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Chapter 4
Software Process and
Project Metrics
Software metrics refers to a broad range of measurements for
computer software.
Measurement can be applied to the software...
Why do we Measure?
• To characterize
• To evaluate
• To predict
• To improve
Measures, Metrics, and
Indicators
A measure provides a quantitative indication of the extent,
amount, dimension, capacity,...
Measures, Metrics, and
Indicators
An indicator is a metric or combination of metrics that provide
insight into the softwar...
Metrics in the Process and
Project Domains
Process indicators enable a software engineering organization to
gain insight i...
Metrics in the Process and
Project Domains
Project indicators enable a software project manager to
1) assess the status of...
4.2.1 Process Metrics and
Software Process Improvement
• Fig 4.1
• We measure the efficacy of a software process indirectl...
Process Metrics and Software
Process Improvement
A software metrics etiquette:
• Use common sense an organizational sensit...
Process Metrics and Software
Process Improvement
A software metrics etiquette (cont.):
• Never use metrics to threaten ind...
Process Metrics and Software
Process Improvement
A more rigorous approach: statistical software process improvement
(SSPI)...
Process Metrics and Software
Process Improvement
SPPI (cont.):
4. The overall cost of errors and defects in each
category ...
4.2.2 Project Metrics
• Project metrics are used by a project manager and a software
team to adapt project work flow and t...
Project Metrics
The intent of project metrics are two folds:
- to minimize the development schedule by making the adjustme...
Project Metrics
Another model of project metrics suggests that every project should
measure:
• Inputs – measures of the re...
Software Measurement
• Direct measures of SE process include cost and effort. Direct
measures of product include LOC produ...
4.3.1 Size-oriented Metrics
• Derived by normalizing quality and/or productivity measures
by considering the size of the s...
Size-oriented Metrics
Then we can develop a set of simple size-oriented metrics:
• Errors per KLOC
• Defects per KLOC
• $ ...
4.3.2 Function-Oriented
Metrics
• Use a measure of the functionality delivered by the
application as a normalization value...
Function-Oriented Metrics
Function points are derived using an empirical relationship
based on countable (direct) measures...
Computing Function
PointsAnalyz e info rmatio n
do main of the
application
and develop co unts
Weight each co unt by
asses...
Function-Oriented Metrics
To compute function points (FP), the following relationship is
used:
FP = count total x [0.65+0....
Analyzing the Information
Domain
complexity multiplier
function points
number of user inputs
number of user outputs
number...
Taking Complexity into
AccountFactors are rated on a scale of 0 (not important)
to 5 (very important):
data communications...
Why Opt for FP
Measures?independent of programming language
uses readily countable characteristics of the
"information dom...
Typical Function-Oriented
Metrics
• errors per FP
• defects per FP
• $ per FP
• pages of documentation per FP
• FP per per...
4.4.3 Extended Function Point
Metrics
• Function point was inadequate for many engineering and
embedded systems.
• A funct...
Extended Function Point
Metrics
• The feature point metric counts a new software characteristic
– algorithms.
• Another fu...
Extended Function Point
Metrics
Function points, feature points, and 3D point represent the
same thing – “functionality” o...
4.4 Reconciling Different Metrics
Approaches
• Attempt to relate FP and LOC measures. Table in page 94
4.5 Metrics for Software
Quality
• Must use technical measures to evaluate quality in objective,
rather than subjective wa...
Measuring Quality
• Correctness: defects per KLOC
• Maintainability: the ease that a program can be corrected,
adapted, an...
Measuring Quality
• Integrity: ability to withstand attacks
• Threat: the probability that an attack of a specific type wi...
Measuring Quality
• Usability: attempt to quantify “user-friendliness” in terms of
four characteristics:
1) The physical/i...
Defect Removal Efficiency
• A quality metric that provides benefit at both the project and
process level.
• DRE is a measu...
Defect Removal
Efficiency
DRE = (errors) / (errors + defects)
where
errors = problems found before release
defects = probl...
4.6 Integrating Metrics Within
the Software Process
Arguments for Software Metrics:
• Why is it so important to measure th...
4.7 Managing Variation:
Statistical Process Control
• How can we compare a variety of different projects?
• Use of Control...
Moving Range (mR) Control
Chart
1. Calculate the moving ranges (mR)
2. Calculate the mean of the moving ranges
3. Multiply...
Individual Chart Control
1. Plot individual metrics values as shown in Fig 4.8
2. Compute the average value, Am
3. Multipl...
Individual Chart Control
Zone rules: If any of the following conditions is true, the metrics data
is out of control:
1. A ...
4.8 Metrics for Small
Organizations“Keep it simple”:
• Time
• Effort
• Errors
• Defects
Homework #2
• Problem# 4.9, 4.11, 4.13, 4.17, and 4.18
• Due Mon 15 July 2002
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Software process and project metrics

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Software process and project metrics

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Software process and project metrics

  1. 1. Chapter 4 Software Process and Project Metrics
  2. 2. Software metrics refers to a broad range of measurements for computer software. Measurement can be applied to the software process with the intent of improving it on a continuous basis. Measurement can be used throughout a software project to assist in estimation, quality control, productivity assessment, and project control. Measurement can be used by software engineers to help assess the quality of technical work products and to assist in tactical decision making as a project proceeds.
  3. 3. Why do we Measure? • To characterize • To evaluate • To predict • To improve
  4. 4. Measures, Metrics, and Indicators A measure provides a quantitative indication of the extent, amount, dimension, capacity, or size of some attribute of a product or process. Metrics is a quantitative measure of the degree to which a system, component, or process possesses a a given attribute.
  5. 5. Measures, Metrics, and Indicators An indicator is a metric or combination of metrics that provide insight into the software process, a software project, or the product itself. An indicator provides insight that enables the project manager or software engineers to adjust the process, the project, or the process to make things better.
  6. 6. Metrics in the Process and Project Domains Process indicators enable a software engineering organization to gain insight into the efficacy of an existing process (I.e., the paradigm, software engineering tasks, work products, and milestones). They enable managers and practitioners to assess what works and what doesn’t.
  7. 7. Metrics in the Process and Project Domains Project indicators enable a software project manager to 1) assess the status of an ongoing project 2) track potential risks 3) Uncover problem areas before they go “critical” 4) Adjust work flow or tasks, and 5) Evaluate the project team’s ability to control quality of software work products
  8. 8. 4.2.1 Process Metrics and Software Process Improvement • Fig 4.1 • We measure the efficacy of a software process indirectly; we derive a set of metrics based on the outcomes that can be derived from the process.
  9. 9. Process Metrics and Software Process Improvement A software metrics etiquette: • Use common sense an 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 Cont..
  10. 10. Process Metrics and Software Process Improvement A software metrics etiquette (cont.): • 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.
  11. 11. Process Metrics and Software Process Improvement A more rigorous approach: statistical software process improvement (SSPI): 1. All errors and defects are categorized by origin (flaw in spec, flaw in logic, nonconformance to standards). 2. The cost to correct each error and defect is recorded. 3. The number of errors and defects in each category is counted and ranked in descending order. Cont..
  12. 12. Process Metrics and Software Process Improvement SPPI (cont.): 4. The overall cost of errors and defects in each category is computed. 5. Resultant data are analyzed to uncover the categories that result in the highest cost to the organization. 6. Plans are developed to modify the process with the intent of eliminating (or reducing the frequency of) the class of errors and defects that is most costly. Fig 4.2 and Fig 4.3
  13. 13. 4.2.2 Project Metrics • Project metrics are used by a project manager and a software team to adapt project work flow and technical activities. • Occurred during: • estimation  monitor and control progress. • production rates: pages of documentation, review hours, function points, and delivered source lines. • errors • technical metrics  quality
  14. 14. Project Metrics The intent of project metrics are two folds: - to minimize the development schedule by making the adjustments necessary to avoid delays and mitigate potential problems. - to assess product quality on an ongoing basis and, when necessary, modify the technical approach to improve quality.
  15. 15. Project Metrics Another model of project metrics suggests that every project should measure: • Inputs – measures of the resources 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
  16. 16. Software Measurement • Direct measures of SE process include cost and effort. Direct measures of product include LOC produced, execution speed, memory size, and defects reported over some set period of time. • Indirect measures of product include functionality, quality, complexity, efficiency, reliability, maintainability, and many other “-abilities”
  17. 17. 4.3.1 Size-oriented Metrics • Derived by normalizing quality and/or productivity measures by considering the size of the software that has been produced. • Fig 4.4 • For example: choose LOC as normalization value.
  18. 18. Size-oriented Metrics Then we can develop a set of simple size-oriented metrics: • Errors per KLOC • Defects per KLOC • $ per LOC • Page of documentation per KLOC And other interesting metrics can be computed: • Errors per person-month, LOC per person-month, $ per page of documentation.
  19. 19. 4.3.2 Function-Oriented Metrics • Use a measure of the functionality delivered by the application as a normalization value. • Functionality can not be measured directly, it must be derived indirectly using other direct measures. • A measure called the function point.
  20. 20. Function-Oriented Metrics Function points are derived using an empirical relationship based on countable (direct) measures of software's information domain and assessments of software complexity. Function points are computed by completing the table shown in Fig 4.5.
  21. 21. Computing Function PointsAnalyz e info rmatio n do main of the application and develop co unts Weight each co unt by assessing co mplexity Assess influence of glo bal facto rs that affect the applicatio n C ompute functio n po ints Establish count for input domain and system interfaces A ssign level of complexity orweight to each count Grade significance of external factors, F such as reuse, concurrency, OS, ... degree of influence: N = F i complexity multiplier: C = (0.65 + 0.01 x N) function points = (count x w eight) x C where: i
  22. 22. Function-Oriented Metrics To compute function points (FP), the following relationship is used: FP = count total x [0.65+0.01xΣ(Fi)]
  23. 23. Analyzing the Information Domain complexity multiplier function points number of user inputs number of user outputs number of user inquiries number of files number of ext.interfaces measurement parameter 3 4 3 7 5 count w eighting factor simple avg. complex 4 5 4 10 7 6 7 6 15 10 = = = = = count-total X X X X X
  24. 24. Taking Complexity into AccountFactors are rated on a scale of 0 (not important) to 5 (very important): data communications distributed functions heavily used configuration transaction rate on-line data entry end user efficiency on-line update complex processing installation ease operational ease multiple sites facilitate change
  25. 25. Why Opt for FP Measures?independent of programming language uses readily countable characteristics of the "information domain" of the problem does not "penalize" inventive implementations that require few er LOC than others makes it easier to accommodate reuse and the trend tow ard object-oriented approaches
  26. 26. Typical Function-Oriented Metrics • errors per FP • defects per FP • $ per FP • pages of documentation per FP • FP per person-month
  27. 27. 4.4.3 Extended Function Point Metrics • Function point was inadequate for many engineering and embedded systems. • A function point extension called feature points, is a superset of the function point measure that can be applied to systems and engineering software applications. • Accommodate applications in which algorithmic complexity is high.
  28. 28. Extended Function Point Metrics • The feature point metric counts a new software characteristic – algorithms. • Another function point extension – developed by Boeing  integrate data dimension of software with functional and control dimensions. “3D function point”. • “Counted, quantified, and transformed”
  29. 29. Extended Function Point Metrics Function points, feature points, and 3D point represent the same thing – “functionality” or “utility” delivered by software.
  30. 30. 4.4 Reconciling Different Metrics Approaches • Attempt to relate FP and LOC measures. Table in page 94
  31. 31. 4.5 Metrics for Software Quality • Must use technical measures to evaluate quality in objective, rather than subjective ways. • Must evaluate quality as the project progresses. • The primary thrust is to measure errors and defects  metrics provide indication of the effectiveness software quality assurance and control activities.
  32. 32. Measuring Quality • Correctness: defects per KLOC • Maintainability: the ease that a program can be corrected, adapted, and enhanced. Time/cost. • Time-oriented metrics: Mean-time-to-change (MTTC) • Cost-oriented metrics: Spoilage – cost to correct defects encountered.
  33. 33. Measuring Quality • Integrity: ability to withstand attacks • Threat: the probability that an attack of a specific type will occur within a given time. • Security: the probability that the attack of a specific type will be repelled. Integrity = sum [(1 – threat)x(1 – security)]
  34. 34. Measuring Quality • Usability: attempt to quantify “user-friendliness” in terms of four characteristics: 1) The physical/intellectual skill to learn the system 2) The time required to become moderately efficient in the use of the system 3) The net increase of productivity 4) A subjective assessment of user attitude toward the system (e.g., use of questionnaire).
  35. 35. Defect Removal Efficiency • A quality metric that provides benefit at both the project and process level. • DRE is a measure of filtering ability of quality assurance and control activities as they applied throughout all process framework activities.
  36. 36. Defect Removal Efficiency DRE = (errors) / (errors + defects) where errors = problems found before release defects = problems found after release The ideal value for DRE is 1  no defects found.
  37. 37. 4.6 Integrating Metrics Within the Software Process Arguments for Software Metrics: • Why is it so important to measure the process of software engineering and the product (software) that it produces?
  38. 38. 4.7 Managing Variation: Statistical Process Control • How can we compare a variety of different projects? • Use of Control Chart: to determine whether the dispersion (variability) and “location” (moving average) of process metrics are stable or unstable. 1) The moving average control chart 2) The individual control chart Fig. 4.8 Page102
  39. 39. Moving Range (mR) Control Chart 1. Calculate the moving ranges (mR) 2. Calculate the mean of the moving ranges 3. Multiply the mean by 3.268  upper control limit (UCL) Fig. 4.8  4.9 - Are all moving range values inside the UCL? - If “yes”  stable
  40. 40. Individual Chart Control 1. Plot individual metrics values as shown in Fig 4.8 2. Compute the average value, Am 3. Multiply the mean of the mR value by 2.660 and add Am in (2)  plot the upper natural process limit (UNPL) 4. Multiply the mean of the mR value by 2.660 and subtract Am in (2)  plot the lower natural process limit (LNPL) 5. Compute the SD as (UNPL – Am)/3. Plot lines one and two SD above and below Am.
  41. 41. Individual Chart Control Zone rules: If any of the following conditions is true, the metrics data is out of control: 1. A single metrics value lies outside the UNPL 2. Two out of three successive metrics values lie more than two SD away from Am 3. Four out of five successive metric values lie more than one SD away from Am 4. Eight consecutive metrics values lie on one side of Am.
  42. 42. 4.8 Metrics for Small Organizations“Keep it simple”: • Time • Effort • Errors • Defects
  43. 43. Homework #2 • Problem# 4.9, 4.11, 4.13, 4.17, and 4.18 • Due Mon 15 July 2002

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