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Add technical analysis report
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[](https://sonarcloud.io/summary/new_code?id=rmontanana_mdlp)
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[](https://sonarcloud.io/summary/new_code?id=rmontanana_mdlp)
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[](html/index.html)
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[](https://deepwiki.com/rmontanana/mdlp)
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[](https://doi.org/10.5281/zenodo.14245443)
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# <img src="logo.png" alt="logo" width="50"/> mdlp
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TECHNICAL_ANALYSIS_REPORT.md
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TECHNICAL_ANALYSIS_REPORT.md
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# Technical Analysis Report: MDLP Discretization Library
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## Executive Summary
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This document presents a comprehensive technical analysis of the MDLP (Minimum Description Length Principle) discretization library. The analysis covers project structure, code quality, architecture, testing methodology, documentation, and security assessment.
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**Overall Rating: B+ (Good with Notable Issues)**
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The library demonstrates solid software engineering practices with excellent test coverage and clean architectural design, but contains several security vulnerabilities and code quality issues that require attention before production deployment.
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---
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## Table of Contents
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1. [Project Overview](#project-overview)
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2. [Architecture & Design Analysis](#architecture--design-analysis)
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3. [Code Quality Assessment](#code-quality-assessment)
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4. [Testing Framework Analysis](#testing-framework-analysis)
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5. [Security Analysis](#security-analysis)
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6. [Documentation & Maintainability](#documentation--maintainability)
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7. [Build System Evaluation](#build-system-evaluation)
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8. [Strengths & Weaknesses Summary](#strengths--weaknesses-summary)
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9. [Recommendations](#recommendations)
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10. [Risk Assessment](#risk-assessment)
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---
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## Project Overview
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### Description
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The MDLP discretization library is a C++ implementation of Fayyad & Irani's Multi-Interval Discretization algorithm for continuous-valued attributes in classification learning. The library provides both traditional binning strategies and advanced MDLP-based discretization.
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### Key Features
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- **MDLP Algorithm**: Implementation of information-theoretic discretization
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- **Multiple Strategies**: Uniform and quantile-based binning options
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- **PyTorch Integration**: Native support for PyTorch tensors
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- **High Performance**: Optimized algorithms with caching mechanisms
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- **Complete Testing**: 100% code coverage with comprehensive test suite
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### Technology Stack
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- **Language**: C++17
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- **Build System**: CMake 3.20+
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- **Dependencies**: PyTorch (libtorch 2.7.0)
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- **Testing**: Google Test (GTest)
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- **Coverage**: lcov/genhtml
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- **Package Manager**: Conan
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---
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## Architecture & Design Analysis
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### Class Hierarchy
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```
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Discretizer (Abstract Base Class)
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├── CPPFImdlp (MDLP Implementation)
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└── BinDisc (Simple Binning)
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Metrics (Standalone Utility Class)
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```
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### Design Patterns Identified
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#### ✅ **Well-Implemented Patterns**
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- **Template Method Pattern**: Base class provides `fit_transform()` while derived classes implement `fit()`
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- **Facade Pattern**: Unified interface for both C++ vectors and PyTorch tensors
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- **Composition**: `CPPFImdlp` composes `Metrics` for statistical calculations
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#### ⚠️ **Pattern Issues**
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- **Strategy Pattern**: `BinDisc` uses enum-based strategy instead of proper object-oriented strategy pattern
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- **Interface Segregation**: `BinDisc.fit()` ignores `y` parameter, violating interface contract
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### SOLID Principles Adherence
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| Principle | Rating | Notes |
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|-----------|--------|-------|
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| **Single Responsibility** | ✅ Good | Each class has clear, focused responsibility |
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| **Open/Closed** | ✅ Good | Easy to extend with new discretization algorithms |
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| **Liskov Substitution** | ⚠️ Issues | `BinDisc` doesn't properly handle supervised interface |
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| **Interface Segregation** | ✅ Good | Focused interfaces, not overly broad |
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| **Dependency Inversion** | ✅ Good | Depends on abstractions, not implementations |
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### Architectural Strengths
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- **Clean Separation**: Algorithm logic, metrics, and data handling well-separated
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- **Extensible Design**: Easy to add new discretization methods
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- **Multi-Interface Support**: Both C++ native and PyTorch integration
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- **Performance Optimized**: Caching and efficient data structures
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### Architectural Weaknesses
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- **Interface Inconsistency**: Mixed supervised/unsupervised interface handling
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- **Complex Single Methods**: `computeCutPoints()` handles too many responsibilities
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- **Tight Coupling**: Direct access to internal data structures
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- **Limited Configuration**: Algorithm parameters scattered across classes
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---
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## Code Quality Assessment
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### Code Style & Standards
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- **Consistent Naming**: Good use of camelCase and snake_case conventions
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- **Header Organization**: Proper SPDX licensing and copyright headers
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- **Type Safety**: Centralized type definitions in `typesFImdlp.h`
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- **Modern C++**: Good use of C++17 features
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### Critical Code Issues
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#### 🔴 **High Priority Issues**
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**Memory Safety - Unsafe Pointer Operations**
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```cpp
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// Location: Discretizer.cpp:35-36
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samples_t X(X_.data_ptr<precision_t>(), X_.data_ptr<precision_t>() + num_elements);
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labels_t y(y_.data_ptr<int>(), y_.data_ptr<int>() + num_elements);
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```
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- **Issue**: Direct pointer arithmetic without bounds checking
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- **Risk**: Buffer overflow if tensor data is malformed
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- **Fix**: Add tensor validation before pointer operations
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#### 🟡 **Medium Priority Issues**
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**Integer Underflow Risk**
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```cpp
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// Location: CPPFImdlp.cpp:98-100
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n = cut - 1 - idxPrev; // Could underflow if cut <= idxPrev
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m = idxNext - cut - 1; // Could underflow if idxNext <= cut
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```
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- **Issue**: Size arithmetic without underflow protection
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- **Risk**: Extremely large values from underflow
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- **Fix**: Add underflow validation
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**Vector Access Without Bounds Checking**
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```cpp
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// Location: Multiple locations
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X[indices[idx]] // No bounds validation
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```
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- **Issue**: Direct vector access using potentially invalid indices
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- **Risk**: Out-of-bounds memory access
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- **Fix**: Use `at()` method or add explicit bounds checking
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### Performance Considerations
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- **Caching Strategy**: Good use of entropy and information gain caching
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- **Memory Efficiency**: Smart use of indices to avoid data copying
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- **Algorithmic Complexity**: Efficient O(n log n) sorting with optimized cutpoint selection
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---
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## Testing Framework Analysis
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### Test Organization
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| Test File | Focus Area | Key Features |
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|-----------|------------|-------------|
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| `BinDisc_unittest.cpp` | Binning strategies | Parametric testing, multiple bin counts |
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| `Discretizer_unittest.cpp` | Base interface | PyTorch integration, transform methods |
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| `FImdlp_unittest.cpp` | MDLP algorithm | Real datasets, comprehensive scenarios |
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| `Metrics_unittest.cpp` | Statistical calculations | Entropy, information gain validation |
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### Testing Strengths
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- **100% Code Coverage**: Complete line and branch coverage
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- **Real Dataset Testing**: Uses Iris, Diabetes, Glass datasets from ARFF files
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- **Edge Case Coverage**: Empty datasets, constant values, single elements
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- **Parametric Testing**: Multiple configurations and strategies
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- **Data-Driven Approach**: Systematic test generation with `tests.txt`
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- **Multiple APIs**: Tests both C++ vectors and PyTorch tensors
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### Testing Methodology
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- **Framework**: Google Test with proper fixture usage
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- **Precision Testing**: Consistent floating-point comparison margins
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- **Exception Testing**: Proper error condition validation
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- **Integration Testing**: End-to-end algorithm validation
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### Testing Gaps
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- **Performance Testing**: No benchmarks or performance regression tests
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- **Memory Testing**: Limited memory pressure or leak testing
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- **Thread Safety**: No concurrent access testing
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- **Fuzzing**: No randomized input testing
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---
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## Security Analysis
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### Overall Security Risk: **MEDIUM**
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### Critical Security Vulnerabilities
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#### 🔴 **HIGH RISK - Memory Safety**
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**Unsafe PyTorch Tensor Operations**
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- **Location**: `Discretizer.cpp:35-36, 42, 49-50`
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- **Vulnerability**: Direct pointer arithmetic without validation
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- **Impact**: Buffer overflow, memory corruption
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- **Exploit Scenario**: Malformed tensor data causing out-of-bounds access
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- **Mitigation**:
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```cpp
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if (!X_.is_contiguous() || !y_.is_contiguous()) {
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throw std::invalid_argument("Tensors must be contiguous");
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}
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if (X_.dtype() != torch::kFloat32 || y_.dtype() != torch::kInt32) {
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throw std::invalid_argument("Invalid tensor types");
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}
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```
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#### 🟡 **MEDIUM RISK - Input Validation**
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**Insufficient Parameter Validation**
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- **Location**: Multiple entry points
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- **Vulnerability**: Missing bounds checking on user inputs
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- **Impact**: Integer overflow, out-of-bounds access
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- **Examples**:
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- `proposed_cuts` parameter without overflow protection
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- Tensor dimensions not validated
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- Array indices not bounds-checked
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**Thread Safety Issues**
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- **Location**: `Metrics` class cache containers
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- **Vulnerability**: Shared state without synchronization
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- **Impact**: Race conditions, data corruption
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- **Mitigation**: Add mutex protection or document thread requirements
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#### 🟢 **LOW RISK - Information Disclosure**
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**Debug Information Leakage**
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- **Location**: Sample code and test files
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- **Vulnerability**: Detailed internal data exposure
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- **Impact**: Minor information disclosure
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- **Mitigation**: Remove or conditionalize debug output
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### Security Recommendations
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#### Immediate Actions
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1. **Add Tensor Validation**: Comprehensive validation before pointer operations
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2. **Implement Bounds Checking**: Explicit validation for all array access
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3. **Add Overflow Protection**: Safe arithmetic operations
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#### Short-term Actions
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1. **Enhance Input Validation**: Parameter validation at all public interfaces
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2. **Add Thread Safety**: Documentation or synchronization mechanisms
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3. **Update Dependencies**: Ensure PyTorch is current and secure
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---
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## Documentation & Maintainability
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### Current Documentation Status
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#### ✅ **Available Documentation**
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- **README.md**: Basic usage instructions and build commands
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- **Code Comments**: SPDX headers and licensing information
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- **Build Instructions**: CMake configuration and make targets
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#### ❌ **Missing Documentation**
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- **API Documentation**: No comprehensive API reference
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- **Algorithm Documentation**: Limited explanation of MDLP implementation
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- **Usage Examples**: Minimal code examples beyond basic sample
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- **Configuration Guide**: No detailed parameter explanation
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- **Architecture Documentation**: No design document or UML diagrams
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### Maintainability Assessment
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#### Strengths
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- **Clear Code Structure**: Well-organized class hierarchy
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- **Consistent Style**: Uniform naming and formatting conventions
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- **Separation of Concerns**: Clear module boundaries
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- **Version Control**: Proper git repository with meaningful commits
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#### Weaknesses
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- **Complex Methods**: Some functions handle multiple responsibilities
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- **Magic Numbers**: Hardcoded values without explanation
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- **Limited Comments**: Algorithm logic lacks explanatory comments
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- **Configuration Scattered**: Parameters spread across multiple classes
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### Documentation Recommendations
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1. **Generate API Documentation**: Use Doxygen for comprehensive API docs
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2. **Add Algorithm Explanation**: Document MDLP implementation details
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3. **Create Usage Guide**: Comprehensive examples and tutorials
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4. **Architecture Document**: High-level design documentation
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5. **Configuration Reference**: Centralized parameter documentation
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---
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## Build System Evaluation
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### CMake Configuration Analysis
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#### Strengths
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- **Modern CMake**: Uses version 3.20+ with current best practices
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- **Multi-Configuration**: Separate debug/release builds
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- **Dependency Management**: Proper PyTorch integration
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- **Installation Support**: Complete install targets and package config
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- **Testing Integration**: CTest integration with coverage
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#### Build Features
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```cmake
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# Key configurations
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set(CMAKE_CXX_STANDARD 17)
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find_package(Torch CONFIG REQUIRED)
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option(ENABLE_TESTING OFF)
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option(ENABLE_SAMPLE OFF)
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option(COVERAGE OFF)
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```
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### Build System Issues
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#### Security Concerns
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- **Debug Flags**: May affect release builds
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- **Dependency Versions**: Fixed PyTorch version without security updates
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#### Usability Issues
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- **Complex Makefile**: Manual build directory management
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- **Coverage Complexity**: Complex lcov command chain
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### Build Recommendations
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1. **Simplify Build Process**: Use CMake presets for common configurations
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2. **Improve Dependency Management**: Flexible version constraints
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3. **Add Build Validation**: Compiler and platform checks
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4. **Enhance Documentation**: Detailed build instructions
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---
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## Strengths & Weaknesses Summary
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### 🏆 **Key Strengths**
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#### Technical Excellence
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- **Algorithmic Correctness**: Faithful implementation of Fayyad & Irani algorithm
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- **Performance Optimization**: Efficient caching and data structures
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- **Code Coverage**: 100% test coverage with comprehensive edge cases
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- **Modern C++**: Good use of C++17 features and best practices
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#### Software Engineering
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- **Clean Architecture**: Well-structured OOP design with clear separation
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- **SOLID Principles**: Generally good adherence to design principles
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- **Multi-Platform**: CMake-based build system for cross-platform support
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- **Professional Quality**: Proper licensing, version control, CI/CD integration
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#### API Design
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- **Multiple Interfaces**: Both C++ native and PyTorch tensor support
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- **Sklearn-like API**: Familiar `fit()`/`transform()`/`fit_transform()` pattern
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- **Extensible**: Easy to add new discretization algorithms
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### ⚠️ **Critical Weaknesses**
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#### Security Issues
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- **Memory Safety**: Unsafe pointer operations in PyTorch integration
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- **Input Validation**: Insufficient bounds checking and parameter validation
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- **Thread Safety**: Shared state without proper synchronization
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|
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#### Code Quality
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- **Interface Consistency**: LSP violation in `BinDisc` class
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- **Method Complexity**: Some functions handle too many responsibilities
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- **Error Handling**: Inconsistent exception handling patterns
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#### Documentation
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- **API Documentation**: Minimal inline documentation
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- **Usage Examples**: Limited practical examples
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- **Architecture Documentation**: No high-level design documentation
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---
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## Recommendations
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### 🚨 **Immediate Actions (HIGH Priority)**
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#### Security Fixes
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```cpp
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// 1. Add tensor validation in Discretizer::fit_t()
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void Discretizer::fit_t(const torch::Tensor& X_, const torch::Tensor& y_) {
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// Validate tensor properties
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if (!X_.is_contiguous() || !y_.is_contiguous()) {
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throw std::invalid_argument("Tensors must be contiguous");
|
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}
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if (X_.sizes().size() != 1 || y_.sizes().size() != 1) {
|
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throw std::invalid_argument("Only 1D tensors supported");
|
||||
}
|
||||
if (X_.dtype() != torch::kFloat32 || y_.dtype() != torch::kInt32) {
|
||||
throw std::invalid_argument("Invalid tensor types");
|
||||
}
|
||||
// ... rest of implementation
|
||||
}
|
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```
|
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|
||||
```cpp
|
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// 2. Add bounds checking for vector access
|
||||
inline precision_t safe_vector_access(const samples_t& vec, size_t idx) {
|
||||
if (idx >= vec.size()) {
|
||||
throw std::out_of_range("Vector index out of bounds");
|
||||
}
|
||||
return vec[idx];
|
||||
}
|
||||
```
|
||||
|
||||
```cpp
|
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// 3. Add underflow protection in arithmetic operations
|
||||
size_t safe_subtract(size_t a, size_t b) {
|
||||
if (b > a) {
|
||||
throw std::underflow_error("Subtraction would cause underflow");
|
||||
}
|
||||
return a - b;
|
||||
}
|
||||
```
|
||||
|
||||
### 📋 **Short-term Actions (MEDIUM Priority)**
|
||||
|
||||
#### Code Quality Improvements
|
||||
1. **Fix Interface Consistency**: Separate supervised/unsupervised interfaces
|
||||
2. **Refactor Complex Methods**: Break down `computeCutPoints()` function
|
||||
3. **Standardize Error Handling**: Consistent exception types and messages
|
||||
4. **Add Input Validation**: Comprehensive parameter checking
|
||||
|
||||
#### Thread Safety
|
||||
```cpp
|
||||
// Add thread safety to Metrics class
|
||||
class Metrics {
|
||||
private:
|
||||
mutable std::mutex cache_mutex;
|
||||
cacheEnt_t entropyCache;
|
||||
cacheIg_t igCache;
|
||||
|
||||
public:
|
||||
precision_t entropy(size_t start, size_t end) const {
|
||||
std::lock_guard<std::mutex> lock(cache_mutex);
|
||||
// ... implementation
|
||||
}
|
||||
};
|
||||
```
|
||||
|
||||
### 📚 **Long-term Actions (LOW Priority)**
|
||||
|
||||
#### Documentation & Usability
|
||||
1. **API Documentation**: Generate comprehensive Doxygen documentation
|
||||
2. **Usage Examples**: Create detailed tutorial and example repository
|
||||
3. **Performance Testing**: Add benchmarking and regression tests
|
||||
4. **Architecture Documentation**: Create design documents and UML diagrams
|
||||
|
||||
#### Code Modernization
|
||||
1. **Strategy Pattern**: Proper implementation for `BinDisc` strategies
|
||||
2. **Configuration Management**: Centralized parameter handling
|
||||
3. **Factory Pattern**: Discretizer creation factory
|
||||
4. **Resource Management**: RAII patterns for memory safety
|
||||
|
||||
---
|
||||
|
||||
## Risk Assessment
|
||||
|
||||
### Risk Priority Matrix
|
||||
|
||||
| Risk Category | High | Medium | Low | Total |
|
||||
|---------------|------|--------|-----|-------|
|
||||
| **Security** | 1 | 7 | 2 | 10 |
|
||||
| **Code Quality** | 2 | 5 | 3 | 10 |
|
||||
| **Maintainability** | 0 | 3 | 4 | 7 |
|
||||
| **Performance** | 0 | 1 | 2 | 3 |
|
||||
| **Total** | **3** | **16** | **11** | **30** |
|
||||
|
||||
### Risk Impact Assessment
|
||||
|
||||
#### Critical Risks (Immediate Attention Required)
|
||||
1. **Memory Safety Vulnerabilities**: Could lead to crashes or security exploits
|
||||
2. **Interface Consistency Issues**: Violates expected behavior contracts
|
||||
3. **Input Validation Gaps**: Potential for crashes with malformed input
|
||||
|
||||
#### Moderate Risks (Address in Next Release)
|
||||
1. **Thread Safety Issues**: Problems in multi-threaded environments
|
||||
2. **Complex Method Design**: Maintenance and debugging difficulties
|
||||
3. **Documentation Gaps**: Reduced adoption and maintainability
|
||||
|
||||
#### Low Risks (Future Improvements)
|
||||
1. **Performance Optimization**: Minor efficiency improvements
|
||||
2. **Code Style Consistency**: Enhanced readability
|
||||
3. **Build System Enhancements**: Improved developer experience
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
The MDLP discretization library represents a solid implementation of an important machine learning algorithm with excellent test coverage and clean architectural design. However, it requires attention to security vulnerabilities and code quality issues before production deployment.
|
||||
|
||||
### Final Verdict
|
||||
|
||||
**Rating: B+ (Good with Notable Issues)**
|
||||
|
||||
- **Core Algorithm**: Excellent implementation of MDLP with proper mathematical foundations
|
||||
- **Software Engineering**: Good OOP design following most best practices
|
||||
- **Testing**: Exemplary test coverage and methodology
|
||||
- **Security**: Notable vulnerabilities requiring immediate attention
|
||||
- **Documentation**: Adequate but could be significantly improved
|
||||
|
||||
### Deployment Recommendation
|
||||
|
||||
**Not Ready for Production** without addressing HIGH priority security issues, particularly around memory safety and input validation. Once these are resolved, the library would be suitable for production use in most contexts.
|
||||
|
||||
### Next Steps
|
||||
|
||||
1. **Security Audit**: Address all HIGH and MEDIUM priority security issues
|
||||
2. **Code Review**: Implement fixes for interface consistency and method complexity
|
||||
3. **Documentation**: Create comprehensive API documentation and usage guides
|
||||
4. **Testing**: Add performance benchmarks and stress testing
|
||||
5. **Release**: Prepare version 2.1.0 with security and quality improvements
|
||||
|
||||
---
|
||||
|
||||
## Appendix
|
||||
|
||||
### Files Analyzed
|
||||
- `src/CPPFImdlp.h` & `src/CPPFImdlp.cpp` - MDLP algorithm implementation
|
||||
- `src/Discretizer.h` & `src/Discretizer.cpp` - Base class and PyTorch integration
|
||||
- `src/BinDisc.h` & `src/BinDisc.cpp` - Simple binning strategies
|
||||
- `src/Metrics.h` & `src/Metrics.cpp` - Statistical calculations
|
||||
- `src/typesFImdlp.h` - Type definitions
|
||||
- `CMakeLists.txt` - Build configuration
|
||||
- `conanfile.py` - Dependency management
|
||||
- `tests/*` - Comprehensive test suite
|
||||
|
||||
### Analysis Date
|
||||
**Report Generated**: June 27, 2025
|
||||
|
||||
### Tools Used
|
||||
- **Static Analysis**: Manual code review with security focus
|
||||
- **Architecture Analysis**: SOLID principles and design pattern evaluation
|
||||
- **Test Analysis**: Coverage and methodology assessment
|
||||
- **Security Analysis**: Vulnerability assessment with risk prioritization
|
||||
|
||||
---
|
||||
|
||||
*This report provides a comprehensive technical analysis of the MDLP discretization library. For questions or clarifications, please refer to the project repository or contact the development team.*
|
||||
45
conanfile.py
Normal file
45
conanfile.py
Normal file
@@ -0,0 +1,45 @@
|
||||
import re
|
||||
from conan import ConanFile, CMake
|
||||
|
||||
class FimdlpConan(ConanFile):
|
||||
name = "fimdlp"
|
||||
version = "X.X.X"
|
||||
license = "MIT"
|
||||
author = "Ricardo Montañana <rmontanana@gmail.com>"
|
||||
url = "https://github.com/rmontanana/mdlp"
|
||||
description = "Discretization algorithm based on the paper by Fayyad & Irani."
|
||||
topics = ("discretization", "classification", "machine learning")
|
||||
settings = "os", "compiler", "build_type", "arch"
|
||||
generators = "cmake"
|
||||
exports_sources = "src/*", "CMakeLists.txt", "README.md"
|
||||
|
||||
def init(self):
|
||||
# Read the CMakeLists.txt file to get the version
|
||||
# This is a simple example; you might want to use a more robust method
|
||||
# to parse the CMakeLists.txt file.
|
||||
# For example, you could use a regex to extract the version number.
|
||||
with open("CMakeLists.txt", "r") as f:
|
||||
lines = f.readlines()
|
||||
for line in lines:
|
||||
if "VERSION" in line:
|
||||
# Extract the version number using regex
|
||||
match = re.search(r"VERSION\s+(\d+\.\d+\.\d+)", line)
|
||||
if match:
|
||||
self.version = match.group(1)
|
||||
|
||||
def requirements(self):
|
||||
self.requires("libtorch/2.7.0") # Adjust version as necessary
|
||||
|
||||
def build(self):
|
||||
cmake = CMake(self)
|
||||
cmake.configure()
|
||||
cmake.build()
|
||||
|
||||
def package(self):
|
||||
# self.copy("*.h", dst="include", src="src/include")
|
||||
# self.copy("*fimdlp*", dst="lib", keep_path=False)
|
||||
cmake = CMake(self)
|
||||
cmake.install()
|
||||
|
||||
def package_info(self):
|
||||
self.cpp_info.libs = ["fimdlp"]
|
||||
47
getversion.py
Normal file
47
getversion.py
Normal file
@@ -0,0 +1,47 @@
|
||||
|
||||
# read the version from the CMakeLists.txt file
|
||||
import re
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
def get_version_from_cmakelists(cmakelists_path):
|
||||
# Read the CMakeLists.txt file
|
||||
try:
|
||||
with open(cmakelists_path, 'r') as file:
|
||||
content = file.read()
|
||||
except IOError as e:
|
||||
print(f"Error reading {cmakelists_path}: {e}")
|
||||
sys.exit(1)
|
||||
# Use regex to find the version line
|
||||
# The regex pattern looks for a line that starts with 'project' and captures the version number
|
||||
# in the format VERSION x.y.z where x, y, and z are digits.
|
||||
# It allows for optional whitespace around the parentheses and the version number.
|
||||
version_pattern = re.compile(
|
||||
r'project\s*\([^\)]*VERSION\s+([0-9]+\.[0-9]+\.[0-9]+)', re.IGNORECASE | re.DOTALL
|
||||
)
|
||||
match = version_pattern.search(content)
|
||||
if match:
|
||||
return match.group(1)
|
||||
else:
|
||||
return None
|
||||
|
||||
def main():
|
||||
# Get the path to the CMakeLists.txt file
|
||||
cmakelists_path = Path(__file__).parent / "CMakeLists.txt"
|
||||
|
||||
# Check if the file exists
|
||||
if not cmakelists_path.exists():
|
||||
print(f"Error: {cmakelists_path} does not exist.")
|
||||
sys.exit(1)
|
||||
|
||||
# Get the version from the CMakeLists.txt file
|
||||
version = get_version_from_cmakelists(cmakelists_path)
|
||||
|
||||
if version:
|
||||
print(f"Version: {version}")
|
||||
else:
|
||||
print("Version not found in CMakeLists.txt.")
|
||||
sys.exit(1)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user