Towards a simplified definition of Function Points

3Background. COSMIC Function Points and traditional Function Points (i.e., IFPUG Function points and more recent variation of Function Points, such as NESMA and FISMA) are probably the best known and most widely used Functional Size Measurement methods. The relationship between the two kinds of Function Points still needs to be investigated. If traditional Function Points could be accurately converted into COSMIC Function Points and vice versa, then, by measuring one kind of Function Points, one would be able to obtain the other kind of Function Points, and one might measure one or the other kind interchangeably. Several studies have been performed to evaluate whether a correlation or a conversion function between the two measures exists. Specifically, it has been suggested that the relationship between traditional Function Points and COSMIC Function Points may not be linear, i.e., the value of COSMIC Function Points seems to increase more than proportionally to an increase of traditional Function Points. Objective. This paper aims at verifying this hypothesis using available datasets that collect both FP and CFP size measures. Method. Rigorous statistical analysis techniques are used, specifically Piecewise Linear Regression, whose applicability conditions are systematically checked. The Piecewise Linear Regression curve is a series of interconnected segments. In this paper, we focused on Piecewise Linear Regression curves composed of two segments. We also used Linear and Parabolic Regressions, to check if and to what extent Piecewise Linear Regression may provide an advantage over other regression techniques. We used two categories of regression techniques: Ordinary Least Squares regression is based on the usual minimization of the sum of squares of the residuals, or, equivalently, on the minimization of the average squared residual; Least Median of Squares regression is a robust regression technique that is based on the minimization of the median squared residual. Using a robust regression technique helps filter out the excessive influence of outliers. Results. It appears that the analysis of the relationship between traditional Function Points and COSMIC Function Points based on the aforementioned data analysis techniques yields valid significant models. However, different results for the various available datasets are achieved. In practice, we obtained statistically valid linear, piecewise linear, and non-linear conversion formulas for several datasets. In general, none of these is better than the others in a statistically significant manner. Conclusions. Practitioners interested in the conversion of FP measures into CFP (or vice versa) cannot just pick a conversion model and be sure that it will yield the best results. All the regression models we tested provide good results with some datasets. In practice, all the models described in the paper –in particular, both linear and non-linear ones– should be evaluated in order to identify the ones that are best suited for the specific dataset at hand.openLavazza, L.; Morasca, S.; Robiolo, G.Lavazza, LUIGI ANTONIO; Morasca, Sandro; Robiolo, G

Design of a fuzzy logic software estimation process

This thesis describes the design of a fuzzy logic software estimation process. Studies show that most of the projects finish overbudget or later than the planned end date (Standish Group, 2009) even though the software organizations have attempted to increase the success rate of software projects by making the process more manageable and, consequently, more predictable. Project estimation is an important issue because it is the basis for the allocation and management of the resources associated to a project. When the estimation process is not performed properly, this leads to higher risks in their software projects, and the organizations may end up with losses instead of the expected profits from their funded projects. The most important estimates need to be made right in the very early phases of a project when the information is only available at a very high level of abstraction and, often, is based on a number of assumptions. The approach for estimating software projects in the software industry is the one typically based on the experience of the employees in the organization. There are a number of problems with using experience for estimation purposes: for instance, the way to obtain the estimate is only implicit, i.e. there is no consistent way to derive the estimated value, and the experience is strongly related to the experts, not to the organization. The research goal of this thesis is to design a software estimation process able to manage the lack of detailed and quantitative information embedded in the early phases of the software development life cycle. The research approach aims to leverage the advantages of the experience-based approach that can be used in early phases of software estimation while addressing some of the major problems generated by this estimation approach. The specific research objectives to be met by this improved software estimation process are: A. The proposed estimation process must use relevant techniques to handle uncertainty and ambiguity in order to consider the way practitioners make their estimates: the proposed estimation process must use the variables that the practitioners use. B. The proposed estimation process must be useful in early stages of the software development process. C. The proposed estimation process needs to preserve the experience or knowledge base for the organization: this implies an easy way to define and capture the experience of the experts. D. The proposed model must be usable by people with skills distinct from those of the people who configure the original context of the proposed model. In this thesis, an estimation process based on fuzzy logic is proposed, and is referred as the ‘Estimation of Projects in a Context of Uncertainty - EPCU’. The fuzzy logic approach was adopted for the proposed estimation process because it is a formal way to manage the uncertainty and the linguistic variables observed in the early phases of a project when the estimates need to be obtained: using a fuzzy system allows to capture the experience from the organization’s experts via inference rules and to keep this experience within the organization. The experimentation phase typically presents a big challenge, in software engineering in particular, and more so since the software projects estimates must be done “a priori”: indeed for verification purposes, there is a typically large elapsed time between the initial estimate and the completion of the projects upon which the ‘true’ values of effort, duration and costs can be known with certainty in order to verify whether or not the estimates were the right ones. This thesis includes a number of experiments with data from the software industry in Mexico. These experiments are organized into several scenarios, including one with reestimation of real projects completed in industry, but using – for estimation purposes - only the information that was available at the beginning of these projects. From the experiments results reported in this thesis it can be observed that with the use of the proposed fuzzy-logic based estimation process, estimates for these projects are better than the estimates based on the expert opinion approach. Finally, to handle the large amount of calculations required by the EPCU estimation model, as well as for the recording and the management of the information generated by the EPCU model, a research prototype tool was designed and developed to perform the necessary calculations