REAL-TIME OS REGRESSION DETECTION: AUTOMATING PERFORMANCE TESTING FOR PEAK EFFICIENCY

To solve the crucial problem of preserving peak performance across various technological environments, from mobile devices to intricate cloud infrastructures, this article explores the development of automatic regression detection in operating systems. The article thoroughly examines contemporary automated testing techniques based on their application at leading global technology enterprises and major cloud service providers. It highlights these systems' architectural elements, implementation techniques, and practical effects at different operational scales. It examines the revolutionary transition from conventional manual testing methods to complex automated frameworks. The article shows how automated regression detection has transformed quality assurance procedures by dramatically increasing detection accuracy, cutting operational overhead, and boosting system reliability in production settings. It offers practical insights for firms looking to adopt or optimize their regression detection capabilities based on a thorough analysis of case studies and industry implementations. Both technical and commercial viewpoints are covered in the article, which provides helpful advice for implementing automation successfully while avoiding typical problems. Emerging techniques and technology that hold promise for improving regression detection skills in the future are given particular focus.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-26
DOI
https://doi.org/10.5281/zenodo.14223572
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00
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article

REAL-TIME OS REGRESSION DETECTION: AUTOMATING PERFORMANCE TESTING FOR PEAK EFFICIENCY

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Bioinformatics
article

REAL-TIME OS REGRESSION DETECTION: AUTOMATING PERFORMANCE TESTING FOR PEAK EFFICIENCY

article en

Abstract

To solve the crucial problem of preserving peak performance across various technological environments, from mobile devices to intricate cloud infrastructures, this article explores the development of automatic regression detection in operating systems. The article thoroughly examines contemporary automated testing techniques based on their application at leading global technology enterprises and major cloud service providers. It highlights these systems' architectural elements, implementation techniques, and practical effects at different operational scales. It examines the revolutionary transition from conventional manual testing methods to complex automated frameworks. The article shows how automated regression detection has transformed quality assurance procedures by dramatically increasing detection accuracy, cutting operational overhead, and boosting system reliability in production settings. It offers practical insights for firms looking to adopt or optimize their regression detection capabilities based on a thorough analysis of case studies and industry implementations. Both technical and commercial viewpoints are covered in the article, which provides helpful advice for implementing automation successfully while avoiding typical problems. Emerging techniques and technology that hold promise for improving regression detection skills in the future are given particular focus.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 63%
Machine Learning in Bioinformatics
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