Identifying Error-Prone Software An Empirical Study
IEEE Transactions on Software Engineering
Software engineering metrics and models
Software engineering metrics and models
Metrics and Models in Software Quality Engineering
Metrics and Models in Software Quality Engineering
An introduction to variable and feature selection
The Journal of Machine Learning Research
Effective Work Practices for FLOSS Development: A Model and Propositions
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 07
MASON: A Multiagent Simulation Environment
Simulation
Core and Periphery in Free/Libre and Open Source Software Team Communications
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Statistics for Engineering and the Sciences (5th Edition)
Statistics for Engineering and the Sciences (5th Edition)
An Exploratory Study on the Evolution of OSS Developer Communities
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
An approach for evaluating FOSS projects for student participation
Proceedings of the 43rd ACM technical symposium on Computer Science Education
Tool Assisted Analysis of Open Source Projects: A Multi-Faceted Challenge
International Journal of Open Source Software and Processes
| Hi-index | 0.00 |
A significant challenge in software engineering is accurately modeling projects in order to correctly forecast success or failure. The primary difficulty is that software development efforts are complex in terms of both the technical and social aspects of the engineering environment. This is compounded by the lack of real data that captures both the measures of success in performing a process, and the measures that reflect a group's social dynamics. This research focuses on the development of a model for predicting software project success that leverages the wealth of available open source project data in order to accurately forecast the behavior of those software engineering groups. The model accounts for both the technical elements of software engineering and the social elements that drive the decisions of individual developers. Agent-based simulations are used to represent the complexity of the group interactions, and the behavior of each agent is based on the acquired open source software engineering data. For four of the five project success measures, the results indicate that the developed model represents the underlying data well and provides accurate predictions of open source project success indicators.