This document outlines the IEEE standard for a software quality metrics methodology. It describes the purpose of establishing software quality metrics which is to quantify quality factors and ensure quality requirements are met. It provides steps for developing a software quality metrics program which include determining quality requirements, selecting metrics, collecting and storing data, validating metrics, and revalidating metrics when the environment changes. The goal is to use metrics to predict and measure software quality in order to identify issues and ensure compliance with quality standards.

1. Kiran Lata Gangwar
Tanmi Kapoor
M.Tech (S.E.)

2. IEEE Standard for a Software Quality
Metrics Methodology
 Sponsor
Software Engineering Standards Committee of the IEEE
Computer Society
 Approved 8 December 1998
IEEE-SA Standards Board

5. Use,
 organizational experience
 required standards
 regulations
 laws
Consider,
 contractual requirements
 cost or schedule constraints
 warranties
 customer metrics requirements
 organizational self-interest

6. 1.2 Determine the list of quality requirements:
 Survey all involved parties
 Create the list of quality requirements
1.3 Quantify each quality factor:
 For each quality factor, assign one or more direct metrics to
represent the quality factor
 assign direct metric values to serve as quantitative
requirements for that quality factor

9.  Identify tools
 Describe data storage procedures
 Establish a traceability matrix
 Identify the organizational entities
 Participate in data collection
 Responsible for monitoring data collection
 Describe the training and experience required for data
collection
 Training process for personnel involved

11.  Test the data collection and metrics computation procedures
on selected software that will act as a prototype
 Select samples that are similar to the project(s) on which the
metrics will be used
 Examine the cost of the measurement process for the
prototype to verify or improve the cost analysis
 Results collected from the prototype to improve the metric
descriptions and descriptions of data items

12.  Using the formats in Table, collect and store data in the
project metrics database at the appropriate time in the life
cycle
 Check the data for accuracy and proper unit of measure
 Monitor the data collection
 Check for uniformity of data if more than one person is
collecting it
 Compute the metric values from the collected data

14.  Interpret and record the results
 Analyze the differences between the collected metric data and
the target values
 Investigate significant differences

15.  Interpret and record the results
 Unacceptable quality may be manifested as
 excessive complexity,
 inadequate documentation,
 lack of traceability,
 or other undesirable attributes

16.  Use validated metrics during development to make predictions
of direct metric values
 Make predictions for software components and process steps
 Analyze in detail software components and process steps
whose predicted direct metric values deviate from the target
values

17.  Use direct metrics to ensure compliance of software products
with quality requirements during system and acceptance
testing
 Use direct metrics for software components and process steps.
Compare these metric values with target values of the direct
metrics
 Classify software components and process steps whose
measurements deviate from the target values as noncompliant

19.  The purpose of metrics validation is to identify both product
and process metrics that can predict specified quality factor
values, which are quantitative representations of quality
requirements
 Metrics shall indicate whether quality requirements have
been achieved or are likely to be achieved in the future

20.  For the purpose of assessing whether a metric is valid
 The following thresholds shall be designated:
V-square of the linear correlation coefficient
B-rank correlation coefficient
A-prediction error
@-confidence level
P-success rate

21. a) Correlation
b) Tracking
c) Consistency
d) Predictability
e) Discriminative power
f) Reliability

22. 5.3.1 Identify the quality factors sample
 A sample of quality factors shall be drawn from the metrics
database
5.3.2 Identify the metrics sample
 A sample from the same domain (e.g., same software
components), as used in 5.3.1, shall be drawn from the metrics
database

23. 5.3.3 Perform a statistical analysis
 The analysis described in 5.2 shall be performed
 Before a metric is used to evaluate the quality of a product or
process, it shall be validated against the criteria described in
5.2. If a metric does not pass all of the validity tests, it shall
only be used according to the criteria prescribed by those tests
5.3.4 Document the results
 Documented results shall include the direct metric, predictive
metric, validation criteria, and numerical results, as a minimum

24.  5.3.5 Revalidate the metrics
A validated metric may not necessarily be valid in other
environments or future applications. Therefore, a predictive
metric shall be revalidated before it is used for another
environment or application
 5.3.6 Evaluate the stability of the environment
Metrics validation shall be undertaken in a stable development
environment (i.e., where the design language, implementation
language, or project development tools do not change over the
life of the project in which validation is performed)