Robust regression and outlier detection
Robust regression and outlier detection
Software engineering metrics and models
Software engineering metrics and models
Evaluating Software Complexity Measures
IEEE Transactions on Software Engineering
Function point analysis
Asset-R: A function point sizing tool for scientific and real-time systems
Journal of Systems and Software
Practical software metrics for project management and process improvement
Practical software metrics for project management and process improvement
Function points: a study of their measurement processes and scale transformations
Journal of Systems and Software
An introduction to 3D function points
Software Development
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Function Points Analysis: An Empirical Study of Its Measurement Processes
IEEE Transactions on Software Engineering
Inter-item correlations among function points
ICSE '93 Proceedings of the 15th international conference on Software Engineering
A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression Models
IEEE Transactions on Software Engineering
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Making Software Measurement Work: Building an Effective Measurement Program
Making Software Measurement Work: Building an Effective Measurement Program
Migrating to Object Technology
Migrating to Object Technology
Counterpoint: The Problem with Function Points
IEEE Software
Function Point Analysis: Difficulties and Improvements
IEEE Transactions on Software Engineering
Towards a Framework for Software Measurement Validation
IEEE Transactions on Software Engineering
Conceptual Differences Among Functional Size Measurement Methods
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
IEEE Transactions on Software Engineering
Journal of Systems and Software
IFPUG-COSMIC Statistical Conversion
SEAA '08 Proceedings of the 2008 34th Euromicro Conference Software Engineering and Advanced Applications
A Metamodeling Approach to Estimate Software Size from Requirements Specifications
SEAA '08 Proceedings of the 2008 34th Euromicro Conference Software Engineering and Advanced Applications
Why comparative effort prediction studies may be invalid
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Convertibility of functional size measurements: new insights and methodological issues
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Functional Size of a Real-Time System
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
An experimental study on the conversion between IFPUG and COSMIC functional size measurement units
Information and Software Technology
Data & Knowledge Engineering
Proceedings of the Workshop on Advances in Functional Size Measurement and Effort Estimation
Information and Software Technology
Convertibility between IFPUG and COSMIC functional size measurements
PROFES'07 Proceedings of the 8th international conference on Product-Focused Software Process Improvement
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Background: Functional size measurement methods are increasingly being adopted by software organizations due to the benefits they provide to software project managers. The Function Point Analysis (FPA) measurement method has been used extensively and globally in software organizations. The COSMIC measurement method is considered a second generation FSM method, because of the novel aspects it brings to the FSM field. After the COSMIC method was proposed, the issue of convertibility from FPA to COSMIC method arose, the main problem being the ability to convert FPA historical data to the corresponding COSMIC Function Point (CFP) data with a high level of accuracy, which would give organizations the ability to use the data in their future planning. Almost all the convertibility studies found in the literature involve converting FPA measures to COSMIC measures statistically, based on the final size generated by both methods. Objectives: This paper has three main objectives. The first is to explore the accuracy of the conversion type that converts FPA measures to COSMIC measures statistically, and that of the type that converts FPA transaction function measures to COSMIC measures. The second is to propose a new conversion type that predicts the number of COSMIC data movements based on the number of file type references referenced by all the elementary processes in a single application. The third is to compare the accuracy of our proposed conversion type with the other two conversion types found in the literature. Method: One dataset from the management information systems domain was used to compare the accuracy of all three conversion types using a systematic conversion approach that applies three regression models: Ordinary Least Squares, Robust Least Trimmed Squares, and logarithmic transformation were used. Four datasets from previous studies were used to evaluate the accuracy of the three conversion types, to which the Leave One Out Cross Validation technique was applied to obtain the measures of fitting accuracy. Results: The conversion type most often used as well as the conversion type based on transaction function size were found to generate nonlinear, inaccurate and invalid results according to measurement theory. In addition, they produce a loss of measurement information in the conversion process, because of the FPA weighting system and FPA structural problems, such as illegal scale transformation. Our proposed conversion type avoids the problems inherent in the other two types but not the nonlinearity problem. Furthermore, the proposed conversion type has been found to be more accurate than the other types when the COSMIC functional processes comprise dataset applications that are systematically larger than their corresponding FPA elementary processes, or when the processes vary from small to large. Finally, our proposed conversion type delivered better results over the tested datasets, whereas, in general, there is no statistical significant difference between the accuracy of the conversion types examined for every dataset, particularly the conversion type most often used is not the most accurate. Conclusions: Our proposed conversion type achieves accurate results over the tested datasets. However, the lack of knowledge needed to use it over all the datasets in the literature limits the value of this conclusion. Consequently, practitioners converting from FPA to COSMIC should not stay with only one conversion type, assuming that it is the best. In order to achieve a high level of accuracy in the conversion process, all three conversion types must be tested via a systematic conversion approach.