Overview

This chapter describes the use of technical metrics in the software quality assurance process. Earlier in the text the use of metrics in project management was discussed. Software engineers use technical metrics to help them assess the quality of the design and construction software product being built. Technical metrics provide software engineers with a basis to conduct analysis, design, coding, and testing more objectively. Qualitative criteria for assessing software quality are not always sufficient by themselves. The process of using technical metrics begins by deriving the software measures and metrics that are appropriate for the software representation under consideration. Then data are collected and metrics are computed. The metrics are computed and compared to pre-established guidelines and historical data. The results of these comparisons are used to guide modifications made to work products arising from analysis, design, coding, or testing.

Software Quality Principles

• Conformance to software requirements is the foundation from which quality is measured.
• Specified standards define a set of development criteria that guide the manner in which software is engineered.
• Software quality is suspect when a software product conforms to its explicitly stated requirements and fails to conform to the customer's implicit requirements (e.g. ease of use).

McCall's Quality Factors

• Product Operation

• Correctness
• Efficiency
• Integrity
• Reliability
• Usability

• Product Revision

• Flexibility
• Maintainability
• Testability

• Product Transition

• Interoperability
• Portability
• Reusability

McCall’s Software Metrics

• Auditability
• Accuracy
• Communication commonality
• Completeness
• Consistency
• Data commonality
• Error tolerance
• Execution efficiency
• Expandability
• Generality
• Hardware independence
• Instrumentation
• Modularity
• Operability
• Security
• Self-documentation
• Simplicity
• Software system independence
• Traceability
• Training

FURPS Quality Factors

• Functionality
• Usability
• Reliability
• Performance
• Supportability

ISO 9126 Quality Factors

• Functionality
• Reliability
• Usability
• Efficiency
• Maintainability
• Portability

Measurement Process Activities

• Formulation – derivation of software measures and metrics appropriate for software representation being considered
• Collection – mechanism used to accumulate the data used to derive the software metrics
• Analysis – computation of metrics
• Interpretation – evaluation of metrics that results in gaining insight into quality of the work product
• Feedback – recommendations derived from interpretation of the metrics is transmitted to the software development team

Formulation Principles for Technical Metrics

• The objectives of measurement should be established before collecting any data.
• Each metric is defined in an unambiguous manner.
• Metrics should be based on a theory that is valid for the application domain.
• Metrics should be tailored to accommodate specific products and processes.

Software Metric Attributes

• Simple and computable
• Empirically and intuitively persuasive
• Consistent and objective
• Consistent in use of units and measures
• Programming language independent
• Provides an effective mechanism for quality feedback

Representative Analysis Metrics

• Function-based metrics
• Bang metric (function strong or data strong)
• Specification quality metrics (Davis)

Representative Design Metrics

• Architectural design metrics

• Structural complexity (based on module fanout)
• Data complexity (based on module interface inputs and outputs)
• System complexity (sum of structural and data complexity)
• Morphology (number of nodes and arcs in program graph)
• Design structure quality index (DSQI)

• Component-level design metrics

• Cohesion metrics (data slice, data tokens, glue tokens, superglue tokens, stickiness)
• Coupling metrics (data and control flow, global, environmental)
• Complexity metrics (e.g. cyclomatic complexity)

• Interface design metrics (e.g. layout appropriateness)

Halstead’s Software Science (Source Code Metrics)

• Overall program length
• Potential minimum algorithm volume
• Actual algorithm volume (number of bits used to specify program)
• Program level (software complexity)
• Language level (constant for given language)

Testing Metrics

• Metrics that predict the likely number of tests required during various testing phases
• Metrics that focus on test coverage for a given component

Maintenance Metrics

• Software Maturity Index (IEEE Standard 982.1-1988)
• SMI approaches 1.0 as product begins to stabilize