1. Software Engineering: A Practitioner’s Approach Chapter 25 Process and Project Metrics copyright © 1996, 2001, 2005 R.S. Pressman & Associates, Inc. For University Use Only May be reproduced ONLY for student use at the university level when used in conjunction with Software Engineering: A Practitioner's Approach. Any other reproduction or use is expressly prohibited.

2. Until you can measure something and express it in numbers, you have only the beginning of understanding.
- Lord Kelvin

3. Until you can measure something and express it in numbers, you have only the beginning of understanding.
- Lord Kelvin
Non-software metrics you use everyday:
- Gas tank scale
- Speedometer
- Thermostat in your house
- Battery monitor in your laptop
What would happen if instead these were not numeric?
What other examples do you have?
Gas Tank
☐A Lot
☐Some
A little

4. Until you can measure something and express it in numbers, you have only the beginning of understanding.
- Lord Kelvin
The problem is non-numeric measurements are subjective… they mean different things to different people. Numbers are objective… they mean the same thing to everyone!
When your friend says “oh yeah, John/Jane Doe is super attractive, you should go out with him/her”… that is a subjective measurement… so, you ask “Send me a photo”. Why? Because the subjective term “super attractive” has vastly different meanings for different people!
Coming up: Metrics for software

5. Metrics for software
When asked to measure something, always try to determine an objective measurement. If not possible, try to get as close as you can!
Coming up: A Good Manager Measures

6. A Good Manager Measures
process
process metrics
project metrics
measurement
product metrics
product
What do we
use as a
basis?
• size?
product measurements are things like code complexity, number of function, classes, coupling, etc…
process/project metrics are how well your process works
• function?
Coming up: We need a basis to say 20 defects per X lines of code. Why is this important?

7. We need a basis to say 20 defects per X lines of code
We need a basis to say 20 defects per X lines of code. Why is this important?
A Because lines of code equals cost
B We want our metrics to be valid across projects of many sizes
C Because you just caused me to die in God of War III… stop asking these questions!
D Because this helps up understand how big our program is
Coming up: Why Do We Measure?

8. Why Do We Measure? assess the status of an ongoing project
track potential risks
uncover problem areas before they go “critical,”
adjust work flow or tasks,
evaluate the project team’s ability to control quality of software work products.
Coming up: Process versus Project Metrics

9. Process versus Project Metrics
Process Metrics - Measure the process to help update and change the process as needed across many projects
Project Metrics - Measure specific aspects of a single project to improve the decisions made on that project
Process may aggregate % delay of deliverables (schedule conformance) across many projects to determine how good our scheduling/planning process is
Project would use the same measurement to make project level decisions
Frequently the same measurements can be used
for both purposes
Coming up: Process Measurement

10. Process Measurement
We measure the efficacy of a software process indirectly.
That is, we derive a set of metrics based on the outcomes of the process
Outcomes include
measures of errors uncovered before release of the software
defects delivered to and reported by end-users
work products delivered (productivity)
human effort expended
calendar time expended
schedule conformance
many others…
We also derive process metrics by measuring the characteristics of specific software engineering tasks.
Efficacy - does the process do what is intended
Specific tasks - measure any of the above for a specific phase (communication or analysis, design, construction)
Coming up: Process Metrics Guidelines

11. Process Metrics Guidelines
Use common sense and organizational sensitivity when interpreting metrics data.
Provide regular feedback to the individuals and teams who collect measures and metrics.
Don’t use metrics to appraise individuals.
Work with practitioners and teams to set clear goals and metrics that will be used to achieve them.
Never use metrics to threaten individuals or teams.
Metrics data that indicate a problem area should not be considered “negative.” These data are merely an indicator for process improvement.
Don’t obsess on a single metric to the exclusion of other important metrics.
Coming up: If I calculate the number of defects per developer and rank them, then using that rank assign salary raises based on that.

12. If I calculate the number of defects per developer and rank them, then using that rank assign salary raises based on that.
A. This is good
B. This is bad
Coming up: Software Process Improvement

13. Software Process Improvement
Process model
SPI
Process improvement
recommendations
Improvement goals
Process metrics
Make your metrics actionable!
Coming up: Typical Process Metrics

14. Typical Process Metrics
Quality-related
focus on quality of work products and deliverables
Productivity-related
Production of work-products related to effort expended
Statistical SQA data
error categorization & analysis
Defect removal efficiency
propagation of errors from process activity to activity
Reuse data
The number of components produced and their degree of reusability
Within a single project this can also be a “project metric”. Across projects this is a “process metric”.
Correctness
Maintainability
Integrity
Usability
Earned Value Analysis
Defects found in this stage
This Stage + Next Stage
Severity of errors (1-5)
MTTF (Mean time to failure)
MTTR (Mean time to repair)
SQA - types of errors (0-5), MTTF (failure), MTTR (Repair),
Quality - Correctness-adherence to rqmts, Maintainability-easy to fix?, Integrity-attack vulnerability, Usability-training time, number of screens, etc…
Coming up: Can you calculate a metric that records the number of ‘e’ that appear in a program? A. Yes B. No

15. Can you calculate a metric that records the number of ‘e’ that appear in a program? A. Yes B. No
Should you calculate the number of ‘e’ in a program?
A. Yes
B. No
Coming up: Effective Metrics (ch 16)

16. Effective Metrics (ch 16)
Simple and computable
Empirically and intuitively persuasive
Consistent and objective
Consistent in use of units and dimensions
Programming language independent
Should be actionable
Persuasive - Be what you would naturally think about a metric
Coming up: Effective Metrics- Baseball On Base Percentage

17. Effective Metrics- Baseball On Base Percentage
Simple and computable
Empirically and intuitively persuasive
Consistent and objective
Consistent in use of units and dimensions
Programming language independent
Should be actionable
Not effective for “general audience”
Persuasive - Be what you would naturally think about a metric
Coming up: Effective Metrics- Baseball Runs Batted In

18. Effective Metrics- Baseball Runs Batted In
Count of times when the outcome of player’s at-bat results in a run being scored
Simple and computable
Empirically and intuitively persuasive
Consistent and objective
Consistent in use of units and dimensions
Programming language independent
Should be actionable
Much more effective for “general audience”
Persuasive - Be what you would naturally think about a metric
Coming up: Actionable Metrics

19. Actionable Metrics
Actionable metrics (or information in general) are metrics that guide change or decisions about something
Actionable: Measure the amount of human effort versus use cases completed.
Too high: more training, more design, etc…
Very low: maybe we can shorten the schedule
Not-Actionable: Measure the number of times the letter “e” appears in code
Think before you measure. Don’t waste people’s time!
Coming up: Project Metrics

20. Project Metrics
used to minimize the development schedule by making the adjustments necessary to avoid delays and mitigate potential problems and risks
used to assess product quality on an ongoing basis and, when necessary, modify the technical approach to improve quality.
every project should measure:
Inputs —measures of the resources (e.g., people, tools) required to do the work.
Outputs —measures of the deliverables or work products created during the software engineering process.
Results —measures that indicate the effectiveness of the deliverables.
Coming up: Typical Project Metrics

21. Typical Project Metrics
Effort/time per software engineering task
Errors uncovered per review hour
Scheduled vs. actual milestone dates
Changes (number) and their characteristics
Distribution of effort on software engineering tasks
Dist of effort on SWE == Effort on each phase in the lifecycle (then see where you have problems and correlate them)
Actionable: What do you do if it’s too high/low?
Coming up: Metrics Guidelines

22. Metrics Guidelines Same as process metrics guidelines
Use common sense and organizational sensitivity when interpreting metrics data.
Provide regular feedback to the individuals and teams who have worked to collect measures and metrics.
Don’t use metrics to appraise individuals.
Work with practitioners and teams to set clear goals and metrics that will be used to achieve them.
Never use metrics to threaten individuals or teams.
Metrics data that indicate a problem area should not be considered “negative.” These data are merely an indicator for process improvement.
Don’t obsess on a single metric to the exclusion of other important metrics.
Same as process metrics guidelines
Coming up: Typical Size-Oriented Metrics

23. Typical Size-Oriented Metrics
errors per KLOC (thousand lines of code)
defects per KLOC
$ per LOC
pages of documentation per KLOC
errors per person-month
Errors per review hour
LOC per person-month
$ per page of documentation
TTH Class got here.
Coming up: Typical Function-Oriented Metrics

24. Typical Function-Oriented Metrics
errors per Function Point (FP)
defects per FP
$ per FP
pages of documentation per FP
FP per person-month
Coming up: But.. What is a Function Point?

25. But.. What is a Function Point?
See Book section for more detail
Function points (FP) are a unit measure for software size developed at IBM in 1979 by Richard Albrecht
To determine your number of FPs, you classify a system’s features into five classes:
Transactions - External Inputs, External Outputs, External Inquires
Data storage - Internal Logical Files and External Interface Files
Each class is then weighted by complexity as low/average/high
Multiplied by a value adjustment factor (determined by asking questions based on 14 system characteristics
EI - Info coming into the system
EO - provides derived info out of a system (use ILF and EIF to create information)
ExtInq - provides non-derived information out of the system (echoes back EI)
ILF - Think structures in RAM, datafiles ONLY updated based on EI
ExtIF - Think database, datafile updated by anything
Coming up: But.. What is a Function Point?

26. But.. What is a Function Point?
Count
Low
Average
High
Total
External Input x3 x4 x6
External Output x5 x7
External Inquiries
Internal Logic Files
x10
x15
External Interface Files
Be wary of statistics!
Unadjusted Total:
Value Adjustment Factor:
Total Adjusted Value:
Coming up: Function Point Categories

27. Function Point Categories
External Inputs (EI) - Info coming into the system
External Outputs (EO) - provides derived info out of a system (use ILF and EIF to create information)
External Inquiries - provides non-derived information out of the system (echoes back EI)
Internal Logical Files (ILF) - Think structures in RAM, datafiles ONLY updated based on EI
External Files - Think database, datafile updated by this code, but also other systems
Coming up: Function Point Example

28. Function Point Example
Coming up: Comparing LOC and FP

29. Comparing LOC and FP Representative values developed by QSM
Coming up: At IBM in the 70s or 80s (I don’t remember) they paid people per line-of-code they wrote

30. At IBM in the 70s or 80s (I don’t remember) they paid people per line-of-code they wrote
What happened?
A. The best programmers got paid the most
B. The worst programmers got paid the most
C. The sneakiest programmers, got paid the most
D. The lawyers got paid the most
Coming up: Why opt against LOC?

31. Why opt against LOC? LOC is not programming language independent
LOC cannot use readily countable characteristics that are determined early in the software process
LOC “penalizes” inventive (short) implementations that use fewer LOC that other more clumsy versions
Other metrics makes it easier to measure the impact of reusable components (screen, widgets, etc…)
Other options: COCOMO, Planning Poker, SLIM, Story Points (remember Scrum?), many others…
Coming up: Object-Oriented Metrics

32. Object-Oriented Metrics
Number of scenario scripts (use-cases)
Number of support classes (required to implement the system but are not immediately related to the problem domain)
Average number of support classes per key class (analysis class)
Number of subsystems (an aggregation of
classes that support a function that is visible to
the end-user of a system)
Coming up: WebEngineering Project Metrics

33. WebEngineering Project Metrics
Number of static Web pages (the end-user has no control over the content displayed on the page)
Number of dynamic Web pages (end-user actions result in customized content displayed on the page)
Number of internal page links (internal page links are pointers that provide a hyperlink to some other Web page within the WebApp)
Number of persistent data objects
Number of external systems interfaced
Number of static content objects
Number of dynamic content objects
Number of executable functions
Got here April 13, 2010
Coming up: Measuring Quality

34. Measuring Quality
Correctness — the degree to which a program operates according to specification
Maintainability—the degree to which a program is amenable to change
Integrity—the degree to which a program is impervious to outside attack
Usability—the degree to which a program is easy to use
Verified non-conformance
with reqmts
KLOC
MTTC
Mean time to change:
time to analyze, design,
implement and deploy
a change
t=threat probability
s=security = likelihood of repelling attack
Integrity =  1-(threat*(1-security)) E.g. t=0.25, s= > I=0.99
Correctness - number of
Maintainability - MTTC (meant time to change) - given an incoming change req, what is time to design, implement and test the change
Integrity – T=Liklihood of threat occuring, S = likelihood of repelling the attack
Many options. See ch 12
Coming up: Defect Removal Efficiency

35. Defect Removal Efficiency
DRE = E /(E + D)
E is the number of errors found before delivery of the software to the end-user
D is the number of defects found after delivery.
Provides a measure of how well your team removed defects during a phase. If that DRE is 33%, you found 33% of the defects that you should have found.
Coming up: Defect Removal Efficiency

36. Defect Removal Efficiency
DRE = E /(E + D)
Defects found during phase:
Requirements (10)
Design (20)
Construction
Implementation (5)
Unit Testing (50)
Testing
Integration Testing (100)
System Testing (250)
Acceptance Testing (5)
By Customer (10)
10 / ( ) = 33%
What are the rest?
10 / ( ) = 33%
20 / ( ) = 28%
5 / (5 + 50) = 9%
50 / ( ) = 33%
100 / ( ) = 28%
250 / ( ) = 98%
5 / (5 + 10) = 33%
Coming up: Metrics for Small Organizations

37. Metrics for Small Organizations
time (hours or days) elapsed from the time a request is made until evaluation is complete, tqueue.
effort (person-hours) to perform the evaluation, Weval.
time (hours or days) elapsed from completion of evaluation to assignment of change order to personnel, teval.
effort (person-hours) required to make the change, Wchange.
time required (hours or days) to make the change, tchange.
errors uncovered during work to make change, Echange.
defects uncovered after change is released to the customer base, Dchange.
Coming up: Establishing a Metrics Program

38. Establishing a Metrics Program
Set Goals
Identify your business goals.
Identify what you want to know or learn.
Identify your subgoals.
Identify the entities and attributes related to your subgoals.
Formalize your measurement goals.
Determine indicators for goals
Identify quantifiable questions and the related indicators that you will use to help you achieve your measurement goals.
Identify the data elements that you will collect to construct the indicators that help answer your questions.
Define Measurements
Define the measures to be used, and make these definitions operational.
Identify the actions that you will take to implement the measures.
Prepare a plan for implementing the measures.
Coming up: Metrics give you information!

39. Metrics give you information!
Metrics about your process help you determine if you need to make changes or if your process is working
Metrics about your project do they same thing
Metrics about your software can help you understand it better, and see where possible problems may lurk. Let’s see the complexity measurement (after a few questions…)
Coming up: Questions

40. Questions
What are some reasons NOT to use lines of code to measure size?
What do you expect the DRE rate will be for the implementation (or construction) phase of the software lifecycle?
What about for testing?
Give an example of a usability metric?
According to the chart, Smalltalk is much more efficient than Java and C++. Why don’t we use it for everything?
Coming up: References

41. Code Metrics
Previously we have been discussing product and process metrics.
However, code metrics are used to make your code better
Complexity - See CyclomaticComplexity slides
Dynamic measurements – Profile Jkaboom
Memory – garbage collector, object crerations
CPU performance
Threading
Coming up: Questions

42. References http://www.lansing.lib.il.us/images/baseball_player.gif
End of presentation