| _version_ | 1866902087028178944 |
|---|---|
| author | Thasni Asharaf et al |
| author_facet | Thasni Asharaf et al |
| contents | <p><span lang="EN-IN">The rapid growth of programming technology has made debugging and understanding code increasingly challenging for students and new developers. Traditional IDEs only highlight errors without context, forcing learners to search online or ask others for help, which slows learning and reduces productivity. This project aims to build an AI-powered code editor that identifies syntactic, logical, and semantic errors in real time while pinpointing the exact location of issues. It provides simple explanations, suggests fixes, predicts runtime behavior, and analyzes code quality using machine learning and NLP. Supporting multiple languages like Python, Java, C, and JavaScript, the system learns continuously from student error datasets to improve accuracy. With features like intelligent syntax highlighting, style feedback, and optimization suggestions, the AI editor enhances understanding, speeds up debugging, promotes self-directed learning, and ultimately transforms programming education into a more intuitive and efficient experience.</span></p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18497113 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Real-Time Syntactic, Semantic, and Logical Error Detection Using AI in Multilanguage Code Editors Thasni Asharaf et al <p><span lang="EN-IN">The rapid growth of programming technology has made debugging and understanding code increasingly challenging for students and new developers. Traditional IDEs only highlight errors without context, forcing learners to search online or ask others for help, which slows learning and reduces productivity. This project aims to build an AI-powered code editor that identifies syntactic, logical, and semantic errors in real time while pinpointing the exact location of issues. It provides simple explanations, suggests fixes, predicts runtime behavior, and analyzes code quality using machine learning and NLP. Supporting multiple languages like Python, Java, C, and JavaScript, the system learns continuously from student error datasets to improve accuracy. With features like intelligent syntax highlighting, style feedback, and optimization suggestions, the AI editor enhances understanding, speeds up debugging, promotes self-directed learning, and ultimately transforms programming education into a more intuitive and efficient experience.</span></p> <p> </p> |
| title | Real-Time Syntactic, Semantic, and Logical Error Detection Using AI in Multilanguage Code Editors |
| url | https://doi.org/10.5281/zenodo.18497113 |