Automated Monitoring of Cyclomatic Complexity in Python Projects Using Lizard

Introduction: As business demands evolve, developers aim for testable, simple code; cyclomatic complexity is a key metric for deciding when to refactor code.

Background: Cyclomatic complexity (CC), also known as condition complexity, measures the number of independent execution paths in a module; typical thresholds are 1‑10 (good), 11‑15 (moderate), and >16 (high complexity) indicating the need for refactoring.

Reduction methods: Extract and split functions with single responsibilities, optimize algorithms by reducing unnecessary conditions and loops, simplify logical expressions, and minimize early returns.

Script design: The solution uses the Lizard tool to scan source code, supporting languages such as C/C++, Java, C#, JavaScript, Python, and Go. Configuration parameters include language selection ( -l LANGUAGES, --languages LANGUAGES ), file exclusion patterns ( -x EXCLUDE, --exclude EXCLUDE ), whitelist ( -W WHITELIST, --whitelist WHITELIST ), cyclomatic complexity warning threshold ( -C CCN, --CCN CCN ), other thresholds ( -T THRESHOLDS, --Threshold THRESHOLDS ), and output file ( -o OUTPUT_FILE, --output_file OUTPUT_FILE ).

Scheduled execution: A Python script creates a background scheduler to run the scan automatically each week.

def dojob():
    scheduler = BackgroundScheduler()
    scheduler.add_job(func, "cron", hour=21, minute=30)
    scheduler.start()

Result presentation: Scan results are rendered as HTML reports with visual charts, can be emailed to developers, and stored for dashboard monitoring, providing clear data on CC, LOC, token count, and parameter count.

Conclusion: Cyclomatic complexity offers a quantitative reference for code quality; integrating automated monitoring into development workflows enables timely refactoring, improves maintainability, and supports data‑driven engineering practices.