An empirical validation of software cost estimation models
Communications of the ACM
Peopleware: productive projects and teams
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Choosing a software cost estimation model for your organization: a case study
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Building tailor-made software cost model: intermediate TUCOMO
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Reformulating and calibrating COCOMO
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IEEE Transactions on Software Engineering
A Model for Software Development Effort and Cost Estimation
IEEE Transactions on Software Engineering
Calibrating the COCOMO II post-architecture model
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COCOMO evaluation and tailoring
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SIGCPR '77 Proceedings of the fifteenth annual SIGCPR conference
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Information and Software Technology
An empirical study of the impact of team size on software development effort
Information Technology and Management
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Journal of Computer Science and Technology
An empirical study of the Cobb-Douglas production function properties of software development effort
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Probabilistic and analytical estimation of software development team size
International Journal of Hybrid Intelligent Systems
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Journal of Systems and Software
A distributed problem-solving framework for probabilistic software effort estimation
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International Journal of Information Technology Project Management
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In most software development organizations, there is seldom a one-to-one mapping between software developers and development tasks. It is frequently necessary to concurrently assign individuals to multiple tasks and to assign more than one individual to work cooperatively on a single task. A principal goal in making such assignments should be to minimize the effort required to complete each task. But what impact does the manner in which developers are assigned to tasks have on the effort requirements? This paper identifies four task assignment factors: team size, concurrency, intensity, and fragmentation. These four factors are shown to improve the predictive ability of the well-known Intermediate COCOMO cost estimation model. A parsimonious effort estimation model is also derived that utilizes a subset of the task assignment factors and Unadjusted Function Points. For the data examined, this parsimonious model is shown to have goodness of fit and quality of estimation superior to that of the COCOMO model, while utilizing fewer cost factors.