Software process and project metrics

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 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.

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, 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.

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.

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.

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

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.

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..

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.

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..

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

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

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.

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

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”

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.

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.

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.

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.

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

Function-Oriented Metrics
To compute function points (FP), the following relationship is
used:
FP = count total x [0.65+0.01xΣ(Fi)]

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

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

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

Typical Function-Oriented
Metrics
• errors per FP
• defects per FP
• $ per FP
• pages of documentation per FP
• FP per person-month

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.

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”

Extended Function Point
Metrics
Function points, feature points, and 3D point represent the
same thing – “functionality” or “utility” delivered by software.

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 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.

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.

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)]

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).

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.

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.

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?

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

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

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.

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.

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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