Composing and Delivering Heterogeneous Web Testing Software as a Composite Web Testing Service

Lee, Shin-Jie; Lin, Yi; Lin, Kun-Hui; You, Jie-Lin

doi:10.1109/ics.2016.0124

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…Girardon et al 24 presented a systematic literature study published in 2020 that proved the advantages of utilizing cloud technology 80 . Several studies in the literature discussed the importance of applying efficient resource scheduling approaches 73,81 .…”

Section: P‐taas: the Need For Efficient Resource Utilizationmentioning

confidence: 99%

“…Girardon et al 24 presented a systematic literature study published in 2020 that proved the advantages of utilizing cloud technology. 80 Several studies in the literature discussed the importance of applying efficient resource scheduling approaches. 73,81 This guarantees high cloud resource utilization and allows parallel execution of test cases of the same test suite on multiple VM simultaneously.…”

Section: P-taas: the Need For Efficient Resource Utilizationmentioning

confidence: 99%

See 1 more Smart Citation

Performance testing as a service using cloud computing environment: A survey

Ali

Maghawry

Badr

2022

J Software Evolu Process

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Cloud testing is gaining much attention in both academia and industry as an emerging concept in the field of software testing. Cloud testing implies leveraging the resources of the cloud computing environment to overcome deficiencies of the traditional testing approaches. As a result, testing-as-a-service (TaaS) is introduced as a service model that conducts all testing activities in a fully automated manner using cloud-based resources. Performance testing is a type of software testing that validates the performance characteristics of the application under test (AUT) when subjected to different workloads during its operation. Performance characteristics include throughput, response time, and resource utilization of the AUT under a certain workload. This paper focuses on reviewing the literature related to the provision of performance testing as a service (P-TaaS). In this study, we survey the previous work related to cloud-based performance testing. We show the strengths and weaknesses of the current research. Besides, we compare the P-TaaS with the traditional performance testing methodologies. A detailed discussion of the benefits and challenges of P-TaaS is introduced along with identifying the research gaps and the future directions that can be adopted.

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Section: P‐taas: the Need For Efficient Resource Utilizationmentioning

confidence: 99%

Section: P-taas: the Need For Efficient Resource Utilizationmentioning

confidence: 99%

Performance testing as a service using cloud computing environment: A survey

Ali

Maghawry

Badr

2022

J Software Evolu Process

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“…Although this platform can provide test resources and scale them rapidly when necessary, its service focuses only on coverage testing based on SAT. Lee et al [19] proposed a system that integrates heterogeneous web testing tools into a composite service to reduce the tester's efforts in load and cross-browser testing. This system can automatically convert the inputs and outputs of various testing tools, initiate a testing process and send a compiled test report via email to reduce testers' workloads using the composite web testing service.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Cloud Testing System Based on Virtual Machines and Networks

2020

KSII TIIS

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Traditional software testing typically uses many physical resources to manually build various test environments, resulting in high resource costs and long test time due to limited resources, especially for small enterprises. Cloud computing can provide sufficient low-cost virtual resources to alleviate these problems through the virtualization of physical resources. However, the provision of various test environments and services for implementing software testing rapidly and conveniently based on cloud computing is challenging. This paper proposes a multilayer cloud testing model based on cloud computing and implements a hybrid cloud testing system based on virtual machines (VMs) and networks. This system realizes the automatic and rapid creation of test environments and the remote use of test tools and test services. We conduct experiments on this system and evaluate its applicability in terms of the VM provision time, VM performance and virtual network performance. The experimental results demonstrate that the performance of the VMs and virtual networks is satisfactory and that this system can improve the test efficiency and reduce test costs through rapid virtual resource provision and convenient test services.

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“…Finally, Lee et al considered a TaaSlike system for load and cross-browser testing for website development. The authors proposed a system for delivering heterogeneous web testing tools [7]. The results indicate that the system reduces the effort required for testing compared to conventional methods.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Rudy¹,

Smutnicki²

2020

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The problem of performing software tests using Testing-as-a-Service cloud environment is considered and formulated as an~online cluster scheduling on parallel machines with total flowtime criterion. A mathematical model is proposed. Several properties of the problem, including solution feasibility and connection to the classic scheduling on parallel machines are discussed. A family of algorithms based on a new priority rule called the Smallest Remaining Load (SRL) is proposed. We prove that algorithms from that family are not competitive relative to each other. Computer experiment using real-life data indicated that the SRL algorithm using the longest job sub-strategy is the best in performance. This algorithm is then compared with the Simulated Annealing metaheuristic. Results indicate that the metaheuristic rarely outperforms the SRL algorithm, obtaining worse results most of the time, which is counter-intuitive for a metaheuristic. Finally, we test the accuracy of prediction of processing times of jobs. The results indicate high (91.4%) accuracy for predicting processing times of test cases and even higher (98.7%) for prediction of remaining load of test suites. Results also show that schedules obtained through prediction are stable (coefficient of variation is 0.2-3.7%) and do not affect most of the algorithms (around 1% difference in flowtime), proving the considered problem is semi-clairvoyant. For the Largest Remaining Load rule, the predicted values tend to perform better than the actual values. The use of predicted values affects the SRL algorithm the most (up to 15% flowtime increase), but it still outperforms other algorithms.

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Composing and Delivering Heterogeneous Web Testing Software as a Composite Web Testing Service

Cited by 3 publications

References 26 publications

Performance testing as a service using cloud computing environment: A survey

Performance testing as a service using cloud computing environment: A survey

A Hybrid Cloud Testing System Based on Virtual Machines and Networks

Bulletin of the Polish Academy of Sciences: Technical Sciences

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