Skip Nav Destination
ASME Press Select Proceedings
International Conference on Software Technology and Engineering, 3rd (ICSTE 2011)
ISBN:
9780791859797
No. of Pages:
760
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
31 User-Centric Process Descriptions
By
Michael Deynet
Michael Deynet
Search for other works by this author on:
Page Count:
6
-
Published:2011
Citation
Deynet, M. "User-Centric Process Descriptions." International Conference on Software Technology and Engineering, 3rd (ICSTE 2011). Ed. Othman, M, & Kasim, RSR. ASME Press, 2011.
Download citation file:
The aim of this paper is to present a rule-based software process language including an approach for user (e.g. developer, SW architect) assistance. The approach observes the actions of the user and tries to predict the next steps of the user. For this we use approaches in the area of machine learning (sequence learning) and adopt these for the use in software processes. An evaluation shows that our approach predicts better than the original prediction algorithm.
This content is only available via PDF.
You do not currently have access to this chapter.
Email alerts
Related Chapters
Automatic Bug Assignment Using History of Packages
International Conference on Computer Engineering and Technology, 3rd (ICCET 2011)
Machine Learning to Judge Labor Relations' Harmoniousness Based on Decision Tree-Based Method
International Symposium on Information Engineering and Electronic Commerce, 3rd (IEEC 2011)
An Algorithm Implementation about SVR Based on Spider
International Symposium on Information Engineering and Electronic Commerce, 3rd (IEEC 2011)
Automatic Classification of Persian Texts Employing Keywords
International Conference on Computer Research and Development, 5th (ICCRD 2013)
Related Articles
A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data
J. Mech. Des (June,2016)
Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach
J. Energy Resour. Technol (February,2021)
Using Machine Learning to Predict Core Sizes of High-Efficiency Turbofan Engines
J. Eng. Gas Turbines Power (November,2019)