Software Metric

Since quantitative methods have proved so powerful in the other sciences, Computer Science practitioners and theoreticians have worked hard to bring similar approaches to software development. Tom DeMarco stated, "You cannot control what you cannot measure" in ''Controlling Software Projects: Management, Measurement and Estimation''.

COMMON SOFTWARE METRICS

Common software metrics include:

order of growth (See Analysis Of Algorithms in terms of Asymptotic Analysis and Big O Notation )

Source Lines Of Code

Cyclomatic Complexity

Function Point s

Bugs Per Line Of Code

Code Coverage

number of lines of customer requirements.

number of classes and interfaces

Robert Cecil Martin 's Software Package Metrics

Cohesion

Coupling (computer Science)

LIMITATIONS

The assessment of "how much" software there is in a program, especially making prediction of such prior to the detail design, is very difficult to satisfactorily define or measure. The practical utility of software metrics has thus been limited to narrow domains where the measurement process can be stabilized.

Management methodologies such as the Capability Maturity Model or ISO 9000 have therefore focused more on Process Metrics which assist in monitoring and controlling the processes that produce the software.

Examples of process metrics affecting software:

Number of times the program failed to rebuild overnight

Number of defects introduced per developer hour

Number of changes to requirements

Hours of programmer time available and spent per week

Number of patch releases required after first product ship

CRITICISMS

Potential weaknesses and criticism of the metrics approach:

Unethical: It is said to be unethical to reduce a persons performance to a small number of numerical variables and then judge them by that measure. A supervisor may assign the most talented programmer to the hardest tasks on the project; they then take the longest and generate the most defects. Uninformed managers overseeing the project might then judge the programmer as performing poorly without consulting the supervisor who has the full picture.

Demeaning: 'Management by numbers' without regard to the quality of experience of the employees, instead of 'managing people'.

Skewing: The measurement process is biased by the act of measurement by employees seeking to maximise management's perception of their performance. For example, if lines of code is used to judge performance, then employees will write as many separate lines of code as possible, and if they find a way to shorten their code, they may not use it.

Inaccurate: No known metrics are both meaningful and accurate. Lines of code measure exactly what is typed, but not of the difficulty of the problem. Function points were developed to better measure the complexity of the code or specification, but they require personal judgement to use well. Different estimators will produce different results. This makes function points hard to use fairly and unlikely to be used well by everyone.

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Software Metric