it opts for scheduling the test cases' execution order in a manner that would maximize the detection percentage of regression faults at the earliest [5]. Mathematically, TCP could be elaborated as [6]:…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

Gupta¹,

Mahapatra²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product. Due to resource constraints, when software is subjected to modifications, the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy. One such strategy is test case prioritization (TCP). Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest. Nonetheless, singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches. Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile, this study has introduced a memetics-inspired methodology for TCP. The proposed structure is first embedded with diverse parameters, and then traditional steps of the shuffled-frog-leaping approach (SFLA) are followed to prioritize the test cases at unit and integration levels. On 5 standard test functions, a comparative analysis is conducted between the established algorithms and the proposed approach, where the latter enhances the coverage rate and fault detection of re-ordered test sets. Investigation results related to the mean average percentage of fault detection (APFD) confirmed that the proposed approach exceeds the memetic, basic multi-walk, PSO, and optimized multi-walk by 21.7%, 13.99%, 12.24%, and 11.51%, respectively.

show abstract

Section: Comparative Studysupporting

confidence: 61%

Section: Comparative Studymentioning

confidence: 83%

Section: Comparative Studymentioning

confidence: 94%

Section: Comparative Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

Gupta¹,

Mahapatra²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Comparative Performance Evaluation of TFC-SVM Approach for Regression Test Case Prioritization

Dahiya¹,

Solanki²,

Rishi³

et al. 2022

Proceedings of International Conference on Communication and Artificial Intelligence

View full text Add to dashboard Cite

Software Testing Integration-Based Model (I-BM) Framework for Recognizing Measure Fault Output Accuracy Using Machine Learning Approach

Zulkifli

Gaol

Trisetyarso

et al. 2023

Int. J. Soft. Eng. Knowl. Eng.

View full text Add to dashboard Cite

In software development, the software testing phase is an important process in determining the quality level of the software. Software testing is a process of executing a program aimed at finding errors in module access, units, and involves the execution of the system being tested on a number of test inputs, and determining whether the output produced is correct. In this study, a model-based testing (MBT) called integration-based model (I-BM) framework will be developed. This I-BM framework integrates testing variables from several software testing methods, namely black-box testing, white-box testing, unit testing, system testing, and acceptance testing. The integrated variables are function, interface, structure, performance, requirement, documentation, positives, and negatives. Then, this framework will document software errors to form a dataset, which will be measured for the level of accuracy of expected manual fault output using neural network algorithm and support vector machine. From the experiment results, it shows that the accuracy level of predicting fault output values from the I-BM framework using the neural network algorithm is on average 80%, and it produces a superior SVM architecture model in predicting I-BM framework output errors with an accuracy value of 0.99, precision of 0.99, recall of 0.99, and [Formula: see text]-score of 0.99. Compared to other MBT, the IBM framework has the advantage of being a more comprehensive software testing model because it starts from the identification of problems, analysis, design, documentation of software testing, and recommendations for each fault output found. Thus, software errors can be classified systematically in the form of a dataset, and not only focus on software testing for product lines and module mappings.

show abstract

Acceptance testing based test case prioritization

Cited by 8 publications

References 22 publications

Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

Comparative Performance Evaluation of TFC-SVM Approach for Regression Test Case Prioritization

Software Testing Integration-Based Model (I-BM) Framework for Recognizing Measure Fault Output Accuracy Using Machine Learning Approach

Contact Info

Product

Resources

About