mathematics

Weighted Micro Function Points

Weighted Micro Function Points is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Weighted Micro Function Points rather than just read about it. In short: Weighted Micro Function Points (WMFP) 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 i…

Key takeaways

Weighted Micro Function Points belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Weighted Micro Function Points to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Weighted Micro Function Points from memory before moving on to harder problems.

Reference excerpt

Weighted Micro Function Points (WMFP) 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 (SLOC) 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 (AUP/Lean/XP/DSDM) 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 (project or file) effort, and are translated into time.

Flow complexity (FC) – Measures the complexity of a programs' flow control path in a similar way to the traditional cyclomatic complexity, with higher accuracy by using weights and relations calculation. Object vocabulary (OV) – Measures the quantity of unique information contained by the programs' source code, similar to the traditional Halstead vocabulary with dynamic language compensation. Object conjuration (OC) – Measures the quantity of usage done by information contained by the programs' source code. Arithmetic intricacy (AI) – Measures the complexity of arithmetic calculations across the program Data transfer (DT) – Measures the manipulation of data structures inside the program Code structure (CS) – Measures the amount of effort spent on the program structure such as separating code into classes and functions Inline data (ID) – Measures the amount of effort spent on the embedding hard coded data Comments (CM) – Measures the amount of effort spent on writing program comments

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:

Σ(WiMi)ΠDq M = the source metrics value measured by the WMFP analysis stage W = the adjusted weight assigned to metric M by the APPW model N = the count of metric types i = the current metric type index (iteration) D = the cost drivers factor supplied by the user input q = the current cost driver index (iteration) K = the count of cost drivers This score is then transformed into time by applying a statistical model called average programmer profile weights (APPW) 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.

See also Software sizing Software metric Function points Cyclomatic complexity Halstead complexity measures Software parametric models

References

Worked examples

Example 1 — a first encounter with Weighted Micro Function Points

Start with the simplest possible case. Write down what Weighted Micro Function Points claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Weighted Micro Function Points before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Weighted Micro Function Points ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Weighted Micro Function Points

In research: Weighted Micro Function Points appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Weighted Micro Function Points in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Weighted Micro Function Points is common in secondary-school and first-year university syllabi. It links to neighbouring topics Software metrics, so understanding it makes those chapters shorter.
In everyday life: Look for Weighted Micro Function Points outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Weighted Micro Function Points in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Weighted Micro Function Points means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Weighted Micro Function Points out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Weighted Micro Function Points in simple terms?

Why does Weighted Micro Function Points matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Weighted Micro Function Points?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Weighted Micro Function Points.