Source code metrics are frequently used to evaluate software quality and identify risky code that requires focused testing. Paul Anderson surveys common source code metrics including Cyclomatic Complexity, Halstead Complexity, and additional metrics aimed at improving security. Using a NASA project as well as data from several recent studies, Paul explores the question of how effective these metrics are at identifying the portions of the software that are the most error prone. He presents new metrics targeted at identifying integration problems. While most metrics to date have focused on calculating properties of individual procedures, newer metrics look at relationships between procedures or components to provide added guidance. Learn about newer metrics that employ data mining techniques implanted with open source machine-learning packages.
- Common code metrics and what they mean
- Security related metrics to help you identify vulnerabilities
- New metrics from data mining techniques