Defect prediction metrics for infrastructure as code scripts in DevOps-Reference-Cited by-同舟云学术

Defect prediction metrics for infrastructure as code scripts in DevOps

Published:2018-05-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings
language:
Short-container-title:

Author:

Rahman Akond¹,Stallings Jonathan¹,Williams Laurie¹

Affiliation:

1. North Carolina State University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3183440.3195034

Reference12 articles.

1. N. F. Alanna Brown Jez Humble Nigel Kersten and Gene Kim. 2017. 2016 State of DevOps Report. https://puppet.com/resources/whitepaper/2016-state-of-devops-report. (2017). {Online; accessed 15-August-2017}. N. F. Alanna Brown Jez Humble Nigel Kersten and Gene Kim. 2017. 2016 State of DevOps Report. https://puppet.com/resources/whitepaper/2016-state-of-devops-report. (2017). {Online; accessed 15-August-2017}.

2. The making of cloud applications: an empirical study on software development for the cloud

3. James Fryman. 2014. DNS Outage Post Mortem-Github. https://github.com/blog/1759-dns-outage-post-mortem. (2014). {Online; accessed 24-August-2017}. James Fryman. 2014. DNS Outage Post Mortem-Github. https://github.com/blog/1759-dns-outage-post-mortem. (2014). {Online; accessed 24-August-2017}.

4. Viral Gupta P.K. Kapur and Deepak Kumar. 2017. Modeling and measuring attributes influencing DevOps implementation in an enterprise using structural equation modeling. Information and Software Technology (2017). Viral Gupta P.K. Kapur and Deepak Kumar. 2017. Modeling and measuring attributes influencing DevOps implementation in an enterprise using structural equation modeling. Information and Software Technology (2017).

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on the optimization model for building an efficient IT infrastructure using the AWS platform;InterConf;2023-10-19

2. Identification of propagated defects to reduce software testing cost via mutation testing;Mathematical Biosciences and Engineering;2022

3. Towards an evidence-based theoretical framework on factors influencing the software development productivity;Empirical Software Engineering;2020-08-18