• E is the Effort in staff months
• a and b are coefficients to be determined
• KLOC is thousands of lines of code (NOTE: cocomoco uses LOC, do not enter kloc values)

With cocomoco, to calculate the effort for 100000 lines of code using the organic model: print(cocomoco.calculate(100000).effort) -> 302.1 (person-months).

• DTime is time for development
• c and d are constants to be determined
• E is the effort

With cocomoco, to calculate the development time for 100000 lines of code using the organic model: print(cocomoco.calculate(100000).dtime) -> 21.9 (months).

Average staff size can be calculated in the following way:

Remember: Effort == Staff Months & Dtime == Months -> divide both cancel the months and staff remains!

With cocomoco, to calculate the average staff size for 100000 lines of code using the organic model: print(cocomoco.calculate(100000).staff) -> 14 number of average staff size.

How many lines of code per staff month can be calculated via:

With cocomoco, to calculate the staff productivity for 100000 lines of code using the organic model: print(cocomoco.calculate(100000).sloc_per_staff_month) -> 331 lines of code per staff member and month.

Models define the coefficients a and b for typical projects.

• Organic
  • 2-50 KLOC
  • stable dev
  • little innovation
• Semidetached
  • 50-300 KLOC
  • average abilities
  • medium time-constraints
• Embedded
  • larger 300 KLOC
  • large project team
  • complex
  • innovative
  • severe constraints

Organic:

• a: 2.4
• b: 1.05
• c: 2.5
• d: 0.38

Semidetached:

• a: 3.0
• b: 1.12
• c: 2.5
• d: 0.35

Embedded:

• a: 3.6
• b: 1.2
• c: 2.5
• d: 0.32

Intermediate cocomo introduces cost drivers to the standard models.

• Product Attributes
  • RELY Required Software Reliability
  • DATA Data Base Size
  • CPLX Product Complexity
• Computer Attributes
  • TIME Execution Time Constraint
  • STOR Main Storage Constraint
  • VIRT Virtual Machine Volatility
  • TURN Computer Turnaround Time
• Personnel Attributes
  • ACAP Analyst Capability
  • AEXP Application Experience
  • PCAP Programming Capability
  • VEXP Virtual Machine Experience
  • LEXP Programming Language Experience
• Project Attributes
  • MODP Modern Programming Practices
  • TOOL Use of Software Tools
  • SCED Required Development Schedule

Cocomo commes with a predefined set of values in the following categories: very low, low, nominal, high, very high, extra high.

Following charts are created via python3 -m cocomoco --demo-mode:

Simple install this module via pip (pip for Python 2 is also supported)

pip3 install --user cocomoco

import cocomoco

result = cocomoco.calculate(100000)
print(result)

$ python3 -m cocomoco --sloc <number> [--model <modelname>]

• Alan Caine, Constructive Cost Model COCOMO, https://cs.uwaterloo.ca/~apidduck/se362/Lectures/cocomo.pdf