Anjan Pakhira
Leveraging the Cloud for Large-Scale Software Testing: A Case Study ‑ Google Chrome on Amazon
Pakhira, Anjan; Andras, Peter
Authors
Prof Peter Andras P.Andras@napier.ac.uk
Dean of School of Computing Engineering and the Built Environment
Abstract
Testing is a critical phase in the software life-cycle. While small-scale component-wise testing is done routinely as part of development and maintenance of large-scale software, the system level testing of the whole software is much more problematic due to low level of coverage of potential usage scenarios by test cases and high costs associated with wide-scale testing of large software. Here, the authors investigate the use of cloud computing to facilitate the testing of large-scale software. They discuss the aspects of cloud-based testing and provide an example application of this. They describe the testing of the functional importance of methods of classes in the Google Chrome software. The methods that we test are predicted to be functionally important with respect to a functionality of the software. The authors use network analysis applied to dynamic analysis data generated by the software to make these predictions. They check the validity of these predictions by mutation testing of a large number of mutated variants of the Google Chrome. The chapter provides details of how to set up the testing process on the cloud and discusses relevant technical issues.
Citation
Pakhira, A., & Andras, P. (2014). Leveraging the Cloud for Large-Scale Software Testing: A Case Study ‑ Google Chrome on Amazon. In Cloud Technology: Concepts, Methodologies, Tools, and Applications. IGI Global. https://doi.org/10.4018/978-1-4666-6539-2.ch055
Publication Date | 2014-10 |
---|---|
Deposit Date | Nov 2, 2021 |
Publisher | IGI Global |
Book Title | Cloud Technology: Concepts, Methodologies, Tools, and Applications |
ISBN | 9781466665392 |
DOI | https://doi.org/10.4018/978-1-4666-6539-2.ch055 |
Public URL | http://researchrepository.napier.ac.uk/Output/2808962 |
You might also like
Structural Complexity and Performance of Support Vector Machines
(2022)
Presentation / Conference Contribution
Federated Learning for Short-term Residential Load Forecasting
(2022)
Journal Article
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search