Decision aiding dangers: The law of the hammer and other maxims
IEEE Transactions on Systems, Man and Cybernetics
Risk Management for Software Projects
IEEE Software
Dynamics of software development
Dynamics of software development
Measuring the software process: statistical process control for software process improvement
Measuring the software process: statistical process control for software process improvement
An examination of the decision styles of project managers: evidence of significant diversity
Information and Management
Software Requirements
Rapid Development: Taming Wild Software Schedules
Rapid Development: Taming Wild Software Schedules
How to Manage a Successful Software Project: With Microsoft Project 2000
How to Manage a Successful Software Project: With Microsoft Project 2000
Software development cost estimation approaches – A survey
Annals of Software Engineering
Software Risk Management: Principles and Practices
IEEE Software
CMMI Guidlines for Process Integration and Product Improvement
CMMI Guidlines for Process Integration and Product Improvement
Quality management metrics for software development
Information and Management
Information and Management
Software debugging, testing, and verification
IBM Systems Journal
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The paper first examines some issues that hinder the effective management of, and decision-making on, quality software development process and products delivery by practitioners. It then generates a decision model for managing software development projects. The model uses four concepts: mappability, accountability, interoperability and controllability in decision-making which is assumed to be based on a set of indicators that link task status of the development process and its quality assessment to the responsible authorities. The quality of the tasks, and hence, of the deliverables is measured using four attributes: completeness, correctness, consistency and compliance. A web-based example implementation is then discussed. We thus show that the model is flexible, extensible and scalable. Implementation challenges and implications are then discussed.