The invention relates to the technical field of
software engineering, in particular to a
source code detection method, which comprises the steps of
source code preprocessing, code dependency analysis, semantic understanding and analysis, code quality evaluation, dynamic behavior
simulation, intelligent
vulnerability detection and the like to realize comprehensive detection of potential vulnerabilities and quality problems in source codes. A
support vector machine and a K-means clustering
algorithm are combined, known and unknown vulnerabilities can be effectively recognized, in addition, a
continuous optimization feedback mechanism is introduced, a mixed
vulnerability detection model can be continuously optimized according to repair operation and feedback information of a developer, the detection accuracy is improved, and the coverage range is expanded. The generated comprehensive detection report covers code structures,
vulnerability positioning, quality problems and optimization suggestions, supports integration of various output formats and development tool chains, helps developers to efficiently repair problems, and improves code quality and project progress; according to the method, the comprehensiveness, the self-adaptability and the user experience of code detection are effectively improved.