A Multi-objective Particle Swarm Optimization for Test Case Selection Based on Functional Requirements Coverage and Execution Effort

Souza, Luciana Sandra Bastos de; Miranda, Péricles; Prudêncio, Ricardo B. C.; Barros, Flávia A.

doi:10.1109/ictai.2011.45

Cited by 48 publications

(39 citation statements)

References 17 publications

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“…De Souza et al present an approach in test case selection using particle swarm optimization and based on two objectives. The first objective is the execution cost and the second is the functional requirement coverage [7]. He et al propose an approach in test suite reduction through combining information from both execution cost and code coverage using genetic algorithms [8].…”

Section: Related Workmentioning

confidence: 99%

Using Textual Similarity for Test Suite Prioritization

Alazzam¹

2016

Fifth International Conference on Advances in Computing, Electronics and Electrical Technology - CEET 2016

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Abstract-Techniques in test suite reduction concentrated on different testing activities in general and regression testing in specific. Regression testing is typically performed after updating or maintaining the software in order to verify that the occurred changes do not incur any problem. Executing all test suites after any modification increases the cost of testing. Thus reduction in test suites resolves this problem by including only relevant test cases. In this paper, a new technique is proposed and presented to reduce the test suite by calculating the similarity between test cases and the source implemented through using Latent Semantic Indexing (LSI) .Moreover the proposed technique is evaluated through calculating the code coverage using EclEmma.

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Section: Related Workmentioning

confidence: 99%

Using Textual Similarity for Test Suite Prioritization

Alazzam¹

2016

Fifth International Conference on Advances in Computing, Electronics and Electrical Technology - CEET 2016

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“…Simultaneously it reduced testing cost and improved the cycle time. Different techniques for optimization are Genetic Algorithm (GA) [1], Particle Swarm Optimization (PSO) [2], Intelligent Search Engine (ISA) [5], Intelligent Test Case Optimization Agent (ITOA) [6], Artificial Bee Colony (ABC) algorithm etc. [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Model Based Test Case Generation and Optimization Using Intelligent Optimization Agent

Mahali

Acharya

Mohapatra

2015

Advances in Intelligent Systems and Computing

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Test case optimization is one of the techniques which efficiently manage the exponential growth in time and cost of testing. But in many times the researchers compromise with the code coverage while going for optimization. In this paper, the test suite is optimized using Intelligent Optimization Agent (IOA) while the keeping the percentage of code coverage unchanged. First the System Under Test (SUT) is modelled using UML Activity Diagram (AD) and converted into an Activity Graph (AG). Then the optimized path is found out in AD by using IOA and cost attributes. Then suitable algorithms are proposed to remove the redundant nodes in the optimized path.

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“…Regarding multi-objective TC selection, we can cite the use of evolutionary approaches [4] and the use of Particle Swarm Optimization (PSO) techniques (our previous work) [7]. In [7], we investigated the multi-objective TC selection considering both the functional requirements coverage (quality) and the execution effort (cost) of the selected subset of TCs as objectives of the selection process.…”

Section: Introductionmentioning

confidence: 99%

“…In [7], we investigated the multi-objective TC selection considering both the functional requirements coverage (quality) and the execution effort (cost) of the selected subset of TCs as objectives of the selection process. Despite of the obtained good results on a case study, further improvements could be performed.…”

Section: Introductionmentioning

confidence: 99%

“…In the current work, we propose the use of hybrid multiobjective algorithms for TC selection, More specifically, we developed hybrid algorithms by adding local search capabilities to the Binary Multi-Objective Particle Swarm Optimization with Crowding Distance and Roulette Wheel (BMOPSO-CDR) algorithm (see [7]), based on the ideas presented in [8]. The local search procedure intends to explore the neighborhood of a solution to (possibly) obtain better solutions nearby this solution.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Binary Multi-objective Particle Swarm Optimization with Local Search for Test Case Selection

Souza

Prudêncio

Barros

2014

2014 Brazilian Conference on Intelligent Systems

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During the software testing process a variety of test suites can be generated in order to evaluate and assure the quality of the products. However, in some contexts the execution of all suites does not fit the available resources (time, people, etc). In such cases, the suites could be automatically reduced based on some selection criterion. Automatic Test Case (TC) selection could be used to reduce the suites based on some selection criterion. This process can be treated as an optimization problem, aiming to find a subset of TCs which optimizes one or more objective functions (i.e., selection criteria). In this light, we developed two new mechanisms for TC selection which consider two objectives simultaneously: maximize branch coverage while minimizing execution cost (time). These mechanisms were implemented using multi-objective techniques based on Particle Swarm Optimization (PSO). Additionally, we create hybrid multi-objective selection algorithms in order to improve the results. The experiments were performed on the space program from the SIR repository, attesting the feasibility of the proposed hybrid strategies.

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A Multi-objective Particle Swarm Optimization for Test Case Selection Based on Functional Requirements Coverage and Execution Effort

Cited by 48 publications

References 17 publications

Using Textual Similarity for Test Suite Prioritization

Using Textual Similarity for Test Suite Prioritization

Model Based Test Case Generation and Optimization Using Intelligent Optimization Agent

A Hybrid Binary Multi-objective Particle Swarm Optimization with Local Search for Test Case Selection

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