Estimating Manual Test Execution Effort and Capacity Based On Execution Points

Aranha, Eduardo; Borba, Paulo

doi:10.1080/1206212x.2009.11441938

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The proposed Algorithms can be used in Industrial setups. As part of the future work the Algorithms will be proposed to appropriate Industry counterparts and feedback will be incorporated appropriately [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Discussionmentioning

confidence: 99%

Quantification of Regression Test Suite Execution Time in Parallel Execution Setup with Weighted Test Suite Split Algorithm

2024

J Sen Net Data Comm

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Regression test suite execution time study focus is essentially on two aspects. They are execution time reduction and making effective use of available hardware resources and manpower. This paper investigates how the regression test suite can be split into subsets to make use of parallel execution across several machines with identical execution speeds and asymmetrical execution speeds. In the symmetrical execution speed setup, long test execution time test cases are evenly distributed across all the hardware machines. However, in asymmetrical execution speed machines, more test cases are distributed to speed machines to make efficient use of hardware resources. In all the situations where there is automation tool to execute the individual test cases of test suite this approach can be employed to make effective use of hardware resources and to keep the execution time within bounds. The Algorithms can also be used in situations where there are queues involved and serial, fixed time service takes place for each of the entity being served.

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Section: Discussionmentioning

confidence: 99%

Quantification of Regression Test Suite Execution Time in Parallel Execution Setup with Weighted Test Suite Split Algorithm

2024

J Sen Net Data Comm

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“…Also, r 1 and r 2 are two random numbers ∈ [0,1]. Once the velocities of all particles have been updated, their positions are updated in each iteration as in (10).…”

Section: ) Differential Evolution (De)mentioning

confidence: 99%

“…It is advisable to have a team dedicated exclusively to ST activities as it allows unbiased testing [10]. When a software project is planned, software testing effort prediction (STEP) is performed to assign suitable resources, such that testing teams are able to execute efficient tests for identifying and fixing them [11].…”

Section: Introductionmentioning

confidence: 99%

Gradient Boosting Optimized Through Differential Evolution for Predicting the Testing Effort of Software Projects

Sánchez-García,

López-Martín,

Abran

2023

IEEE Access

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Software testing (ST) is one of the most important software development life cycle (SDLC) phases and ST effort is often expressed as a percentage of SDLC effort. Unfortunately, in the literature ST effort percentage ranges from 10% to 60%. In the literature most of the machine learning algorithms and metaheuristics for optimizing them have looked at predicting overall SDLC effort without focusing on any specific SDLC phase, including testing. Therefore, this study investigates the application of the STEP of Gradient Boosting (GB) machine learning regression algorithm optimized through Differential Evolution (DE). Its prediction accuracy is compared with those obtained when the GB is also optimized through Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). The performance of GB-DE, GB-PSO, and GB-GA was also compared to that of statistical regression (SR). Seven data sets of actual projects were selected from an international public repository for software projects. The results showed that GB-DE was statistically better than SR in all seven data sets at 95% confidence, whereas GB-PSO and GB-GA were better than SR in four and three data sets, respectively. Thus, we can conclude that GB-DE can be used for STEP of either new projects or enhancement projects developed in either the third or fourth programming language generation.

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Machine learning techniques for software testing effort prediction

López-Martín

2021

Software Qual J

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Estimating Manual Test Execution Effort and Capacity Based On Execution Points

Cited by 5 publications

References 7 publications

Quantification of Regression Test Suite Execution Time in Parallel Execution Setup with Weighted Test Suite Split Algorithm

Quantification of Regression Test Suite Execution Time in Parallel Execution Setup with Weighted Test Suite Split Algorithm

Gradient Boosting Optimized Through Differential Evolution for Predicting the Testing Effort of Software Projects

Machine learning techniques for software testing effort prediction

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