Lightweight Plug-In-Based Application Development
NODe '02 Revised Papers from the International Conference NetObjectDays on Objects, Components, Architectures, Services, and Applications for a Networked World
The Art and Science of Software Release Planning
IEEE Software
A systematic approach for solving the wicked problem of software release planning
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Software product release planning through optimization and what-if analysis
Information and Software Technology
Empirical studies of agile software development: A systematic review
Information and Software Technology
IEEE Software
A systematic review on strategic release planning models
Information and Software Technology
Sensitivity analysis for weak constraint generation
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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Strategic release planning (SRP) is a critical step in iterative software development. SRP involves the assignment of features or requirements to releases while considering hard and soft constraints, such as time, effort, quality or resources. ReleasePlanner™ is a web-based decision support tool that is based on a sound and rigorous formal strategic release planning model. In this paper we describe the integration of ReleasePlanner™ with Visual Studio in the form of a Visual Studio plug-in called SRPPlugin. The SRP-Plugin is used to demonstrate our hypothesis that tools implemented as plug-ins for widely used development platform (such as Visual Studio) help to increase efficiency of the development process. The plug-in augments the rich Visual Studio environment with advanced release planning capabilities which result in better release planning quality, increased productivity and enhanced communication among project stakeholders. The added value of providing systematic release planning as part of a development is not limited to release planning alone. We have outlined the future work to extend SRPPlugin to include proactive decision making features such as utilization of sensitivity analysis and machine learning techniques to provide recommendations for the project manager during the challenging task of deciding which sets of features should be offered to whom, when, and why.