scholarly journals Improving Fault Detection Capability by Selectively Retaining Test Cases during Test Suite Reduction

2007 ◽  
Vol 33 (2) ◽  
pp. 108-123 ◽  
Author(s):  
Dennis Jeffrey ◽  
Neelam Gupta
Keyword(s):  
Fault Detection ◽  
Test Suite ◽  
Test Cases ◽  
Detection Capability ◽  
Test Suite Reduction

Refinement & Reduction of Cases for Regression Testing to Measure Software Reliability

2015 ◽  
Vol 8 (2) ◽  
Author(s):  
Dr. Rajat Sheel Jain ◽  
Dr. Amit Gupta
Keyword(s):  
Fault Detection ◽  
Software Quality ◽  
Software Reliability ◽  
Execution Time ◽  
Regression Testing ◽  
Test Suite ◽  
Test Cases ◽  
Detection Capability ◽  
Software Application ◽  
System Under Test

Requirements are very important features for the designing of any software application. Test suite creation is a big and hectic task for the Software Quality Analyst. We propose to develop a technique for analysis of Covered Requirement and its impact on the designed Test suite. The requirement associated with the test cases accepts specification like execution time, costing for minimization of test suite. The specification analyser compares the information about the techniques like Precision, Efficiency, Inclusiveness and Generality. By reducing the test suite size, we can reduce the execution cost and time, validation and management of the test cases from the suite for future releases of the software and able to maintain the fault detection capability by reusing the refined test cases. The requirement coverage will increase time-effectiveness in sorting the features of the application and reduces the duplicacy. An improved rate of testing activity will provide faster feedback of the system under test.

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

An Effective Approach to Test Suite Reduction and Fault Detection Using Data Mining Techniques

2017 ◽  
Vol 8 (4) ◽  
pp. 1-31 ◽  
Author(s):  
B. Subashini ◽  
D. Jeya Mala
Keyword(s):  
Data Mining ◽  
Attribute Reduction ◽  
Test Suite ◽  
Test Cases ◽  
Data Mining Techniques ◽  
Coverage Analysis ◽  
Quality Product ◽  
Irrelevant Attributes ◽  
Test Suite Reduction ◽  
Using Data

Software testing is used to find bugs in the software to provide a quality product to the end users. Test suites are used to detect failures in software but it may be redundant and it takes a lot of time for the execution of software. In this article, an enormous number of test cases are created using combinatorial test design algorithms. Attribute reduction is an important preprocessing task in data mining. Attributes are selected by removing all weak and irrelevant attributes to reduce complexity in data mining. After preprocessing, it is not necessary to test the software with every combination of test cases, since the test cases are large and redundant, the healthier test cases are identified using a data mining techniques algorithm. This is healthier and the final test suite will identify the defects in the software, it will provide better coverage analysis and reduces execution time on the software.

An Optimal Hybrid Regression Testing Approach Based on Code Path Pruning

2018 ◽  
pp. 265-286
Author(s):  
Varun Gupta
Keyword(s):  
Fault Detection ◽  
Detection Rate ◽  
Source Code ◽  
Hybrid Approach ◽  
Regression Testing ◽  
Test Suite ◽  
Test Cases ◽  
Hybrid Approaches ◽  
Testing Approach ◽  
Better Than

Hybrid regression testing approaches involve the combinations of test suite selections, prioritizations, and minimizations. The hybrid approaches must reduce size of test suite to minimal level and enhance fault detection rate. The chapter proposes a new hybrid regression testing approach that reduces the number of test cases by reducing the paths of source code on the basis of the dependency between the statements and the changes. The proposed technique is evaluated to be better than the existing hybrid approach in terms of percentage savings in test cases and fault detection rate.

Hybrid Regression Testing Based on Path Pruning

2015 ◽  
Vol 5 (1) ◽  
pp. 35-55 ◽  
Author(s):  
Varun Gupta ◽  
Durg Singh Chauhan ◽  
Kamlesh Dutta
Keyword(s):  
Fault Detection ◽  
Regression Testing ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Testing Time ◽  
Ripple Effect ◽  
Web Based ◽  
Testing Approach ◽  
Globally Distributed

Regression testing has been studied by various researchers for developing and testing the quality of software. Regression testing aims at re-execution of evolved software code to ensure that no new errors had been introduced during the process of modification. Since re-execution of all test cases is not feasible, selecting manageable number of test cases to execute modified code with good fault detection rate is a problem. In past few years, various hybrid based regression testing approaches have been proposed and successfully employed for software testing, aiming at reduction in the number of test cases and higher fault detection capabilities. These techniques are based on sequence of selections, prioritizations and minimization of test suite. However, these techniques suffer from major drawbacks like improper consideration of control dependencies, neglection of unaffected fragments of code for testing purpose. Further, these techniques have been employed on hypothetical or simple programs with test suites of smaller size. Present paper proposes hybrid regression testing, a combination of test case selections, test case prioritizations and test suite minimization. The technique works at statement level and is based on finding the paths containing statements that affects or gets affected by the addition/deletion or modification (both control and data dependency) of variables in statements. The modification in the code may cause ripple effect thereby resulting into faulty execution of the code. The hybrid regression testing approach is aimed at detecting such faults with lesser number of test cases. Reduction in number of test cases is possible because of the decreased number of paths to be tested. A web based framework to automate and parallelize this testing technique to maximum extend, making it well suited for globally distributed environments is also proposed in the present paper. Framework when implemented as a tool can handle large pool of test cases and will make use of parallel MIMD architectures like multicore systems. Technique is applied on prototype live system and results are compared with recently proposed hybrid regression testing approach against parameters of interest. Obtained optimized results are indicators of effectiveness of approach in terms of reduction in effort, cost as well as testing time in general and increment delivery time in particular.

scholarly journals Optimum Test Suite using Fault-Type Coverage Based Ant Colony Optimization Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Test Suite ◽  
Computational Time ◽  
Test Case ◽  
Test Cases ◽  
Product Lines ◽  
Fault Type ◽  
Testing Cost ◽  
Test Suite Reduction

Software Product Lines(SPLs) covers a mixture of features for testing Software Application Program(SPA). Testing cost reduction is a major metric of software testing. In combinatorial testing(CT), maximization of fault type coverage and test suite reduction plays a key role to reduce the testing cost of SPA. Metaheuristic Genetic Algorithm(GA) do not offer best outcome for test suite optimization problem due to mutation operation and required more computational time. So, Fault-Type Coverage Based Ant Colony Optimization(FTCBACO) algorithm is offered for test suite reduction in CT. FTCBACO algorithm starts with test cases in test suite and assign separate ant to each test case. Ants elect best test cases by updating of pheromone trails and selection of higher probability trails. Best test case path of ant with least time are taken as optimal solution for performing CT. Hence, FTCBACO Technique enriches reduction rate of test suite and minimizes computational time of reducing test cases efficiently for CT.

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