Quality software management (Vol. 1): systems thinking
Quality software management (Vol. 1): systems thinking
The dynamics of software project scheduling
Communications of the ACM
Software Engineering Economics
Software Engineering Economics
The Mythical Man-Month: Essays on Softw
The Mythical Man-Month: Essays on Softw
Analyzing medium-scale software development
ICSE '78 Proceedings of the 3rd international conference on Software engineering
How to Improve the Calibration of Cost Models
IEEE Transactions on Software Engineering
System-level partitioning with uncertainty
CODES '99 Proceedings of the seventh international workshop on Hardware/software codesign
IEEE Transactions on Software Engineering
Early Prediction of Project Schedule Slippage
ASSET '98 Proceedings of the 1998 IEEE Workshop on Application - Specific Software Engineering and Technology
Information and Management
Journal of Systems and Software
Unified framework for developing testing effort dependent software reliability growth models
WSEAS TRANSACTIONS on SYSTEMS
DSOM '09 Proceedings of the 20th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management: Integrated Management of Systems, Services, Processes and People in IT
ACM SIGSOFT Software Engineering Notes
Software reliability analysis and assessment using queueing models with multiple change-points
Computers & Mathematics with Applications
Measuring bug complexity in object oriented software system
ACM SIGSOFT Software Engineering Notes
Empirical findings on team size and productivity in software development
Journal of Systems and Software
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Several algorithmic models have been proposed to estimate software costs and other management parameters. Early prediction of completion time is absolutely essential for proper advance planning and aversion of the possible ruin of a project. Putnam's SLIM model offers a fairly reliable method that is used extensively to predict project completion times and manpower requirements as the project evolves. However, the nature of the Norden/Rayleigh curve used by Putnam, renders it unreliable during the initial phases of the project, especially in projects involving a fast manpower buildup, as is the case with most software projects. In this paper, we propose the use of a model that improves early prediction considerably over the Putnam model. An analytic proof of the model's improved performance is also demonstrated on simulated data.