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.

scholarly journals Test Case Reduction Using Ant Colony Optimization for Object Oriented Program

2015 ◽  
Vol 5 (6) ◽  
pp. 1424
Author(s):  
Sudhir Kumar Mohapatra ◽  
Srinivas Prasad
Keyword(s):  
Ant Colony Optimization ◽  
System Development ◽  
Object Oriented ◽  
Ant Colony ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Reduction Techniques ◽  
Maintenance Stage ◽  
The Cost

Software testing is one in all the vital stages of system development. In software development, developers continually depend upon testing to reveal bugs. Within the maintenance stage test suite size grow due to integration of new functionalities. Addition of latest technique force to make new test case which increase the cost of test suite. In regression testing new test case could also be added to the test suite throughout the entire testing process. These additions of test cases produce risk of presence of redundant test cases. Because of limitation of time and resource, reduction techniques should be accustomed determine and take away. Analysis shows that a set of the test case in a suit should satisfy all the test objectives that is named as representative set. Redundant test case increase the execution price of the test suite, in spite of NP-completeness of the problem there are few sensible reduction techniques are available. During this paper the previous GA primarily based technique proposed is improved to search out cost optimum representative set using ant colony optimization.

scholarly journals Fault-type coverage based ant colony optimization algorithm for attaining smaller test suite

2020 ◽  
Vol 9 (3) ◽  
pp. 507
Author(s):  
Bharathi M ◽  
Sangeetha V
Keyword(s):  
Ant Colony Optimization ◽  
Execution Time ◽  
Food Source ◽  
Reduction Rate ◽  
Ant Colony ◽  
Test Suite ◽  
Test Cases ◽  
Greedy Approach ◽  
Fault Type ◽  
Test Suite Optimization

<table width="0" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="593"><p>In this paper, we proposed Fault-Type Coverage Based Ant Colony Optimization (FTCBACO) technique for test suite optimization. An algorithm starts with initialization of FTCBACO factors using test cases in test suite. Then, assign separate ant to each test case called vertex. Each ant chooses best vertices to attain food source called objective of the problem by means of updating of pheromone trails and higher probability trails. This procedure is repeated up to the ant reaches food source. In FTCBACO algorithm, minimal number of test cases with less execution time chosen by an ant to cover all faults type (objective) are taken as optimal solution. We measured the performance of FTCBACO against Greedy approach and Additional Greedy Approach in terms of fault type coverage, test suite size and execution time. However, the heuristic Greedy approach and Additional Greedy approach required more execution time and maximum test suite size to provide the best resolution for test suite optimization problem. Statistical investigations are performed to finalize the performance significance of FTCBACO with other approaches that concludes FTCBACO technique enriches the reduction rate of test suite and minimizes execution time of reducing test cases efficiently.</p></td></tr></tbody></table>

Optimal Test Case Selection Using Ant Colony and Rough Sets

2020 ◽  
Vol 11 (2) ◽  
pp. 1-14
Author(s):  
Angelin Gladston ◽  
Niranjana Devi N.
Keyword(s):  
Optimization Technique ◽  
Computational Effort ◽  
Ant Colony ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Optimal Test ◽  
Average Percentage ◽  
Test Case Selection ◽  
Case Selection

Test case selection helps in improving quality of test suites by removing ambiguous, redundant test cases, thereby reducing the cost of software testing. Various works carried out have chosen test cases based on single parameter and optimized the test cases using single objective employing single strategies. In this article, a parameter selection technique is combined with an optimization technique for optimizing the selection of test cases. A two-step approach has been employed. In first step, the fuzzy entropy-based filtration is used for test case fitness evaluation and selection. In second step, the improvised ant colony optimization is employed to select test cases from the previously reduced test suite. The experimental evaluation using coverage parameters namely, average percentage statement coverage and average percentage decision coverage along with suite size reduction, demonstrate that by using this proposed approach, test suite size can be reduced, reducing further the computational effort incurred.

scholarly journals Using Global Constraints to Automate Regression Testing

AI Magazine ◽  
2017 ◽  
Vol 38 (1) ◽  
pp. 73-87
Author(s):  
Arnaud Gotlieb ◽  
Dusica Marijan
Keyword(s):  
Product Line ◽  
Regression Testing ◽  
Test Suite ◽  
Global Constraints ◽  
Test Case ◽  
Delivery Mode ◽  
Test Cases ◽  
High Quality ◽  
Test Suite Reduction ◽  
Test Requirements

Nowadays, any communicating or autonomous systems rely on high-quality software-based components. To ensure a sufficient level of quality, these components must be thoroughly verified before being released and being deployed in operational settings. Regression testing is a crucial verification process that executes any new release of a software-based component against previous versions of the component, with existing test cases. However, the selection of test cases in regression testing is challenging as the time available for testing is limited and some selection criteria must be respected. This problem, coined as Test Suite Reduction (TSR), is usually addressed by validation engineers through manual analysis or by using approximation techniques. Even if the underlying optimization problem is untractable in theory, solving it in practice is crucial when there are pressing needs to release high-quality components while at the same time reducing the time-to-market of new software releases. In this paper, we address the TSR problem with sound Artificial intelligence techniques such as Constraint Programming (CP) and global constraints. By associating each test case a cost-value aggregating distinct criteria, such as execution time, priority or importance due to the error-proneness of each test case, we propose several constraint optimization models to find a subset of test cases covering all the test requirements and optimizing the overall cost of selected test cases. Our models are based on a combination of NVALUE, GLOBALCARDINALITY, and SCALAR_PRODUCT, three well-known global constraints that can faithfully encode the coverage relation between test cases and test requirements. Our contribution includes the reuse of existing preprocessing rules to simplify the problem before solving it and the design of structure-aware heuristics, which take into account the notion of costs, associated with test cases. The work presented in this paper has been motivated by an industrial application in the communication domain. Our overall goal is to develop a constraint-based approach of test suite reduction that can be deployed to test a complete product line of conferencing systems in continuous delivery mode. By implementing this approach in a software prototype tool and experimentally evaluated it on both randomly generated instances and industrial instances, we hope to foster a quick adoption of the technology.

scholarly journals Test Case Optimization for Enhancing System Software Quality using Genetic Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 68-75
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Test Suite ◽  
Test Case ◽  
Product Lines ◽  
Reduction Techniques ◽  
Mutation Operators ◽  
Effective Manner ◽  
Software Programs ◽  
Test Suite Reduction

Software Product Lines (SPLs) embraces an enormous capacity of feature mixtures which cause challenges for evaluating software programs. Testsuite optimization plays major role to develope the quality of SPLs. In combinatorial testing (CT), pair wise fault coverage maximization and test case reduction accomplishes a substantial role for shrinking the testing cost of software programs. Many research works have been developed and designed for CT using different test suite reduction techniques. However Fuzzy clustering and TSRSO techniques do not provide a finest solution for test suite optimization problem. For that, Genetic Algorithm (GA) Technique is recommended and designed for test suite reduction in CT. Metaheuristic genetic algorithm delivers optimum solution in an effective manner. GA chooses and consolidates the testcases in a testsuite based on some principles such that maximum faults covered with minimum execution time. In Proposed GA, finest individuals are nominated for reproduction in order to create descendants of the succeeding generation. In addition, GA is a superior type of evolutionary algorithms generate finest solutions to optimization problems using selection, crossover and mutation operators. Consequently, GA is applied for resolving test suite reduction problem in CT

scholarly journals PRIORITIZATION OF COMBINATORIAL TEST CASES BY INCREMENTAL INTERACTION COVERAGE

2013 ◽  
Vol 23 (10) ◽  
pp. 1427-1457 ◽  
Author(s):  
RUBING HUANG ◽  
XIAODONG XIE ◽  
DAVE TOWEY ◽  
TSONG YUEH CHEN ◽  
YANSHENG LU ◽  
...  
Keyword(s):  
Test Case ◽  
Test Cases ◽  
New Strategy ◽  
Testing Cost ◽  
Test Prioritization ◽  
Interaction Testing ◽  
Parameter Interactions ◽  
Combinatorial Interaction Testing ◽  
Alternative Assumption ◽  
Better Than

Combinatorial interaction testing is a well-recognized testing method, and has been widely applied in practice, often with the assumption that all test cases in a combinatorial test suite have the same fault detection capability. However, when testing resources are limited, an alternative assumption may be that some test cases are more likely to reveal failure, thus making the order of executing the test cases critical. To improve testing cost-effectiveness, prioritization of combinatorial test cases is employed. The most popular approach is based on interaction coverage, which prioritizes combinatorial test cases by repeatedly choosing an unexecuted test case that covers the largest number of uncovered parameter value combinations of a given strength (level of interaction among parameters). However, this approach suffers from some drawbacks. Based on previous observations that the majority of faults in practical systems can usually be triggered with parameter interactions of small strengths, we propose a new strategy of prioritizing combinatorial test cases by incrementally adjusting the strength values. Experimental results show that our method performs better than the random prioritization technique and the technique of prioritizing combinatorial test suites according to test case generation order, and has better performance than the interaction-coverage-based test prioritization technique in most cases.

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 Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

2020 ◽  
Vol 17 (3) ◽  
pp. 165-173
Author(s):  
C.O. Yinka-Banjo ◽  
U. Agwogie
Keyword(s):  
Mobile Robot ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Fruit Fly ◽  
Convergence Speed ◽  
Ant Colony ◽  
Computational Time ◽  
Robot Path Planning ◽  
Nature Inspired Algorithms ◽  
Robot Path

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs). Keywords:  Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.

Test Pair Selection for Test Case Prioritization in Regression Testing for WS-BPEL Programs

2013 ◽  
Vol 10 (1) ◽  
pp. 73-102 ◽  
Author(s):  
Lijun Mei ◽  
Yan Cai ◽  
Changjiang Jia ◽  
Bo Jiang ◽  
W.K. Chan
Keyword(s):  
Web Services ◽  
Structural Similarity ◽  
Regression Testing ◽  
Test Pair ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Empirical Results ◽  
Selection For

Many web services not only communicate through XML-based messages, but also may dynamically modify their behaviors by applying different interpretations on XML messages through updating the associated XML Schemas or XML-based interface specifications. Such artifacts are usually complex, allowing XML-based messages conforming to these specifications structurally complex. Testing should cost-effectively cover all scenarios. Test case prioritization is a dimension of regression testing that assures a program from unintended modifications by reordering the test cases within a test suite. However, many existing test case prioritization techniques for regression testing treat test cases of different complexity generically. In this paper, the authors exploit the insights on the structural similarity of XML-based artifacts between test cases in both static and dynamic dimensions, and propose a family of test case prioritization techniques that selects pairs of test case without replacement in turn. To the best of their knowledge, it is the first test case prioritization proposal that selects test case pairs for prioritization. The authors validate their techniques by a suite of benchmarks. The empirical results show that when incorporating all dimensions, some members of our technique family can be more effective than conventional coverage-based techniques.

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.

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