Performance Testing in Mobile Application

Norberto, Marcus; Gaedicke, Lukas; Bernardino, Maicon; Legramante, Guilherme; Basso, Fábio Paulo; Rodrigues, Elisa

doi:10.1145/3364641.3364653

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Issues related to performance testing mentioned in the previous studies 6–12 include the following: Performance testing is usually conducted at the end of the testing phase. Therefore, there is limited time left for accomplishing it. Performance testing is costly in practice.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

“…Issues related to performance testing mentioned in the previous studies [6][7][8][9][10][11][12] include the following:…”

mentioning

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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A Systematic Mapping Study on Software Testing for Systems-of-Systems

Lopes

Norberto

Winston

et al. 2020

Proceedings of the 5th Brazilian Symposium on Systematic and Automated Software Testing

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Machine Learning for Software Engineering: A Tertiary Study

2023

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Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009–2022, covering 6 117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions including: conducting further empirical validation and industrial studies on ML; reconsidering deficient SE methods; documenting and automating data collection and pipeline processes; reexamining how industrial practitioners distribute their proprietary data; and implementing incremental ML approaches.

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Performance Testing in Mobile Application

Cited by 3 publications

References 14 publications

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

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

A Systematic Mapping Study on Software Testing for Systems-of-Systems

Machine Learning for Software Engineering: A Tertiary Study

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