1.2 Programmer Productivity

• A measure of the rate at which individual engineers involved in software development produce software and associated documentation
• Not quality-oriented although quality assurance is a factor in productivity assessment
• Essentially, we want to measure useful functionality produced per time unit

1.2.0.0 Productivity metrics  

• Size related measures based on some output from the software process. This may be lines of delivered source code, object code instructions, etc.
• Function-related measures based on an estimate of the functionality of the delivered software. Function-points are the best known of this type of measure

1.2.0.0 Metric problems  

• Estimating the size of the measure
• Estimating the total number of programmer months which have elapsed
• Estimating contractor productivity (e.g. documentation team) and incorporating this estimate in overall estimate

1.2.1 Lines of code

• What's a line of code?
• What programs should be counted as part of the system?
• Assumes linear relationship between system size and volume of documentation

1.2.1.0 Cross-language comparisons  

• The lower level the language, the more productive the programmer
• The more verbose the programmer, the higher the productivity

1.2.2 Function points

• Based on a combination of program characteristics
  • external inputs and outputs
  • user interactions
  • external interfaces
  • files used by the system

• A weight is associated with each of these
• The function point count is computed by multiplying each raw count by the weight and summing all values

1.2.2.0 Function points  

• Function point count modified by complexity of the project
• FPs can be used to estimate LOC depending on the average number of LOC per FP for a given language
• FPs are very subjective -- depend on the estimator. They cannot be counted automatically
• FPs are language-independent

1.2.2.0 Productivity estimates  

• Real-time embedded systems, 40-160 LOC/P-month
• Systems programs , 150-400 LOC/P-month
• Commercial applications, 200-800 LOC/P-month

1.2.2.0 Factors affecting productivity  

Studies have indicated that the best programmers are over 10× more productive than their peers.

• Application domain experience
• Process quality
• Project size
• Technology support
• Working environment

1.2.2.0 Quality and productivity  

• All metrics based on volume/unit time are flawed because they do not take quality into account
• Productivity may generally be increased at the cost of quality
• It is not clear how productivity/quality metrics are related