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Title: :  A SURVEY ON AI POWERED SELF HEALING CODE DEBUGGER
PaperId: :  28129
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 12 Issue 2 2026
DUI:    16.0415/IJARIIE-28129
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Nandana ShibuHoly Grace Academy Of Engineering, Mala
Nandhana K NHoly Grace Academy Of Engineering, Mala
Neha KumariHoly Grace Academy Of Engineering, Mala
Unnimaya K SHoly Grace Academy Of Engineering, Mala
Sanam E Anto Holy Grace Academy Of Engineering, Mala
Ajith P J Holy Grace Academy Of Engineering, Mala

Abstract

Computer Engineering
Artificial Intelligence, Self-Healing Code Debugger, Large Language Models, Automated Debugging, Multi-language Programming, Code Error Detection, Runtime Analysis, Code Optimization
Software development often requires identifying and correcting errors in program code, a process known as debugging. This stage is one of the most challenging and time-consuming parts of the software development lifecycle, especially as modern applications become more complex and involve multiple programming languages and frameworks. Traditional debugging tools mainly help developers by highlighting errors or allowing step-by-step execution of programs. However, these tools still depend heavily on the developer’s knowledge and experience to analyse the problem and implement a solution. This can increase development time and may be difficult for beginners who struggle to understand compiler messages or identify logical mistakes in their code. To overcome these challenges, this project proposes an AI-Powered Self-Healing Code Debugger that uses Artificial Intelligence and Large Language Models to automatically detect, analyse, and repair programming errors. The system is designed to identify syntax errors, runtime issues, and certain logical mistakes in programs written in multiple programming languages. By examining the structure of the code and analysing execution results, the AI module generates corrected versions of the code and provides clear explanations that help users understand the cause of the error and the reasoning behind the solution. The system can also suggest improvements and programming best practices, making it useful not only for debugging but also for learning. In addition, the platform includes a secure environment where the submitted code can be safely compiled and executed to capture runtime feedback. This helps the system produce more accurate debugging suggestions. The interface of the system is designed to be user-friendly, offering features such as syntax highlighting, theme selection, and easy copying of corrected code. By automating repetitive debugging tasks and providing clear explanations, the AI-based self-healing debugger helps reduce development time, improve code quality, and support programmers of different skill levels. Ultimately, the system aims to make debugging faster, smarter, and more accessible.

Citations

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IJARIIE Nandana Shibu, Nandhana K N, Neha Kumari, Unnimaya K S, Sanam E Anto , and Ajith P J . "A SURVEY ON AI POWERED SELF HEALING CODE DEBUGGER" International Journal Of Advance Research And Innovative Ideas In Education Volume 12 Issue 2 2026 Page 229-233
MLA Nandana Shibu, Nandhana K N, Neha Kumari, Unnimaya K S, Sanam E Anto , and Ajith P J . "A SURVEY ON AI POWERED SELF HEALING CODE DEBUGGER." International Journal Of Advance Research And Innovative Ideas In Education 12.2(2026) : 229-233.
APA Nandana Shibu, Nandhana K N, Neha Kumari, Unnimaya K S, Sanam E Anto , & Ajith P J . (2026). A SURVEY ON AI POWERED SELF HEALING CODE DEBUGGER. International Journal Of Advance Research And Innovative Ideas In Education, 12(2), 229-233.
Chicago Nandana Shibu, Nandhana K N, Neha Kumari, Unnimaya K S, Sanam E Anto , and Ajith P J . "A SURVEY ON AI POWERED SELF HEALING CODE DEBUGGER." International Journal Of Advance Research And Innovative Ideas In Education 12, no. 2 (2026) : 229-233.
Oxford Nandana Shibu, Nandhana K N, Neha Kumari, Unnimaya K S, Sanam E Anto , and Ajith P J . 'A SURVEY ON AI POWERED SELF HEALING CODE DEBUGGER', International Journal Of Advance Research And Innovative Ideas In Education, vol. 12, no. 2, 2026, p. 229-233. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/A_SURVEY_ON_AI_POWERED_SELF__HEALING_CODE_DEBUGGER_ijariie28129.pdf (Accessed : ).
Harvard Nandana Shibu, Nandhana K N, Neha Kumari, Unnimaya K S, Sanam E Anto , and Ajith P J . (2026) 'A SURVEY ON AI POWERED SELF HEALING CODE DEBUGGER', International Journal Of Advance Research And Innovative Ideas In Education, 12(2), pp. 229-233IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/A_SURVEY_ON_AI_POWERED_SELF__HEALING_CODE_DEBUGGER_ijariie28129.pdf (Accessed : )
IEEE Nandana Shibu, Nandhana K N, Neha Kumari, Unnimaya K S, Sanam E Anto , and Ajith P J , "A SURVEY ON AI POWERED SELF HEALING CODE DEBUGGER," International Journal Of Advance Research And Innovative Ideas In Education, vol. 12, no. 2, pp. 229-233, Mar-App 2026. [Online]. Available: https://ijariie.com/AdminUploadPdf/A_SURVEY_ON_AI_POWERED_SELF__HEALING_CODE_DEBUGGER_ijariie28129.pdf [Accessed : ].
Turabian Nandana Shibu, Nandhana K N, Neha Kumari, Unnimaya K S, Sanam E Anto , and Ajith P J . "A SURVEY ON AI POWERED SELF HEALING CODE DEBUGGER." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 12 number 2 ().
Vancouver Nandana Shibu, Nandhana K N, Neha Kumari, Unnimaya K S, Sanam E Anto , and Ajith P J . A SURVEY ON AI POWERED SELF HEALING CODE DEBUGGER. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2026 [Cited : ]; 12(2) : 229-233. Available from: https://ijariie.com/AdminUploadPdf/A_SURVEY_ON_AI_POWERED_SELF__HEALING_CODE_DEBUGGER_ijariie28129.pdf
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