International Journal of Advanced Research in Computer and Communication Engineering

B.Tech Student, Dept. of CSE–Data Science KKR & KSR Institute Of Technology and Sciences, Guntur2,3,4,5
Abstract: Imperfections in software lead to critical problems in software reliability and stability. Detection of errors at a early stage assures a decrease in software development costs and efforts. Conventional static software analysis tools function on predefined rules and processes. These are not always effective in unearthing any fundamental errors in code logic. As complexities in software evolve, better means of analysis are needed. Artificial Intelligence opens up new model of code analysis to the program- mer. This project is all about the automated code analysis of static code via advanced learning algorithms. This code is converted into structured forms that show how the code logic and syntax map. These structured forms aid the system in analyzing code relationships. Code defects are associated with patterns that a language model understands. The system is known to point to lines of code which are mostly liable to flaws. This is meant to help the programmer concentrate their efforts. The process can be used to test various projects. The project helps to ensure the production of quality software.

Index Terms: Static Code Analysis, Defect Prediction, Large Language Models, Program Representation, Software Quality.

Downloads: | DOI: 10.17148/IJARCCE.2026.15239