Understanding Software Quality Metrics and Measurement

Measurement

Process of quantifying attributes of software.

Metrics

Standardized measurements used for evaluating quality.

Maintainability Index

Score from 0 to 100 indicating code maintainability.

High Maintainability

Index value above 20, indicating good maintainability.

Moderate Maintainability

Index value between 10 and 19, indicating average maintainability.

Low Maintainability

Index value below 10, indicating poor maintainability.

Color Coded Ratings

Visual indicators to assess code maintainability quickly.

Halstead Volume

Metric estimating size and vocabulary of code.

Lines of Code (LOC)

Total number of lines in a source code file.

Normalization of LOC

Adjusting LOC count by excluding comments and empty lines.

Logical Lines of Code

Count of executable statements in the code.

Language Productivity Ratio

Comparison of line efficiency across programming languages.

Operators

Symbols that perform operations in programming languages.

Operands

Variables or values on which operations are performed.

Control Flow Graph (CFG)

Graphical representation of all paths through a program.

Test Case Coverage

Number of test cases needed to cover all paths.

Code Maintainability

Ease of making changes to existing code.

Maintainability Index Formula

MAX(0,(171 - 5.2log(HV) - 0.23CC - 16.2log(LOC))100/171)

Software Quality Metric

Function producing numerical value indicating software quality.

Scalability

Ability of software to handle increased load.

Security

Protection of software from unauthorized access.

Extensibility

Ease of adding new features to software.

Documentation

Written description of software functionality and usage.

Performance

Efficiency of software in executing tasks.

Consistency

Uniformity in software behavior and output.

Portability

Software's ability to run on different platforms.

Installability

Ease of installing software on systems.

Maintainability

Ease of maintaining and updating software.

Functionality

Software's ability to perform required tasks.

Availability

Software's readiness for use when needed.

Ease of Use

User-friendliness of software interface.

On-time Release

Delivery of software by scheduled deadline.

Development Speed

Rate at which software is developed.

The Streetlight Effect

Bias of searching for information where it's easiest.

Goal

Defines objectives for measurement and analysis.

Question

Quantifiable inquiries to assess progress toward goals.

Metric

Specific attributes and definitions for measurement data.

GQM Tree

Hierarchical representation of goals, questions, and metrics.

GQM Steps

Process: Determine goals, create questions, define metrics.

Operationalize

Capture information for the defined measure.

Validate Measure

Ensure metric accurately reflects intended measurement.

Object

What is being examined in the measurement.

Purpose

Reason for examining the object in question.

Focus

Attribute being analyzed in the measurement.

Viewpoint

Perspective from which the examination is conducted.

Environment

Context and scope of the measurement process.

Effectiveness of Finding Defects

Assessment of defect identification during testing.

Escape Characters

Special characters that affect test outcomes.

Impact of Additional Tests

Effect of new tests on existing test processes.

Stakeholders

Individuals involved in clarifying measurement goals.

Shared Understanding

Common comprehension among stakeholders about objectives.

Measurement Theory

Framework guiding the collection and analysis of data.

Statistical Design

Plan for analyzing measurement data statistically.

Collection Frequency

How often data is gathered for metrics.

Current Test Time

Total duration of unit tests executed.

Test Runs Per Week

Number of unit test executions weekly.

Time Per Passing Test

Average duration for each successful test.

Number of Tests Finding Defects

Count of tests that successfully detect issues.

GQM Framework

Goals, Questions, Metrics for project analysis.

Stakeholder Involvement

Engagement of relevant parties in data collection.

Operationalization

Defining measurement instruments for data collection.

Measurement Scales

Types of data affecting analysis methods.

Nominal Scale

Categorical data without order or magnitude.

Ordinal Scale

Ordered data without defined magnitude.

Interval Scale

Ordered data with magnitude but no zero.

Ratio Scale

Ordered data with magnitude and true zero.

Defect Severity Levels

Ranking system for categorizing defect severity.

Predictive Validity

Measurement's ability to forecast outcomes.

External Validity

Generalizability of findings to other contexts.

Fault Count

Number of errors identified in each module.

Error Prone Module

Module with the highest fault count.

Estimation Process

Method for predicting module development time.

CI/Build Process

Continuous Integration system for automated testing.

Causation

Requires theory, correlation, and predictive validation.

Confounding Variables

External factors affecting the relationship between variables.

Epidemiological Example

Coffee consumption and cancer affected by smoking.

Object-Oriented Metrics

Metrics assessing software quality in object-oriented programming.

Fault Proneness

Likelihood of software faults based on metrics.

Bad Statistics

Misunderstanding measurement theory leads to incorrect conclusions.

Bad Decisions

Incorrect use of data causing unintended consequences.

Bad Incentives

Ignoring human factors affecting measurement outcomes.

Incentivizing Productivity

Bonus systems based on specific performance metrics.

Stack Ranking

Ranking employees based on performance metrics.

Intrinsic Motivation

Internal drive to perform tasks without external rewards.

Cyclomatic Complexity

Metric for software complexity, often disputed.

Measurement Objectives

Clear goals guiding the measurement process.

Cultural Issues

Organizational factors influencing measurement effectiveness.

Safe Environment

Encouraging honest data collection and reporting.

Complementary Measures

A suite of metrics enhancing overall assessment.

Kaner's Questions

Guidelines for selecting effective software metrics.

Natural Scale

The inherent scale of an attribute being measured.

Measurement Error

Variability in readings from measurement instruments.

Construct Validity

Relationship between attribute and measurement instrument.

Monitoring vs Incentives

Distinction between tracking performance and rewarding it.

Defect Prediction

Forecasting software faults using historical data.

Benchmarking

Comparing performance metrics against standards or peers.