Metrics, Measures & Analytical Methods Resources
The Westfall Team Posts Metrics, Measures & Analytical Methods Resources.
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Metrics & Measurement Theory
I love to start any discussion with definitions to make sure everyone understands what it is I am trying to talk about. So let’s start by defining measurement. According to Norman Fenton in his book Software Metrics, A Rigorous Approach, "measurement is the process by which numbers or symbols are assigned to attributes of entities in the real world in such a way as to describe them according to clearly defined rules."
12 Steps to Useful Software Metrics
12 Steps to Useful Software Metrics introduces the reader to a practical process for establishing and tailoring a software metrics program that focuses on goals and information needs. The process provides a practical, systematic, start-to-finish method of selecting, designing, and implementing software metrics. It outlines a cookbook method that the reader can use to simplify the journey from software metrics in concept to delivered information.
Data to Information to Knowledge
In a previous article, Measurement Defined, I talked about Norman Fenton’s definition of measurement as “the process by which numbers or symbols are assigned to attributes of entities in the real world in such a way as to describe them according to clearly defined rules." Once we perform the measurement process, we have one or more numbers or symbols, these are data items. Data items are simply “facts” that have been collected in some storable, transferable, or expressible format.
However, if the data is going to be useful, it must be transformed into information products that can be interpreted by people into knowledge so that it can be used to make better, fact-based decisions.
Model: Defect density is a measure of the total known defects divided by the size of the software entity being measured.
Number of Known Defects / Size
The Number of Known Defects is the count of total defects identified against a particular software entity during a particular time period. Examples include:
- Defect to date since the creation of a module
- Defects found in a program during an inspection
- Defects to date since the shipment of a release to the customer
A Software Quality Engineers (SQEs) needs to know when and how to use different sampling techniques in order to effectively use sampling during product and project management, audits, testing, product acceptance. and other quality activities.
When the set of all possible items in a population is very large, it may be too costly or time-consuming to do a comprehensive analysis of all of the items. For example, during an audit, there is just not enough time to talk to every auditee, witness every process step, or look at every quality record. If the customer base is large, it may be too costly to survey all the customers to determine their satisfaction level. Evaluating or estimating attributes or characteristics of the entire system, process, product, or project through a representative sample can be more efficient while still providing the required information. To legitimately be able to use a sample to extrapolate the results to the whole population requires the use of one of four statistical sampling methods.
Many times, it takes more than one metric to understand, evaluate or control a software project, product, process, or service. One way to show a summary view of a set of metrics is to use a Kiviat chart, also called a polar chart, radar chart or spider chart. In a Kiviat chart, each “spoke” represents a metric with the metric’s value plotted on that spoke. The outer circle on a Kiviat chart can be used as the objective or threshold or inner and outer circles can be used to depict valid ranges.