scholarly journals Fault Detection Power of a Widely Used Test Suite for a System of Communicating FSMs

2000 ◽  
pp. 35-56 ◽  
Author(s):  
Ana Cavalli ◽  
Svetlana Prokopenko ◽  
Nina Yevtushenko
Keyword(s):  
Fault Detection ◽  
Test Suite ◽  
Detection Power

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.

scholarly journals A Tester-Assisted Methodology for Test Redundancy Detection

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Negar Koochakzadeh ◽  
Vahid Garousi
Keyword(s):  
Fault Detection ◽  
False Positive ◽  
Test Suite ◽  
Test Case ◽  
Maintenance Costs ◽  
Detection Ability ◽  
Analysis Technique ◽  
Inspection Process ◽  
Redundancy Detection ◽  
Test Suites

Test redundancy detection reduces test maintenance costs and also ensures the integrity of test suites. One of the most widely used approaches for this purpose is based on coverage information. In a recent work, we have shown that although this information can be useful in detecting redundant tests, it may suffer from large number of false-positive errors, that is, a test case being identified as redundant while it is really not. In this paper, we propose a semiautomated methodology to derive a reduced test suite from a given test suite, while keeping the fault detection effectiveness unchanged. To evaluate the methodology, we apply the mutation analysis technique to measure the fault detection effectiveness of the reduced test suite of a real Java project. The results confirm that the proposed manual interactive inspection process leads to a reduced test suite with the same fault detection ability as the original test suite.

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.

Neuro-Fuzzy Modeling for Multi-Objective Test Suite Optimization

2016 ◽  
Vol 25 (2) ◽  
pp. 123-146 ◽  
Author(s):  
Zeeshan Anwar ◽  
Ali Ahsan ◽  
Cagatay Catal
Keyword(s):  
Fault Detection ◽  
Fuzzy Inference System ◽  
Execution Time ◽  
Detection Rate ◽  
Fuzzy Inference ◽  
Test Suite ◽  
Inference System ◽  
Multi Objective ◽  
Neuro Fuzzy ◽  
Regression Test

AbstractRegression testing is a type of testing activity, which ensures that source code changes do not affect the unmodified portions of the software adversely. This testing activity may be very expensive in, some cases, due to the required time to execute the test suite. In order to execute the regression tests in a cost-effective manner, the optimization of regression test suite is crucial. This optimization can be achieved by applying test suite reduction (TSR), regression test selection (RTS), or test case prioritization (TCP) techniques. In this paper, we designed and implemented an expert system for TSR problem by using neuro-fuzzy modeling-based approaches known as “adaptive neuro-fuzzy inference system with grid partitioning” (ANFIS-GP) and “adaptive neuro-fuzzy inference system with subtractive clustering” (ANFIS-SC). Two case studies were performed to validate the model and fuzzy logic, multi-objective genetic algorithms (MOGAs), non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithms were used for benchmarking. The performance of the models were evaluated in terms of reduction of test suite size, reduction in fault detection rate, reduction in test suite execution time, and reduction in requirement coverage. The experimental results showed that our ANFIS-based optimization system is very effective to optimize the regression test suite and provides better performance than the other approaches evaluated in this study. Size and execution time of the test suite is reduced up to 50%, whereas loss in fault detection rate is between 0% and 25%.

Sign in / Sign up

Export Citation Format

Share Document