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Function Point Analysis

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Function Point Analysis How to use this document...................................................................................... 1 1 Introduction ......................................................................................................... 2 2 Measures Derived from FPA ............................................................................... 2 3 Different FPA Methods ....................................................................................... 2 4 The advantages of using FPA............................................................................. 3 5 The disadvantages of, or problems using, FPA .................................................. 3 6 Developed v Delivered FPs................................................................................. 4 7 The use of FPs in estimating effort and cost ...................................................... 5 8 Function Point counting procedure ..................................................................... 6 8.1 IFPUG Method ............................................................................................. 6 8.1.1 Complexity Matrices............................................................................... 7 8.1.2 Complexity/UFP Contribution................................................................. 7 8.1.3 Overall FP Count ................................................................................... 8 8.1.4 IFPUG FPA Process Summary.............................................................. 8 8.2 MK II Method ................................................................................................ 9 8.2.1 MK II FPA Process Summary .............................................................. 10 9 The relative advantages of the MK II and IFPUG approaches.......................... 10 10 IFPUG Examples ............................................................................................ 11 10.1 IFPUG Example 1 (FPA terminology) ................................................... 11 10.2 IFPUG Example 2 (data entry screen) .................................................. 12 10.3 ‘Add’ Transaction in MK II FPA ............................................................. 13 10.4 IFPUG Example 3 (simple customer information application) .............. 13 10.5 IFPUG Example 4 (simple sales order application) .............................. 13 10.6 IFPUG Example 5 (complex sales order application) ........................... 14 10.7 IFPUG Example 6 (estimating effort and schedule).............................. 14

How to use this document
This document has a wide target audience.       A brief introduction to FPs and what they can be used for - sections 1 and 2. Advantages and disadvantages of FPs - sections 3 to 7 inclusive. Differences between IFPUG and Mark II - sections 8 and 9. Details of how FPA is conducted - sections 8 and 10. How to use FPs for estimating - read sections 7 and 10.5. Those wanting to learn how to conduct FPA and/or make use of FPs for estimating and benchmarking should read all sections.

Software Estimating

FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited.

Page 1

1 Introduction
Function Point Analysis (FPA) can be used to measure the size of the end-user functionality requested and delivered by an application or system. FPA can be used to size existing applications, new applications and enhancements to existing applications. FPA measures the size of an application by counting business transactions (inputs, outputs and processing) and business information (logical files) as specified by business requirements and recognisable by the end-user. FPA takes the logical user view and not the physical or technical view. A simple application that allows a user to maintain customer information with basic functionality to add, delete and amend customer records, query and print off a particular customer’s details and query and print off a list of all customers is approximately 30 FPs. Of course in real life most applications are far more complex than this. If this same application also had facilities for maintaining products, customer orders, supplier details and salesperson’s details, and conducting sales and products analysis, the size would be 400 - 500 FPs.

2 Measures Derived from FPA
The size of applications in FPs are used as the normalising factor to derive various measurements such as the following:       Productivity Delivery Support Quality Cost Portfolio size FPs per person month of development effort (FPs/pm) FPs per elapsed calendar month (FPs/cm) FPs supported/maintained per person per year (FPs/person) Valid defects during live operation per FP (defects/FP) Development cost per FP (£/FP) The size of the application assets of an organisation in FPs

These measures can then be used for estimating, comparative analysis and for setting baselines and improvement targets.

3 Different FPA Methods
There are many FPA variations, but there are only two in general use, both of which are described in this document. The first is version 4.1.1 of the International Function Point User Group (IFPUG) referred to as the Albrecht approach. The other less widely used method is version 1.3.1 of the UKSMA (UK Software Metrics Association) referred to as the Mark II approach. (UKSMA was formally known as the UFPUG (UK Function Point User Group)). COSMIC-FFP (Full Function Points) was introduced in 1997, but is not as yet widely used. It is claimed to be an improvement on all previous functional size measurement methods.
Software Estimating FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited. Page 2

4 The advantages of using FPA
The main benefits of using FPA over other measures of application size, such as lines of code (LOC), are that: 1. It is based on business requirements and is meaningful to business users. 2. It can be used early in the lifecycle and for estimating throughout the lifecycle. 3. It is independent of tools, methods and technologies e.g. it has been proven for OO, Client/Server, Internet, UML, DSDM and other technologies. 4. It is mechanistic and repeatable, giving relatively accurate, consistent, results. 5. FPA can be carried out from a combination of a detailed requirements document and a detailed logical design document. 6. It is an internationally recognised technique and complies with the requirements of the ISO Standard on Functional Size Measurement (ISO/IEC 14143-1:1998).

5 The disadvantages of, or problems using, FPA
The main disadvantages or problems with FPA are that: 1. The counting rules are sometimes open to interpretation. 2. Error margins can be large for small applications. 3. Current FPA methods have a size component and a complexity component (Technical Complexity Adjustment (TCA) or a Value Adjustment Factor Adjustment (VAF)). Evaluating the complexity component is very subjective and the complexity component merely has the effect of adjusting the size component by plus or minus 35%. The complexity component has fallen in to disrepute, particularly as FPs are supposed to be purely a measure of functional size. The ISO Standard on Functional Size Measurement (ISO/IEC 14143-1:1998) excludes the use of the TCA. 4. Some business functions can consume substantial effort, time and cost yet this is not reflected in the FP count e.g. Help screens and menus. (The use of help screens improves usability and thus delivers a higher quality application). 5. Large ‘chunks’ of functionality could be deleted easily yet a very small amount of ‘work’ can be required e.g. removing an item from a menu would remove the users’ ability to use a function that delivers, say 100 FPs, yet the code for the function has not been physically removed. Care needs to be taken when sizing projects where functionality is deleted and/or amended. 6. If documentation (requirements and logical design) is not kept up to date e.g. change requests that have resulted in scope changes, then the FP analyst will need to spend substantial time reviewing such changes to the scope, as well as spending time with the development team and users in order to come up with an accurate FP count. More often than not, the development team has been disbanded and/or documentation cannot be found after project delivery. 7. It is claimed by some that experienced FP analysts could count up to 100 FPs in an hour. (This of course will depend upon experience of FPA, experience of the business domain of the application, quality of documentation, etc.). However, it is the author’s experience that it is not necessarily the counting of FPs but documenting the count that takes up most of the time.
Software Estimating FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited. Page 3

6 Developed v Delivered FPs
It is important to realise the distinction between developed FPs and delivered FPs and that the productivity figure based on FPs delivered per staff month and FPs developed per staff month can be very different. Application A, of say 1000 FPs, may consist purely of new functionality that has been developed from scratch by an organisation. In this case, the number of developed FPs and delivered FPs are the same i.e.1000 FPs. Application B, again of 1000 FPs, may consist of 800 FPs of a commercially off the shelf (COTS) package with 200 FPs of additional functionality developed by the organisation. In this case, the number of developed FPs is 200 and delivered FPs is 1000. The author has come across many examples of ‘so called’ high productivity projects e.g. 400 FPs per staff month, where the project was actually ‘re-using’ substantial functionality (e.g. 50-70%) from an existing application. In such case, the metrics from the project should state the productivity figure based on FPs delivered per staff month and FPs developed per staff month. An organisation that is able to use existing software (whether internal or external COTS) and add missing functionality should be able to ‘deliver’ the application more cheaply than another organisation that has to ‘develop’ the application from scratch. This can give software houses an edge when bidding for work i.e. if they have already delivered something for another client, they can re-use the application and make the additional changes rather than starting from scratch. (NB This is not always the case as there are numerous cases of COTS integration projects that have gone badly wrong, mainly due to a mis-match between what the COTS application offered and what the business requirements were, which then result in far more code/functionality having to be written from scratch.)

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FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited.

Page 4

7 The use of FPs in estimating effort and cost
It is important to understand that FPs only provide the ‘size’ component of an estimating model. Most estimating models are of the form: Effort, Cost = f (Personnel, Product, Process, Platform, Politics)      Personnel - experience, skills and capabilities of staff and organisation Product – size, quality, functionality, performance and reliability. Process –tools, techniques, methods, standards and automation used Platform – operating system, hardware, C/S, communications Politics – fixed delivery dates, estimating to ‘win’ or to a target cost

Thus size, albeit important, is only one factor that must be taken into consideration when estimating effort and cost.

Software Estimating

FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited.

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8 Function Point counting procedure
This section does not attempt to give an in-depth description of FP Counting. It provides a high level overview and a basic introduction to the terminology. The IFPUG and/or Mk II Counting Practices Manual can be referenced for more details. The two methods are very similar, but can result in quite different FP sizes. 8.1 IFPUG Method An IFPUG FP Count has six components, the first five representing specific functionality and the sixth general functionality, as specified by the users’ requirements. These are: 1. External Inputs (EI) e.g. on-line data entry or a file from another system. 2. External Outputs (EO) e.g. reports, screen displays and files sent to other systems. 3. Enquiries (EQ) e.g. retrieving a customer’s address and postcode. 4. Internal Logical Files (ILF) e.g. holding customer details. 5. External Interface Files (EIF) e.g. referencing a postcode database external to the application to retrieve an address for the postcode. 6. Fourteen General System Characteristics (GSC) used to obtain a Value Adjustment Factor Adjustment (VAF) factor of between 0.65 and 1.35: 1. Data Communications 2. Distributed Data Processing 3. Performance 4. Heavily Used Configuration 5. Transaction Rate 6. On-Line Data Entry 7. End-User Efficiency 8. On-Line Update 9. Complex Processing 10.Reusability 11.Installation Ease 12.Operational Ease 13.Multiple Sites 14.Facilitate Change Each of the 14 GSCs are rated on a scale of 0-5, giving a maximum possible total GSC score of 70. The Value Adjustment Factor (VAF) is derived from the formula VAF = 0.65 + (0.01 x GSC score).

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8.1.1 Complexity Matrices The EI, EO and EQ components are categorised into Low (L), Medium (M) and High (H) complexity transactions according to the number of Data Elements (DET) and ILF/EIF Files Referenced (FTR). The ILFs and EIFs are categorised into Low (L), Medium (M) and High (H) complexity files according to the number of Data Elements (DET) and Record Types (RET) in the files. 1-4 DETs 0-1 FTRs L 2 FTRs L >2 FTRs M EI 5-15 DETs L M H >15 DETs M H H

EOs and EQs share the same matrix as shown below. 1-5 DETs 0-1 FTRs L 2-3 FTRs L >3 FTRs M EO/EQ 6-19 DETs L M H >19 DETs M H H

ILF and EIFs share the same complexity matrix as shown below. 1-19 DETs 1 RETs L 2-5 RETs L >5 RETs M ILF/EIF 20-50 DETs L M H >50 DETs M H H

The table below can then be used to determine the UFPs for the EI, EO, EQ, ILF and EIF components in the system. 8.1.2 Complexity/UFP Contribution L 3 4 3 7 5 M 4 5 4 10 7 H 6 7 6 15 10

EI EO EQ ILF EIF

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8.1.3 Overall FP Count The Unadjusted Function Point (UFP) count is determined from the sum of all EI, EO, EQ, ILF and EIF components by categorising them as Low, Medium or High complexity and applying weighting factors based on the complexities. The Adjusted Function Point (AFP) count is determined by multiplying the UFP by the VAF i.e. AFP = UFP x VAF. 8.1.4 IFPUG FPA Process Summary The process can be summarised as follows: 1. Identify the System Boundary 2. Identify all of the FP components 3. Classify each component as EI, EO, EQ, ILF and EIFs and categorise as L, M or H 4. Total up the number of components in each class/category combination 5. Multiply the totals for each class/category combination by the appropriate factor from the complexity/UFP contribution table 6. Add up all the UFP contributions from each of the class/category combinations giving the total UFP value 7. Complete the GSC questionnaire and determine the VAF factor 8. Multiply the UFP by the VAF to give the Adjusted Function Points(AFP) AFP = UFP x VAF

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FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited.

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8.2 MK II Method The MK II method does not classify transactions and files into EI, EO, EQ, ILF and EIF and thus there are no complexity matrices. All logical business transactions are identified and for each transaction the following elements are counted: Input Data Elements Entities Referenced Output Data Elements NI NE NO

The contributions from each individual transaction are then totalled giving NI, NE and NO. The UFPs are then calculated as follows: UFPs = (NI x W I) + (NE x W E) + (NO x W O) where W I = Weight for an input data element W E = Weight for an entity referenced W O = Weight for an output data element The current ‘Industry Average’ weights are W I = 0.58 W E = 1.66 W O = 0.26 MK II has a total of 20 General System Characteristics, as opposed to the 14 in the IFPUG approach. The additional ones are as follows: 15.Requirements of other applications 16.Security, Privacy, Auditability 17.User Training Needs 18.Direct Use by Third Parties 19.Documentation 20.Client Defined Characteristics

The Technical Complexity Adjustment (TCA) factor is derived from the formula TCA = 0.65 + (0.005 x GSC score), having a range between 0.65 and 1.15. (Note that the IFPUG method has a TCA range between 0.65 and 1.35).

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FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited.

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8.2.1 MK II FPA Process Summary The process can be summarised as follows: 1. Identify the System Boundary 2. Identify all of the logical transactions 3. Count the input data elements, entities referenced and output data elements for each transaction 4. Total up the input, entities and outputs for all transactions 5. Multiply the input, entity and output totals by their corresponding weights and calculate the sum giving the total UFP value 6. Complete the GSC questionnaire and determine the TCA factor 7. Multiply the UFP by the TCA to give the Adjusted Function Points(AFP) AFP = UFP x TCA

9 The relative advantages of the MK II and IFPUG approaches
1. The IFPUG approach does not generate a continuous measure i.e. it limits the FPs attributed to a component once it has reached maximum complexity. Thus two high complexity components may be attributed the same number of FPs although one may be far more complex than the other. The MK II approach on the other hand does generate a continuous measure and reflects more accurately the level of functionality offered by an application. 2. MK II FPA is much easier and quicker to perform, particularly as there are no complexity matrices within Mark II. 3. The IFPUG approach is used world-wide, whereas MK II is used by a relatively much smaller number of organisations, predominantly in the UK. Consequently there is much more IFPUG benchmarking data as opposed to MK II data.

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FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited.

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10 IFPUG Examples
10.1 IFPUG Example 1 (FPA terminology) In the ‘Library Application’ below, the librarian can ‘Add a new borrower’ (EI) and updates the ‘Borrower Information’ file (ILF). During this process the post code is used to retrieve the address from the ‘Addresses and Post Codes’ file (EIF). A borrower can ‘Request and display books on loan’ (EQ). This simply looks at any books the borrower has out on loan and displays the book information and the date the book was taken out and when it is due back. The chief librarian can request a ‘Report of all books overdue by one month’ (EO). This request produces a list of books for each person that has any books overdue by at least a month. In addition to the borrower’s details and the details of each book overdue, the request also calculates the number of days that each book is overdue and the total number of books overdue.

Borrower

Request and display books on loan (EQ)

Library Application Librarian New borrower Information (EI) Borrower Information (ILF) Address

Post Code Application Addresses & Post Codes (EIF)

Boundary Report of all books over-due by at least one month (EO)

Chief Librarian

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10.2 IFPUG Example 2 (data entry screen)

Maintain Borrower Information

Title Surname Forenames Borrower Number Date of Birth House Number House Name Street City/Town County Post Code Telephone Number

Mr Smith Michael Richard PQ123456 20-08-1981 102 Heath Lodge Heath Road Petersfield Hampshire GU31 4QT 01730-299817 Age 20

Suspended

Error and other messages go here F1 - Add F2 - Update F3 - Display F4 - Delete F5 - Retrieve Address

Using the example above, the ‘Add’ function is an EI (External Input). The following 11 DETs can be recognised: 1. 2. 3. 4. 5. 6. Title Name (fornames - surname combination) Borrower number Date of Birth Age Address (house number, house name, street, city/town and county combination) 7. Post Code 8. Telephone number 9. Suspension flag 10. Error message field 11. F1 command key The Borrower Information ILF is maintained by this EI transaction. Thus with 1 ILF referenced, and 11 DETs, this is a Low Complexity EI, which makes a contribution of 3FPs to the overall size of the application.

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FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited.

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10.3 ‘Add’ Transaction in MK II FPA In Mark II the 'Add' function has  9 Input Data Elements (title, name, borrower number, date of birth, age, address, post code, telephone number and suspension flag)  1 Entity Referenced (borrower information)  1 Output Data Element (error message field) Note that the F1 command key is ignored under Mark II rules. UFPs = 9 x 0.58 + 1 x 1.66 + 1 x 0.26 = 7.14

10.4 IFPUG Example 3 (simple customer information application) Consider a simple application that allows a user to maintain a customer database. Assume all FP components are of low complexity. The FP contribution of each component is shown in brackets:  The customer database - one ILF (7)  The ability to add, delete and amend customer information – three EIs (3 x 3)  The ability to query customer details on screen – one EQ (3)  The ability to list all customers on screen – one EQ (3)  The ability to print a customer’s details – one EQ (3)  The ability to print a list of all customers – one EQ (3) This gives a total contribution of 30 FPs. However, the four query, print and list customer detail transactions s might use derived information and therefore be EOs contributing 4 FPs each rather than 3 FPs. Therefore assuming 30 FPs is reasonable.

10.5 IFPUG Example 4 (simple sales order application) If the application in example 2 also had facilities for maintaining customer accounts, products, customer orders, supplier details and sales person’s details, then the FP count would be approximately 180 FPs (6 x 30). However, the assumption that all FP components are of low complexity may no longer be valid as there are now 6 ILFs and probably more DETs per transaction and for each ILF. A figure of 250 FPs may be more appropriate.

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FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited.

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10.6 IFPUG Example 5 (complex sales order application) Following on from example 2, additional facilities would include  The ability to conduct analysis such as the most profitable products, the most profitable sales regions, the most profitable sale person, sales forecasts, stock levels etc. The requirements for such facilities can easily result in 20-40 additional reports.  Interfaces to other systems e.g. HR system to check and/or retrieve sales person’s details, suppliers systems to get product details, address files to determine addresses from post codes also adds complexity and additional functionality.  Batch processing and processes triggered by events or automatic weekly/monthly reports are additional functionality to be found in such an application.  Other ‘states’ can also exist for customers, suppliers and sales people. For example, a customer’s account can be suspended if they have not paid their bills and of course, re-activated. This will probably raise the functionality to around 600 FPs.

10.7 IFPUG Example 6 (estimating effort and schedule) This example demonstrates how we could use FPs to do a high level estimate of the effort and schedule required for a 600 FP project. Assuming a productivity of 15 FPs per staff month it will require 40 (600/15) staff months of effort to deliver a 600 FP application. This equates to a team of 4 staff over 10 months (40/10). (NB This is a first cut and very simplistic approach. For example, this assumes that the team have the right skills, experience etc, are available when required and also that they are fully productive through out each work day.)

Software Estimating

FPA (version 1.4) Author K S Banga This document is Copyright © of KSB Associates Limited.

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