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Description Logical data group Brief Definition A group of logical attributes a conceptual entity that is functionally significant as a whole for the user An internal logical file or external interface file (ILF, EIF in IFPUG), or permanent objects of interest (OOI in COSMIC) A set of two or more logical data groups Its size is evaluated based on the (estimated) amount of included logical data groups The smallest software process with autonomy and significant characteristics, allowing the user to achieve a unitary business objective It corresponds to an external input, external output, or external query (IFPUG), or to any functional process (COSMIC) A particular case of a general process: a set of most frequent transactions on a logical data group (or a small set of LDGs) Usually denoted as Management of [LDG/OOI] It can be of three flavors : CRUD (create, retrieve, update, and delete), CRUDL (CRUD + elementary list), or CRUDL + Report (totals or other derived data) A set of two or more average FPs It can be likened to an operational subsystem, which provides an organized whole response to a specific application goal Its size is evaluated based on the (estimated) quantity of included FPs A set of two or more average general processes It can be likened to a relevant subsystem, or even to a bounded application, of an overall information system Its size is evaluated based on the (estimated) quantity of included general processes
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Table 5-4
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In the COSMIC method3, objects of interest are identified, but not assigned any numerical values
COSMIC Transactional Functions
Base functional processes correspond to the functional processes of the standard COSMIC method Typical functional processes and
Refer to 20
5:
p r a c t i c a l S o f t w a r e S i z e a p p r o x i m a t i o n te c h n i q u e s
higher-level aggregations (general and macro functional processes) are also defined
COSMIC Ranges and Numerical Assignments
Each E&QC FFP (full function point) element is assigned three estimated values, that is, minimum, likely, and maximum COSMIC function points Tables 5-5 and 5-6 show component ranges and numerical assignments for the business application software case (for example, MIS [management information system]) and the realtime case
Type BFP
Level/Complexity Low (2 5 DM) Average (5 8 DM) High (8 14 DM) Very High (14+ DM)
Emin 20 50 80 140 144 252 420 200 400 550
Elikely 36 63 105 180 180 300 500 500 800 1100
Emax 50 80 140 250 252 420 650 1000 1600 2100
Low CRUD/L Average CRUD/L High CRUD/L
Low (6 10 BFPs) Average (10 15 BFPs) High (15 20 BFPs)
Table 5-5
Early & Quick COSMIC Ranges for Business Application Software
Type BFP
Level / Complexity Low (2 3 DM) Average (3 5 DM) High (5 10 DM) Very High (10+ DM)
Emin 20 30 50 100 150 250 380
Elikely 25 40 75 150 320 380 700
Emax 30 50 100 200 750 1100 1500
Low (6 10 BFPs) Average (10 15 BFPs) High (15 20 BFPs)
Table 5-6
Early & Quick COSMIC Ranges for Real-Time Software
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Some Other Examples of Extrapolative Approaches to Size Approximation
Other examples of extrapolative approaches to size approximation are FP Prognosis FP = 73 #Inputs + Outputs + 56 FP = 35 ILFs + 15 EIFs FP = 4 EIs + 5 EOs + 4 EQs + NESMA Indicative FP
Lite or Quick & Early FP 10 ILFs + 7 EIFs
Weighted Averages FP = 43 EIs + 54 EOs + 38 EQs + 74 ILFs + 55 EIFs Thirties Rule of Thumb One logical file equals thirty something unadjusted FPs So for an application that has about 40 logical files, a very rough size can be obtained as follows: 40 35 = 1,400 FP This sort of rough estimate should have an allowance of plus or minus 30 percent or more
Using Functional Size to Estimate Project Effort and Duration
If functional size is being used to estimate the effort and duration of a development or enhancement project, try and get the most accurate size that you can within the limitations of information and resources available Use several size approximation techniques to refine the result before using its values in the project estimation exercise, since an error in the size compounds the errors in the estimates of effort and duration
Note When using approximated sizes for project estimation purposes, be
aware of the need to validate, refine, and eventually translate your size estimates into exact measures, in order to avoid spreading uncertainty from the size approximation to the effort and duration estimates
Once you have established your project s software functional size expressed as a number of functional size measurement method units (for example, function points) you can use the ISBSG data to estimate the likely project effort and duration The following chapters present estimating techniques that allow you to do this
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