Continuous validation for data analytics systems

Staples, Mark, Zhu, Liming and Grundy, John 2016, Continuous validation for data analytics systems, in ICSE'16 : Proceedings of the 38th International Conference on Software Engineering Campanion, ACM, New York, N.Y., pp. 769-773, doi: 10.1145/2889160.2889207.

Attached Files
Name Description MIMEType Size Downloads

Title Continuous validation for data analytics systems
Author(s) Staples, Mark
Zhu, Liming
Grundy, JohnORCID iD for Grundy, John
Conference name Software Engineering Companion : Conference (38th: 2016 : Austin, Texas)
Conference location Austin, Texas
Conference dates 14-22 May. 2016
Title of proceedings ICSE'16 : Proceedings of the 38th International Conference on Software Engineering Campanion
Publication date 2016
Start page 769
End page 773
Total pages 4
Publisher ACM
Place of publication New York, N.Y.
Keyword(s) software validation
continuous development
machine learning
data analytics
Summary From a future history of 2025: Continuous development is common for build/test (continuous integration) and operations (devOps). This trend continues through the lifecycle, into what we call `devUsage': continuous usage validation. In addition to ensuring systems meet user needs, organisations continuously validate their legal and ethical use. The rise of end-user programming and multi-sided platforms exacerbate validation challenges. A separate trend isthe specialisation of software engineering for technical domains, including data analytics. This domain has specific validation challenges. We must validate the accuracy of sta-tistical models, but also whether they have illegal or unethical biases. Usage needs addressed by machine learning are sometimes not speci able in the traditional sense, and statistical models are often `black boxes'. We describe future research to investigate solutions to these devUsage challenges for data analytics systems. We will adapt risk management and governance frameworks previously used for soft-ware product qualities, use social network communities for input from aligned stakeholder groups, and perform cross-validation using autonomic experimentation, cyber-physical data streams, and online discursive feedback.
ISBN 9781450342056
Language eng
DOI 10.1145/2889160.2889207
Field of Research 080309 Software Engineering
Socio Economic Objective 890201 Application Software Packages (excl. Computer Games)
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, ACM
Persistent URL

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 3 times in TR Web of Science
Scopus Citation Count Cited 6 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 204 Abstract Views, 11 File Downloads  -  Detailed Statistics
Created: Wed, 14 Sep 2016, 12:19:02 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact