Code Complexity Analyzer
Analyze source code complexity to detect obfuscation patterns and assess code quality. Measure cyclomatic complexity, nesting depth, function length, identifier quality, and Shannon entropy. Get a readability score (A-F) with severity-flagged obfuscation indicators. 100% private — all processing happens in your browser.
Code Complexity Analyzer
Paste source code above or choose a quick example to analyze its complexity
100% Private: All code complexity analysis happens entirely in your browser. Your source code is never sent to any server. No data leaves your device.
Why Use Our Code Complexity Analyzer?
Cyclomatic Complexity Scoring
Calculate cyclomatic complexity by counting decision points in your code — if/else, for/while loops, switch cases, catch blocks, ternary operators, and logical operators. Higher scores indicate harder-to-test, harder-to-maintain code that may be intentionally obfuscated.
Nesting Depth Analysis
Analyze maximum and average nesting depth across your codebase. Deeply nested structures (5+ levels) are a hallmark of obfuscated or machine-generated code. Our analyzer tracks both brace-based and indentation-based nesting for all languages.
Obfuscation Pattern Detection
Automatically detects red flags: short identifiers (avg < 5 chars), hex-like variable names (_0x format), abnormally long functions (100+ lines), extremely high entropy, sparse comments, and excessive nesting. Each flag includes severity rating and actionable explanation.
100% Private Browser Processing
All code complexity analysis happens locally in your browser using pure JavaScript. Your source code never leaves your device — no server uploads, no data logging, no privacy concerns. 100% free with no signup required.
Common Use Cases for Code Complexity Analyzer
Security Code Review
Flag potentially malicious or obfuscated code in pull requests and code submissions. Complexity spikes, suspiciously short identifiers, and abnormal entropy patterns help security reviewers identify code that warrants closer inspection before deployment.
Code Quality Audits
Evaluate codebase health by measuring cyclomatic complexity, nesting depth, and function length across your project. Identify functions that exceed maintainability thresholds and prioritize refactoring efforts based on quantitative complexity data.
Malware & Payload Analysis
Analyze suspicious scripts for obfuscation indicators before running them in a sandbox. Extremely high entropy, minified single-line functions, hex-like variable names, and near-zero comments are common signatures of malicious payloads.
Programming Education
Help students understand code complexity concepts by visualizing cyclomatic complexity, nesting depth, and identifier quality metrics. Compare well-structured code against obfuscated examples side by side with quantitative analysis.
Third-Party Library Vetting
Evaluate open-source packages and vendored dependencies for obfuscation before integrating them into your project. Automated complexity analysis helps surface suspicious code that manual review might miss in large codebases.
Refactoring Prioritization
Identify the most complex functions in your codebase to prioritize refactoring efforts. Functions with cyclomatic complexity over 15, nesting depth over 5, or length over 50 lines are prime candidates for simplification and testing improvements.
Understanding Code Complexity Analysis
What is Code Complexity Analysis?
Code complexity analysis is the process of measuring how complex a piece of source code is using quantitative metrics. It goes beyond simple line counts to evaluate structural characteristics like the number of independent paths through the code (cyclomatic complexity), how deeply nested blocks are, how long functions are, and how meaningful identifier names are. In the context of obfuscation detection, complexity analysis is a powerful tool because obfuscated code typically exhibits extreme values in multiple metrics simultaneously — very short identifiers, deeply nested control flow, and abnormally high cyclomatic complexity that make the code difficult to understand. Our analyzer compares these metrics against established thresholds and flags suspicious patterns automatically.
How Our Code Complexity Analyzer Works
- Parse: Paste your source code and select the appropriate language. The analyzer parses the code to identify functions, control flow structures, identifiers, comments, and tokens — no actual compilation is needed.
- Measure: The engine computes six key metrics: cyclomatic complexity (counting decision points), nesting depth (maximum and average), function length (max, min, average), identifier length analysis (average and extremes), comment-to-code ratio, and Shannon entropy of the character distribution.
- Flag & Score:Each metric is compared against obfuscation thresholds to generate targeted flags with severity ratings. An overall readability score (0-100) and letter grade (A-F) summarize the code's transparency at a glance.
Key Complexity Metrics Explained
- Cyclomatic Complexity:Counts the number of linearly independent paths through code. Each if, else, for, while, case, catch, &&, and || adds a path. Values under 10 are considered healthy; over 15 is suspicious; over 30 strongly indicates obfuscation.
- Nesting Depth: Measures how many levels deep blocks of code are nested. 3-4 levels is normal for complex logic. 5-7 is suspicious. Over 8 levels is almost certainly obfuscated or machine-generated.
- Identifier Length: Obfuscated code uses very short identifiers (a, b, _0x). Average length under 5 characters with no long identifiers is a strong obfuscation signal. Well-written code typically averages 6-12 characters per identifier.
- Token Entropy: Shannon entropy measures the randomness of the character distribution. Natural code has entropy around 4.0-4.8 bits/char. Obfuscated or encoded code often exceeds 5.0 bits/char due to hex strings and randomized tokens.
Privacy, Security & Availability
100% private: All code analysis, metric computation, and flag generation happen entirely in your browser using pure JavaScript. Your source code never leaves your device — no server uploads, no data logging, no analytics tracking.
Completely free: Our code complexity analyzer is and always will be free to use. No signup required, no usage limits, no file size restrictions. Use it unlimited times for any project — commercial, personal, or educational.
Frequently Asked Questions About Code Complexity Analyzer
Code complexity analysis is the process of measuring source code using quantitative metrics like cyclomatic complexity, nesting depth, function length, and identifier quality. These metrics help assess maintainability, identify potential bugs, and detect obfuscation patterns. Code that exhibits extreme values across multiple metrics is often intentionally obfuscated to make automated analysis harder.
Cyclomatic complexity counts the number of linearly independent paths through your code. Each decision point adds one to the count — if/else branches, for and while loops, switch cases, catch blocks, ternary operators, and logical AND/OR operators all contribute. A base value of 1 represents the single entry point. Values under 10 are healthy, while values over 30 strongly suggest obfuscation.
A nesting depth of 3-4 levels is normal for reasonably complex logic. Depths of 5-7 levels are suspicious and may indicate either poorly structured code or obfuscation. Depths exceeding 8 levels are almost always a strong indicator of obfuscated, machine-generated, or automatically minified code, as human developers rarely write code with such deep nesting.
Obfuscated code consistently uses very short, meaningless identifiers to reduce file size and make the code harder to understand. Common patterns include single-letter names (a, b, c), hex-prefixed names (_0x5f3a, _0x1a2b), and names that follow a systematic short pattern. An average identifier length under 5 characters with no meaningful names is a strong obfuscation signal.
Shannon entropy measures the average information content or randomness of character sequences. Typical source code has an entropy of 4.0-4.8 bits per character due to its structured nature (keywords, operators, whitespace patterns). Obfuscated code containing encoded strings, hex literals, and randomized identifiers often exceeds 5.0 bits/char, making entropy a powerful obfuscation indicator.
No single tool can detect all obfuscation techniques. Our code complexity analyzer focuses on structural obfuscation indicators — complexity, nesting, naming, and entropy patterns. It is effective at detecting minification, variable renaming, control flow flattening, and encoded payloads. However, runtime obfuscation (eval-based, proxy-based) and cryptographic obfuscation may require dynamic analysis tools.
Absolutely. All code parsing, metric computation, flag generation, and scoring happen entirely in your browser using pure JavaScript. Your source code never leaves your device. No data is uploaded to any server — 100% private browser-local processing with no analytics trackers or hidden data collection.
Our analyzer supports JavaScript/TypeScript, Python, Java, C#, C, C++, Go, and Rust through four language modes. The JavaScript mode works for JS, TS, and JSX. The Python mode uses indentation-based analysis. The Java mode works for Java, C#, and C++. The C-Like mode handles C, C++, Go, and Rust. Each mode adjusts parsing for language-specific syntax like Python colons or brace-delimited blocks.
Yes, our code complexity analyzer is 100% free with no hidden costs or limitations. No signup required, no premium tier, no usage limits, no file size restrictions, and no advertisements. Use it unlimited times for any project — commercial, personal, or educational. All processing happens locally in your browser so there are no server costs.