Software Quality Engineering - Software Metrics

Software Metrics

Use of Metrics

• Metrics are used to determine the quality of a current product or process.
• They help predict qualities of a product or process.
• Metrics assist in improving the quality of a product or process.

Motivation for Metrics

• Estimate the cost and schedule of future projects.
• Evaluate the productivity impacts of new tools and techniques.
• Establish productivity trends over time.
• Improve software quality.
• Forecast future staffing needs.
• Anticipate and reduce future maintenance needs.

Example Metrics

• Defect rates
• Error rates
• Measured by:
  • Individual
  • Module
  • During development
• Errors should be categorized by origin, type, cost.

Definitions

• Measure: Quantitative indication of extent, amount, dimension, capacity, or size of some attribute of a product or process.
  • Example: Number of errors.
• Metric: Quantitative measure of the degree to which a system, component, or process possesses a given attribute. It's a handle or guess about a given attribute.
  • Example: Number of errors found per person-hours expended.

Metric Classification

• Products: Explicit results of software development activities, including deliverables, documentation, and by-products.
• Processes: Activities related to the production of software.
• Resources: Inputs into the software development activities, such as hardware, knowledge, and people.

Product vs. Process

• Process Metrics
  • Provide insights into process paradigms, software engineering tasks, work products, or milestones.
  • Lead to long-term process improvement.
• Product Metrics
  • Assess the state of the project.
  • Track potential risks.
  • Uncover problem areas.
  • Adjust workflow or tasks.
  • Evaluate team's ability to control quality.

Types of Measures

• Direct Measures (Internal Attributes)
  • Cost, effort, Lines of Code (LOC), speed, memory.
• Indirect Measures (External Attributes)
  • Functionality, quality, complexity, efficiency, reliability, maintainability.

Size-Oriented Metrics

• Size of the software produced.
• LOC: Lines of Code
• KLOC: 1000 Lines of Code
• SLOC: Statement Lines of Code (ignore whitespace)
• Typical Measures:
  • Errors/KLOC, Defects/KLOC, Cost/LOC, Documentation Pages/KLOC

LOC Metrics

• Easy to use.
• Easy to compute.
• Language & programmer dependent.

Complexity Metrics

• LOC is a function of complexity.
• Language and programmer dependent.
• Halstead’s Software Science (entropy measures):
  • $n1$ - number of distinct operators
  • $n2$ - number of distinct operands
  • $N1$ - total number of operators
  • $N2$ - total number of operands

Example

if (k < 2) {
 if (k > 3)
  x = x*k;
}

• Distinct operators: if ( ) { } > < = * ;
• Distinct operands: k 2 3 x
• $n1 = 10$
• $n2 = 4$
• $N1 = 13$
• $N2 = 7$

Halstead’s Metrics

• Amenable to experimental verification [1970s]
• Program length: $N = N1 + N2$
• Program vocabulary: $n = n1 + n2$
• Estimated length: $= n1 \log<em>2 n1 + n2 \log</em>2 n2$ – Close estimate of length for well-structured programs
• Purity ratio: $PR = /N$

Program Complexity

• Volume: $V = N \log_2 n$ – Number of bits to provide a unique designator for each of the n items in the program vocabulary.
• Difficulty: (formula missing in original slides)
• Program effort: $E=D*V$ – This is a good measure of program understandability.

FURPS

• Functionality: Features of system
• Usability: Aesthetics, documentation
• Reliability: Frequency of failure, security
• Performance: Speed, throughput
• Supportability: Maintainability

Measures of Software Quality

• Correctness: Degree to which a program operates according to specification.
  • Defects/KLOC
  • Defect is a verified lack of conformance to requirements.
  • Failures/hours of operation
• Maintainability: Degree to which a program is open to change.
  • Mean time to change
  • Change request to new version (Analyze, design etc.)
  • Cost to correct
• Integrity: Degree to which a program is resistant to outside attack.
  • Fault tolerance, security & threats
• Usability: Easiness to use
  • Training time, skill level necessary to use, Increase in productivity, subjective questionnaire or controlled experiment.