Software engineering metrics and models
Software engineering metrics and models
Applied software measurement (2nd ed.): assuring productivity and quality
Applied software measurement (2nd ed.): assuring productivity and quality
Software Metrics: A Rigorous Approach
Software Metrics: A Rigorous Approach
Software Engineering Economics
Software Engineering Economics
Towards a value-based approach in software engineering
CEA'08 Proceedings of the 2nd WSEAS International Conference on Computer Engineering and Applications
Timesheet assistant: mining and reporting developer effort
Proceedings of the IEEE/ACM international conference on Automated software engineering
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Due to the pervasive nature of software, software-engineering practitioners have continuously expre.~.~ed their concerns over their inability to accurately predict the cost, schedule and quality of a software product under development Thus, one of the most important objectives of the software engineering community has been to develop useful models that constructively explain the software development life-cycle and accurately predict the cost, schedule and quality of developing a software product Most of the existing parametric models have been empirically calibrated to actual data from completed software projects. The most commonly used technique for empirical calibration has been the popular classical multiple regression approach. This approach imposes a few restrictions often violated by software engineering data and has resulted in the development of inaccurate empirical models that do not perform very well. The focus of this dissertation is to explain the drawbacks of the multiple regression approach for software engineering data and discuss the Bayesian approach which alleviates a few of the problems faced by the multiple regression approach.