Risk-Based Test Case Prioritization by Correlating System Methods and Their Associated Risks

Jahan, Hosney; Feng, Ziliang; Mahmud, Sakib

doi:10.1007/s13369-020-04472-z

Cited by 22 publications

(14 citation statements)

References 45 publications

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“…The experiment shows that the Firefly algorithm performs better than existing methods in terms of APFD. Jahan et al [20] presented a semi-automatic risk-based test case prioritization technique using the software modification information and the functions invocation relationship. Solanki et al [21] proposed a method based on the Ant Colony Optimization (ACO) algorithm to sort test cases by using the ant colony's natural food selection strategy.…”

Section: Related Work a Test Case Prioritizationmentioning

confidence: 99%

A Learn-to-Rank Method for Model-Based Regression Test Case Prioritization

2021

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Regression testing plays an indispensable role in software maintenance, which refers to retest the software following modifications to determine whether the changes have introduced new faults. However, regression testing requires massive amounts of effort to achieve a high fault detection rate. To address this issue, the test case prioritization technique is used to improve the fault detection rate by adjusting the execution order of test cases. For model-based regression test case prioritization, existing approaches have been developed using the single aspect of model-related information extracted from the previous executed test cases. In this paper, a novel learn-to-rank technique is proposed to prioritize test cases by combining the multidimensional features of Extended Finite State Machine (EFSM) under test to improve fault detection rate. Specifically, our method utilizes the random forest algorithm to combine multiple existing heuristic prioritization methods. Detailed experiments are conducted to evaluate the proposed method's performance in terms of Average Percentage Fault Detected (APFD). The experimental results show that the mean APFD value of our method reaches 0.884 for five subject EFSMs, which is 33.9% higher than the compared methods. INDEX TERMS regression testing, model based testing, EFSM, test case prioritization, learn to rank

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Section: Related Work a Test Case Prioritizationmentioning

confidence: 99%

A Learn-to-Rank Method for Model-Based Regression Test Case Prioritization

2021

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“…APFD to detect the fault detection rate over complete faulty test suite in TCP approach. Higher the value of fault detection rate then earlier and maximum faults detected during regression testing [40], [42], [44], [46], [47], using equation 1; APFD = 1-[(TF1+TF2+…….+TFm)/nm] +1/2n ………… (1) TFi = Number of first Test Cases in execution order, m = faults' number in application tests, n = test cases' total number in the suite. In the following section, we investigate the performance and faults rate identification of PBCV through empirical study.…”

Section: ) Calculate Priority (Cp)mentioning

confidence: 99%

“…R.T. is the most common method for verifying the quality of software application when there is a modification in the system during Development, as shown in Figure 2. This depicts the process of faults identification after modification for reliability before the release of a new device [14], [37], [42]. There are large sets of test suite which needed more time and cost to re-execute in the regression testing process.…”

Section: Introductionmentioning

confidence: 99%

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Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based

et al. 2020

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To survive in the competitive environment, most organizations have adopted componentbased software development strategies in the rapid technology advancement era and the proper utilization of cloud-based services. To facilitate the continuous configuration, reduce complexity, and faster system delivery for higher user satisfaction in dynamic scenarios. In cloud services, customers select services from web applications dynamically. Healthcare body sensors are commonly used for diagnosis and monitoring patients continuously for their emergency treatment. The healthcare devices are connected with mobile or laptop etc. on cloud environment with network and frequently change applications. Thus, organizations rely on regression testing during changes and implementation to validate the quality and reliability of the system after the alteration. However, for a large application with limited resources and frequently change component management activities in the cloud computing environment, component-based system verification is difficult and challenging due to irrelevant and redundant test cases and faults. In this study, proposed a test case selection and prioritization framework using a design pattern to increase the faults detection rate. First, we select test cases on frequently accessed components using observer patterns and, secondly, prioritize test cases on adopting some strategies. The proposed framework was validated by an experiment and compared with other techniques (previous faults based and random priority). Hence, experimental results show that the proposed framework successfully verified changes. Subsequently, the proposed framework increases the fault detection rate (i.e., more than 90%) than previous faults based and random priority (i.e., more than 80% respectively).

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“…In the time window established and presented in Figure 1, since year 2000 onwards, there is an increasing interest in RBT, with research increasing from 2012 to the present. The percentage of studies according to the classification type per year is: (i) 22.7% corresponding to approaches: 2012 (30) , 2017 (14) , 2018 (22) , 2020 (32,33) ; (ii) 18.2% corresponds to case study: 2000 (21) , 2010 (29) , 2014 (2) , 2016 (12) ; (iii) 9.1% corresponds to Taxonomy: 2014 (9) , 2019 (4) ; (iv) 9.1% corresponds to Techniques: 2005 (26) , 2018 (28) . (v) 40.9% corresponds to one article per year in Framework 2014 (27) , Tools 2007 (31) , Method 2016 (8) , Methodology 2013 (25) , Model 2012 (13) , Prediction of Defects 2016 (23) , Procedure 2014 (7) , Process 2010 (10) and Exploratory Review 2016 (24) .…”

Section: What Studies On Risk-based Testing Exist?mentioning

confidence: 99%

Un mapeo sistemático de literatura: pruebas basadas en riesgo en el desarrollo de software

Bastidas

Pardo

Ardila

2020

inycomp

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Las pruebas basadas en riesgos son un tipo de prueba que ayuda a identificar los riesgos del producto desde el inicio de desarrollo, incorporando técnicas que permitan su identificación y ser clasificados según su impacto y probabilidad, de modo que permitan crear casos de prueba para aquellos requerimientos que sean requeridos. Sin embargo, en las organizaciones de desarrollo software los riesgos que se identifican tienen relación con la planificación o coste del proyecto para garantizar la entrega del producto y no consideran otros riesgos como elementos de entrada para la creación de casos de prueba y evaluación de la calidad del producto. Este tipo de pruebas ayudan a detectar fallas tempranas durante el desarrollo del producto y a través de métricas poder evaluar el desempeño de los casos de pruebas. Actualmente, existen propuestas de como incorporar pruebas basadas en riesgos que se describirán en este documento y que se han identificado a través de un mapeo sistemático de la literatura.

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Risk-Based Test Case Prioritization by Correlating System Methods and Their Associated Risks

Cited by 22 publications

References 45 publications

A Learn-to-Rank Method for Model-Based Regression Test Case Prioritization

A Learn-to-Rank Method for Model-Based Regression Test Case Prioritization

Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based

Un mapeo sistemático de literatura: pruebas basadas en riesgo en el desarrollo de software

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