The software engineering laboratory: an operational software experience factory
ICSE '92 Proceedings of the 14th international conference on Software engineering
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Benchmarking Software-Development Productivity
IEEE Software
Benchmarking Software Organizations
IEEE Software
An Empirical Study of Software Productivity
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Building A Software Cost Estimation Model Based On Categorical Data
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
An Empirical Analysis of Software Productivity over Time
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Get Your Experience Factory Ready for the Next Decade--Ten Years after "How to Build and Run One"--
ICSE COMPANION '07 Companion to the proceedings of the 29th International Conference on Software Engineering
A General Empirical Solution to the Macro Software Sizing and Estimating Problem
IEEE Transactions on Software Engineering
Information and Software Technology
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During the past 10 years, the amount of effort put on setting up benchmarking repositories has considerably increased at the organizational, national and even at international levels to help software managers to determine the performance of software activities and to make better software estimates. This has enabled a number of studies with an emphasis on the relationship between software product size, effort and cost drivers in order to either measure the average performance for similar software projects or to develop estimation models and then refine them using the collected data. However, despite these efforts, none of those methods are yet deemed to be universally applicable and there is still no agreement on which cost drivers are significant in the estimation process. This study discusses some of the possible reasons why in software engineering, practitioners and researchers have not yet been able to come up with reasonable and well quantified relationships between effort and cost drivers although considerable amounts of data on software projects have been collected. An improved classification of application types in benchmarking repositories is also proposed.