EXTENDED FUNCTION POINT (EFP) METRICS

Extended Function Point (EFP) Metrics

The traditional Function Point (FP) metric has been enhanced to support additional measures, including:

• Feature Points
• 3D Function Points

Feature Points

Feature Points are an extension of the Function Point method, designed to handle systems and engineering software applications with high algorithmic complexity. They are especially useful for domains such as:

• Real-time systems with strict time constraints
• Embedded systems
• Other software where algorithmic processing plays a major role

In this method:

• Feature Points are calculated by counting information domain values.
• A single weighting factor is applied to these values.
• An additional measurement parameter—ALGORITHM—is introduced, which is not part of the standard FP method.

The computation of Feature Points follows a defined table that incorporates this new parameter.

Feature Point Computation Table

Feature Point Calculation

The Feature Point (FP) is calculated using the formula:

                                               14   

 𝐹𝑃=Count-total×[0.65+0.01×∑ fi]​

                                              𝑖=1

or simply:

               FP=Count-total×CAF

where:

• Count-total is obtained from the computation table.
• CAF (Complexity Adjustment Factor) is calculated a:

                            14

CAF=0.65+0.01×∑ fi

                           𝑖=1

∑fi  is the sum of responses to 14 standard complexity questions (usually provided in assessments).

Key points:

• Both Function Points and Feature Points measure system functionality.
• For complex, real-time applications, Feature Point values are typically 20–35% higher than the corresponding Function Point count.

3D Function Points

The 3D Function Point approach measures software functionality across three dimensions:

1.Data Dimension – Evaluated similarly to traditional FP, counting inputs, outputs, inquiries, external interfaces, and files.

2. Functional Dimension – Adds the concept of Transformation, i.e., the sequence of steps that converts input into output.

3.Control Dimension – Adds the concept of Transition, defined as the number of state changes in the system. A state represents an externally observable mode of operation.

For the average case, fi = 3.

Therefore,

                 14

                 ∑ f𝑖 =14×3

                𝑖=1   

The Function Point (FP) is calculated as:

FP=Count-total × [0.65+0.01×∑(fi)]

​

             = 618×[0.65+0.01×42]=618×(0.65+0.42)=618×1.07=661.26

The Feature Point is computed as:

Feature Point =  (32×4+60×5+24×4+80+14)×1.07+{12×15×1.07}

                                       =853.86

Example – 3D Function Point Calculation

$$Given\; (Embedded\; System):Internal\; data\; structures\; =\; 6External\; data\; structures\; =\; 3User\; inputs\; =\; 12User\; outputs\; =\; 60User\; inquiries\; =\; 9External\; interfaces\; =\; 3Transformations\; =\; 36Transitions\; =\; 24Complexity:\; HighApproach:For\; embedded\; systems,\; when\; complexity\; is\; high,\; the\; weighting\; factor\; for\; each\; parameter\; is\; taken\; from\; the\; complex\; category\; of\; the\; 3D\; Function\; Point\; weighting\; table.\; The\; table\; is\; prepared\; first,\; mapping\; each\; count\; to\; its\; corresponding\; weight,\; and\; then\; the\; totals\; are\; computed.$$