Function Point in Object Oriented Metrics

1
Function Point in Object Oriented Metrics

• Evren Ayorak

2
Outline

• Functional Point Analysis
• Object Oriented Approaches
• Kemerer Metrics
• 3D Function Points
• Predictive Object Points
• Object Oriented Fucntion Points
• Class Points

3
Function Points Analysis (FPA)

• What is Function Point Analysis (FPA)?
• It is designed to estimate and measure the time,
  and thereby the cost, of developing new software
  applications and maintaining existing software
  applications.
• It is also useful in comparing and highlighting
  opportunities for productivity improvements in
  software development.
• It was developed by A.J. Albrecht of the IBM
  Corporation in the early 1980s.

4
FPA

• How is Function Point Analysis done?
• Working from the project design specifications,
  the following system functions are measured
  (counted)
• External Inputs (EI)
• External Outputs (EO)
• Files (ILF-internal logical files)
• External Inquires (EQ)
• Interfaces (ELF external logical files)

5
FPA
Inquiries
Boundary
Input
Output
Internal Logical Files
External Interface Files
6
FPA

• EI An elementary process in which data crosses
  the boundary from outside to inside.
• Data input screen
• Another application
• Business data does update ILF
• Control data does not update ILF
• EO An elementary process in which derived data
  passes across the boundary from inside to
  outside.
• Creates reports
• Creates output files sent to other applications
• Created from ILF and ELF

7
FPA

• EQAn elementary process with both input and
  output components that result in data retrieval
  from one or more ILF and ELF
• Sent outside the application boundary
• Input process does not update ILF
• Output side does not contain derived data
• ILF A User identifiable group of logically
  related data that entirely within the
  applications boundary and is maintained through
  External Inputs
• EIF A User identifiable group of logically
  related data that is used for reference purposes
  only.
• Resides entirely outside application
• Maintained by another application
• It is an ILF for another application

8
Unadjusted FP Calculation

• Functional Count by (Complexity)
• Complexity rated by three categories
• Simple
• Average
• Complex
• Each of the 5 functional components has its own
  unique complexity matrix weighting based on level
  of complexity

9
Degrees of Influence (DI)

• Data communications
• Distributed functions
• Perfomance objectives
• Heavily used configuration
• Transaction rate
• On-line data entry
• End-user efficiency
• On-line update
• Complex processing
• Reusability
• Installation ease
• Operational ease
• Multiple sites
• Facilitate change

General characteristics to be ranked by degree of
influence from 0-5 Degree of Influence
Measures Not Present, or no influence
present0 Insignificant Influence1 Moderate
Influence2 Average Influence3 Significant
Influence4 Strong influence, throughout5
10
(No Transcript)
11
FP Calculation

• Complexity Adjustment Factor (CAF)
• CAF 0.65 0.01 x DI
• each degree of influence is worth 1 percent of a
  TCF which can range from 0.65 to 1.35
• Adjusted Function Points (AFP)
• AFP CAF x UFP

12
Complexity of Files Transactions

• Data Element Type (DET)
• A unique user recognizable field from a business
  perspective which participates in a transaction
  or is stored on a logical data file.
• Record Element Type (RET)
• A user recognizable subgroup of data elements
  within an ILF or EIF. (orders types)
• The complexity of an transaction is determined
  by counting the number of logical File Types
  Referenced (FTRs) and the number of DET.

13
Productivity Index

• Function points method can be used for measuring
  the productivity of development activities

14
Critics to FPA

• The calculation of function counts tends to take
  a black box view of the system.
• The user defined function types currently
  established may not be wholly appropriate for
  current technology.
• The classification of the user function types
  into simple, average, and complex appears to be
  oversimplified
• The choice of weights was determined by debate
  and trial.
• The restriction to 14 processing complexity
  factors is not going to be satisfactory for all
  time

15
What about Object Oriented Languages??
16
Chidamber and Kemerer Approach
17
Chidamber and Kemerer Approach

• All the previous measures provide a class-level
  measurement.
• As a consequence, they are more useful for
  productivity analysis once the software has been
  realized but less profitable for effort
  prediction

18
3D Function Points

• Each class is an internal file
• Messages sent across the system boundary as
  transactions
• Require a greater degree of detail in order to
  determine size and consequently make early
  counting more difficult.

19
Predictive Object Points

• Systemlevel measure for effort prediction, which
  adopts a counting scheme analogous to FPs.
• POPs counting is based on the WMC measure applied
  to top level classes, and combines such
  information with the average depth of the
  inheritance tree and the average NOC.
• To compute the WMC value,
• Each method is weighted by a complexity
  determined by the type of method (constructor,
  destructor, modifier, selector, iterator), by the
  number of properties the method affects and the
  number of services it provides to the system.
• Determining complexity of methods is not
  available in early stages of the software
  development process.

20
Object-Oriented Function Points(OOFP)

• Characterized by a mapping of FP concepts
  (logical files and transactions) to OO concepts
  (classes and methods), and by a flexible method
  for handling specific OO concepts like
  inheritance and aggregation.

21
OOFP

• Uses OMT Model
• Object Model
• Static representation of classes and objects
• First to be developed so can be measured early
  stages
• Function Model
• Data Flow Diagrams
• Identifiying and Design some methods in early
  stages
• Dynamic Model
• State machiness
• Use case and Scenarios

22
OOFP

• Central Analogy to map FP to OOFP
• Logical files (collection of user identifiable
  data)? Classes(encapsulates collection of data
  items)
• Transactions ? Methods
• Application Boundary
• External classes encapsulates non-system
  components (external services and reused library
  classes) EIF
• Classes with in the App. Boundary is ILF

23
OOFP

• OOFP Calculation

24
OOFP Process

• Analyze object model and identify units to be
  counted as LF.
• Calculate the complexity of each LF and SR.
• Convert complexity values to numbers
• If LF is reused its OOFP value is calculated
  with a scale factor flt1
• All OOFP values are summed up.

25
OOFP Process
26
OOFP

• Identify LF
• Classes are mapped to LF
• Aggregation and Inheritance is encountered
• Mainly a concern of implementation
• At analysis phase
• Each class is a LF
• Scale factor 1 (Origin of class does not matter)
• At design phase
• Scale factor lt1, reuse makes classes easier to
  develop
• For designer, each class is LF
• For user perspective, it is complicated

27
OOFP

• Ways to Identify LF
• Simple LF
• Sigle Class is a LF
• Composite LF
• Aggregation
• Generalization/Specialization
• Mixed (combine aggregation and generalization)

28
Aggregation
Single
Mix
Generalization
29
OOFP

• Calcution of DET and RETs
• One RET for each ILF/ELF
• Simple LF
• Simple attributes sucs as integer and strings
  counted as DET
• Associations are counted as DET or RET accoring
  to cardinality
• Single valued association is DET
• Multiple valued association is RET

30
OOFP

• Composite LF
• DETs and RETs are counted as in simple LF, except
  for aggregation
• Aggregations act as subgroups in composite LF
• One RET is counted for aggregations
• For each OOFP , weighted vector table for ILF and
  ELF in IFPUG (international function point user
  group)

31
OOFP

• Service Requests
• Concrete methods are only counted once, abstract
  methods are not counted
• Simple Items (analogy to DET)
• simple data items referenced as a argument
• simple global variables referenced by the method
  
• Complex Items (analogy to FTR)
• Complex arguments, objects and complex global
  variables references by the method
• For each OOFP(SR) , weighted vector table for
  EI,EQ in IFPUG

32
OOFP
33
Class Points

• Recasting the ideas underlying the FP analysis
  within the OO paradigm and by suitably combining
  well-known OO measures.
• CP1 Provides an initial size estimation at the
  beginning of the development process.
• CP2 Calculated whendata that are usually
  available later in the development process.
• Similar to POP counting, with the main difference
  lying in the fact that we distinguish among
  categories of classes, rather than classifying
  methods.

34
Class Point Measure Steps

• Identification and Classification of User Classes
• Problem Domain Type(PDT)
• Human Interaction Type (HIT)
• Data Management Type (DMT)
• Task Management Type (TMT)

35
Class Point Measure Steps

• Evaluation of a Class Complexity Level
• Number of External Methods (NEM) measures the
  size of the interface of a class and is
  determined by the number of locally defined
  public methods.
• Number of Services Requested (NSR) provides a
  measure of the interconnection of system
  components.

CP1
36
Class Point Measure Steps
CP2
TUPC
37
Class Point Measure Steps

• Complexity Adjustment Factor
• 4 requirements are added to requirements
• User Adaptivity
• Rapid Prototyping
• Multiuser Interactivity
• Multiple Interfaces
• Final Calculations

38

• THANKS!!