omen

There are two types of complexity:

• Cyclomatic Complexity counts the number of different paths through your code. Every if, for, while, or switch creates a new path. A function with cyclomatic complexity of 10 means there are 10 different ways to run through it. The higher the number, the more test cases you need to cover all scenarios.

• Cognitive Complexity measures how hard code is for a human to read. It penalizes deeply nested code (like an if inside a for inside another if) more than flat code. Two functions can have the same cyclomatic complexity, but the one with deeper nesting will have higher cognitive complexity because it's harder to keep track of.

Why it matters: Research shows that complex code has more bugs and takes longer to fix. McCabe's original 1976 paper found that functions with complexity over 10 are significantly harder to maintain. SonarSource's cognitive complexity builds on this by measuring what actually confuses developers.

Rule of thumb: Keep cyclomatic complexity under 10 and cognitive complexity under 15 per function.

When developers write TODO: fix this later or HACK: this is terrible but works, they're creating technical debt and admitting it. Omen finds these comments and groups them by type:

Why it matters: Potdar and Shihab's 2014 study found that SATD comments often stay in codebases for years. The longer they stay, the harder they are to fix because people forget the context. Maldonado and Shihab (2015) showed that design debt is the most common and most dangerous type.

Rule of thumb: Review SATD weekly. If a TODO is older than 6 months, either fix it or delete it.

Dead code includes:

• Functions that are never called
• Variables that are assigned but never used
• Classes that are never instantiated
• Code after a return statement that can never execute

Why it matters: Dead code isn't just clutter. It confuses new developers who think it must be important. It increases build times and binary sizes. Worst of all, it can hide bugs - if someone "fixes" dead code thinking it runs, they've wasted time. Romano et al. (2020) found that dead code is a strong predictor of other code quality problems.

Rule of thumb: Delete dead code. Version control means you can always get it back if needed.

Churn looks at your git history and counts:

• How many times each file was modified
• How many lines were added and deleted
• Which files change together

Files with high churn are "hotspots" - they're constantly being touched, which could mean they're:

• Central to the system (everyone needs to modify them)
• Poorly designed (constant bug fixes)
• Missing good abstractions (features keep getting bolted on)

Why it matters: Nagappan and Ball's 2005 research at Microsoft found that code churn is one of the best predictors of bugs. Files that change a lot tend to have more defects. Combined with complexity data, churn helps you find the files that are both complicated AND frequently modified - your highest-risk code.

Rule of thumb: If a file has high churn AND high complexity, prioritize refactoring it.

There are three types of clones:

Why it matters: When you fix a bug in one copy, you have to remember to fix all the other copies too. Juergens et al. (2009) found that cloned code has significantly more bugs because fixes don't get applied consistently. The more clones you have, the more likely you'll miss one during updates.

Rule of thumb: Anything copied more than twice should probably be a shared function. Aim for duplication ratio under 5%.

Omen combines multiple signals to predict defect probability:

• Complexity (complex code = more bugs)
• Churn (frequently changed code = more bugs)
• Size (bigger files = more bugs)
• Age (newer code = more bugs, counterintuitively)
• Coupling (code with many dependencies = more bugs)

Each file gets a risk score from 0% to 100%.

Why it matters: You can't review everything equally. Menzies et al. (2007) showed that defect prediction helps teams focus testing and code review on the files most likely to have problems. Rahman et al. (2014) found that even simple models outperform random file selection for finding bugs.

Rule of thumb: Prioritize code review for files with >70% defect probability.

TDG combines multiple metrics into a single score (0-5 scale, lower is better):

Scores are classified as:

• Normal (< 1.5): Healthy code
• Warning (1.5 - 2.5): Needs attention
• Critical (> 2.5): Prioritize for refactoring

Why it matters: Technical debt is like financial debt - a little is fine, too much kills you. Cunningham coined the term in 1992, and Kruchten et al. (2012) formalized how to measure and manage it. TDG gives you a single number to track over time and compare across files.

Rule of thumb: Fix critical TDG files before adding new features. Track average TDG over time - it should go down, not up.

Omen builds a graph showing which files import which other files, then calculates:

• PageRank: Which files are most "central" (many things depend on them)
• Betweenness: Which files are "bridges" between different parts of the codebase
• Coupling: How interconnected modules are

Why it matters: Highly coupled code is fragile - changing one file breaks many others. Parnas's 1972 paper on modularity established that good software design minimizes dependencies between modules. The dependency graph shows you where your architecture is clean and where it's tangled.

Rule of thumb: Files with high PageRank should be especially stable and well-tested. Consider breaking up files that appear as "bridges" everywhere.

Maurice Halstead developed these metrics in 1977 to measure programs like physical objects:

Why it matters: Halstead metrics give you objective measurements for comparing different implementations of the same functionality. They can estimate how long code took to write and predict how many bugs it might contain.

Rule of thumb: Use Halstead for comparing alternative implementations. Lower effort and predicted bugs = better.