Measuring software functional size via standard Function Points Analysis (FPA) requires the availability of fully specified requirements and specific competencies. Most of the time, the need to measure software functional size occurs well in advance with respect to these ideal conditions, under the lack of complete information or skilled experts. To work around the constraints of the official measurement process, several estimation methods for FPA have been proposed and are commonly used. Among these, the International Function Points User Group (IFPUG) has adopted the “High-level FPA” method (also known as the NESMA method). This method avoids weighting each data and transaction function by using fixed weights instead. Applying High-level FPA, or similar estimation methods, is faster and easier than carrying out the official measurement process but inevitably yields an approximation in the measures. In this article, we contribute to the problem of estimating software functional size measures by using machine learning. To the best of our knowledge, machine learning methods were never applied to the early estimation of software functional size. Our goal is to understand whether machine learning techniques yield estimates of FPA measures that are more accurate than those obtained with High-level FPA or similar methods. An empirical study on a large dataset of functional size predictors was carried out to train and test three of the most popular and robust machine learning methods, namely Random Forests, Support Vector Regression , and Neural Networks. A systematic experimental phase, with cycles of dataset filtering and splitting, parameter tuning, and model training and validation, is presented. The estimation accuracy of the obtained models was then evaluated and compared to that of fixed-weight models (e.g., High-level FPA) and linear regression models, also using a second dataset as the test set. We found that Support Vector Regression yields quite accurate estimation models. However, the obtained level of accuracy does not appear significantly better with respect to High-level FPA or to models built via ordinary least squares regression. Noticeably, fairly good accuracy levels were obtained by models that do not even require discerning among different types of transactions and data.

Background: Functional size measures of software are widely used for effort estimation, because they provide a fairly objective quantification of software size, which is one of the main factors affecting software development effort. Unfortunately, in some conditions, performing the standard Function Point Analysis process may be too long and expensive. Moreover, functional measures could be needed before functional requirements have been elicited completely and at the required detail level. Aim: Basing effort estimation on measures that are simpler than Function Points---hence, faster and cheaper to obtain---could be beneficial, if good estimation accuracy is possible. In this paper, the level of accuracy that can be obtained using simple measures instead of Function Points---as defined by IFPUG, the International Function Point User Group---is evaluated, based on an empirical study. Method: An analysis of the data provided in the ISBSG dataset was performed. Effort models based on standard IFPUG Function Points and on the number of transactions were derived. The accuracy of the effort estimates delivered by the two models were then compared. Results: Effort models based on the number of transactions appear marginally less accurate than models based on standard IFPUG Function Points for new development projects, and marginally more accurate for projects extending previously developed software. Conclusions: Based on the results of the empirical study, it seems that using the number of transactions instead of the standard IFPUG Function Points measures does not cause estimate accuracy to change to a practically appreciable extent. This conclusion applies to effort models using size measure as the only independent variable. Additional research is necessary to evaluate the performance of effort models based on the number of transactions in combination with other factors.

Simple Function Point is a functional size measurement method that can be used in place of IFPUG Function Point, but requires a much simpler - hence less time and effort consuming - measurement process. Simple Function Point was designed to be equivalent to IFPUG Function Point in terms of numerical results. This paper reports an empirical study aiming at verifying the effectiveness of Simple Function Point as a functional size measurement method, especially suitable to support estimation of software development effort. The data from a large popular public dataset were analyzed to verify the correlation of Simple Function Point with IFPUG Function Point, and the correlation of both size measures to development effort. The results obtained confirm, at a reasonable level of confidence, the hypothesis that Simple Function Point can be effectively used in place of IFPUG Function Point.

The COSMIC method provides a standardized way of measuring the functional size of software from the functional domains commonly referred to as 'business application' or 'Management Information Systems' (MIS) and 'real-time' software, and hybrids of these. In practice it is often sufficient to measure a functional size approximately. Typical situations where such a need arises are early in the life of a project, before the functional user requirements ('FUR') have been specified down to the level of detail where the precise size measurement is possible or when a measurement is needed, but there is insufficient time or no need to measure the required size using the standard method. The guideline describes the current state of the art with regard to approximate COSMIC functional size measurement. All proposed COSMIC approximation methods rely on determining some average of the size(s) and/or number(s) of functional processes. The fact that the size of a single functional process has no upper finite limit is probably the reason why multiple COSMIC approximation methods have been developed for different types of software. Therefore the guideline describes a number of approximation methods with their pros and cons, their recommended area of application and their validity, rather than document a single COSMIC approximation method.

Customer verification of functional size measures provided by the supplier in the acceptance phase is a critical task for the correctness of contract execution. A lack of control by the customer, both in depth and in scope, can lead to relevant deviations of the actual unitary price if compared to that accepted in the bid assignment process, with potential consequences in terms of unfairness or, in some cases, illegality. In this paper we summarize an efficient and well defined approach to validate supplier's functional size measurements in order to present the validation experiment. This approach was extensively presented at SMEF 2012. The approach, although statistically based, is rigorous, since it defines clear and unambiguous game roles, and efficient, in order to spend the adequate effort to achieve the expected confidence about supplier's functional size measurement capabilities. The approach consists in applying a variation of the Incremental Sampling Method that allows the customer tuning the validation effort on the quality level of size measures provided by the supplier, detected by the gap among these measures and the ones checked and validated on a sampled base. An empirical validation experiment, which is the focus of the present paper, is presented to illustrate the advantages of the approach.