Weighted Micro Function Points


Weighted Micro Function Points is a modern software sizing algorithm which is a successor to solid ancestor scientific methods as COCOMO, COSYSMO, maintainability index, cyclomatic complexity, function points, and Halstead complexity. It produces more accurate results than traditional software sizing methodologies, while requiring less configuration and knowledge from the end user, as most of the estimation is based on automatic measurements of an existing source code.
As many ancestor measurement methods use source lines of code to measure software size, WMFP uses a parser to understand the source code breaking it down into micro functions and derive several code complexity and volume metrics, which are then dynamically interpolated into a final effort score. In addition to compatibility with the waterfall software development life cycle methodology, WMFP is also compatible with newer methodologies, such as Six Sigma, Boehm spiral, and Agile methodologies, due to its differential analysis capability made possible by its higher-precision measurement elements.

Measured elements

The WMFP measured elements are several different software metrics deduced from the source code by the WMFP algorithm analysis. They are represented as percentage of the whole unit effort, and are translated into time.

Calculation

The WMFP algorithm uses a three-stage process: function analysis, APPW transform, and result translation.
A dynamic algorithm balances and sums the measured elements and produces a total effort score.
The basic formula is:
This score is then transformed into time by applying a statistical model called average programmer profile weights which is a proprietary successor to COCOMO II 2000 and COSYSMO. The resulting time in programmer work hours is then multiplied by a user defined cost per hour of an average programmer, to produce an average project cost, translated to the user currency.

Downsides

The basic elements of WMFP, when compared to traditional sizing models such as COCOMO, are more complex to a degree that they cannot realistically be evaluated by hand, even on smaller projects, and require a software to analyze the source code. As a result, it can only be used with analogy based cost predictions, and not theoretical educated guesses.