1. Software measurement
Ronan Fitzpatrick

2. Quotes
“Measure what is measurable, and make measurable what is not so”.
Galileo Galilei:
“When you can measure what you are speaking about and express it in numbers you know something about it; but when you cannot measure it, when you cannot express it in numbers your knowledge is of a meagre and unsatisfactory kind”.
William Thomson (Lord Kelvin)

3. Quotes “You can’t control what you can’t measure”.
Tom DeMarco
“We must be bold in our attempts at measurement. Just because no one has measured some attribute of interest does not mean that it cannot be measured satisfactorily”.
Fenton and Pfleeger

4. Vocabulary Measure Indirect measure Metric Composite metric
Models for measurement
Generic methods

5. Definition Measurement Compare the similarity with
set of operations having the object of determining a value of a measure. ISO/IEC 15939:2002
[A] Software measurement is a quantified attribute of a characteristic of a software product or the software process. Wikipedia, 2010
Compare the similarity with
A software metric is a measure of some property of a piece of software or its specifications.

6. The purpose of software measurement
Prediction –
To predict complexity
To predict usage
Predictive analytics
Control –
Production hours, cost, security, quality
Assessment –
Usability
Web analytics
Return on Investment

7. Some generic examples Halstead McCall et al. Boehm – CoCoMo
Albrecht – Function Point Analysis
Bevan – Usability metrics.
Nielsen - Heuristics

8. Domain of units
Counting is core to measurement, that is counts of the:
Units of the metric
Units of time
Units of cost

9. Perspectives of software metrics

10. Production Complexity Metrics Cohesion and Coupling
Instability (I): The ratio of efferent coupling (Ce) to total coupling (Ce + Ca) such that I = Ce / (Ce + Ca). This metric is an indicator of the package's resilience to change. The range for this metric is 0 to 1, with I=0 indicating a completely stable package and I=1 indicating a completely instable package.
Robert Cecil Martin’s software package metrics

11. Project Metrics Historical records Producing lines of code
Cost per hour

12. Software Metrics Wikipedia March 2009
Code coverage
Cohesion
Comment density[3]
Coupling
Cyclomatic complexity
Function point analysis
Instruction path length
Program load time
Number of classes and interfaces
Number of lines of customer requirements.
Program Size
Robert Cecil Martin’s software package metrics
Bugs per line of code
Source lines of code or Program Length
Execution Time

13. Process Metrics

14. Product Metrics Suitability Installability Functionality Adaptability
Ease-of-use
Learnability
Interoperability
Reliability
Safety
Security
Correctness
Efficiency
Portability
Testability
Maintainability
Re-usability
H-CI
quality
Technical
quality

15. Top 10 Call Centre Metrics and What they mean to you Canadian Marketing Association 2010
Examples
Manage your workforce
Control costs effectively
Continuously enhance the client experience
Ensure the contact centre is a contributor to the overall profitability of an organization

16. Top 10 Call Centre Metrics and What they mean to you Canadian Marketing Association 2010
Specific examples
1. Abandon Rate
Abandon rate is the number of calls that hang-up before connecting to an agent. This number does not include those calls that receive a busy signal.
Calculation:
Abandoned Calls / Total Incoming calls
Compare this to web analytics e.g., converted customers

17. Top 10 Call Centre Metrics and What they mean to you Canadian Marketing Association 2010
Specific examples
10. Call Quality
Call Quality is a standard scoring/rating system that contact centres use to determine how well an agent deals with the customers. There are no industry standards for monitoring quality, but there will usually be a list of criteria that an agent must cover during a call. This includes, but is not limited to, how the agent answers the call, how they navigate the caller to a resolution, and how they end the call.
Calculation:
Number of Criteria Met / Number of Total Criteria

18. Procurement Metrics ROI.
The total amount of revenue brought in by the software minus the total amount of costs to produce the software.

19. Operations and Maintenance Metrics
SLA Metrics
Mean Time Metrics
Between failures; to recover; to repair

20. Usability Metrics
Effectiveness
Productivity
Safety
Satisfaction

21. Other Measures Total Ownership Cost Cost of Quality
Cost of program development
Cost of program maintenance
Cost of Quality
Percent of total time spent in appraisal (walkthroughs, reviews, inspections)
Percent of total time spent in rework (compile and re-test)
Requirements Satisfaction
Number of acceptance test defects in User acceptance
Acceptance test defects per KLOC

22. Desirable characteristics of metrics
Metrics should be
reliable (free from random error)
repeatable (same entity, same environment, same visitors and same evaluator)
reproducible (same entity, same environment, same visitors but different evaluator)
available (constraint conditions)
indicative of improvement (of the entity quality)
correct (objective, impartial and precise) and meaningful (about the entity behaviour or quality characteristics).

23. Measurement and International Standards (typical)
BS ISO/IEC 20968:2002  Software engineering. Mk II function point analysis. Counting practices manual
BS ISO/IEC to6:1998  Information technology. Software measurement. Functional size measurement.
BS ISO/IEC 15939:2002  Software engineering. Software measurement process
PD ISO/IEC TR :2004 Software engineering. Product quality. Part 4 – Quality in Use metrics

24. Generic Metric validation methodology
Fenton and Pfleeger
Conception
Design
Preparation
Execution
Analysis
Documentation and decision making
IEEE std 1061
Identify the quality factors
Identify the metrics sample
Perform a statistical analysis
Document the results
Revalidate the metrics
Evaluate the stability of the environment

25. Validating software metrics
Analysis using statistical methods
correlation, tracking changes capability, consistency, predictability, discriminative power and reliability.

26. Conclusion An alternative school of thought suggests that:
The emphases on measurement is unkind to experts from other disciplines, for example, Darwin, Maslow, Van Gough.