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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 04

Published on: April 2026

CODESENSE AI-POWERED CODE ANALYSIS AND LEARNING PLATFORM

N Sai Gayatri G Sathish E Pranusha B Arjun Yadav

B Saritha

Department of CSE (Data Science) ACE Engineering College Hyderabad Telangana India

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

CodeSense is an educational platform powered by artificial intelligence, aimed at improving the learning experience of programming through intelligent code analysis, organized explanations, and performance assessments. Unlike conventional programming tools that mainly concentrate on executing code and detecting syntax errors, CodeSense helps learners grasp the underlying logic of their programs. This capability counters the tendency for shallow understanding and enhances problem-solving skills.

The platform tackles this challenge by examining Java code submitted by users and offering in-depth explanations of essential elements such as methods, variables, and loops. It also assesses the time and space complexity of the code and illustrates these concepts through interactive visuals. Furthermore, CodeSense includes a compiler and debugger that pinpoints errors, clearly indicates the lines where they occur, and supplies helpful explanations along with recommended corrections. With its emphasis on user-friendliness and educational value, CodeSense is particularly beneficial for students and beginners looking to deepen their understanding of programming principles.

How to Cite this Paper

Gayatri, N. S., Sathish, G., Pranusha, E. & Yadav, B. A. (2026). CodeSense AI-Powered Code Analysis and Learning Platform. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.180

Gayatri, N, et al.. "CodeSense AI-Powered Code Analysis and Learning Platform." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.180.

Gayatri, N,G Sathish,E Pranusha, and B Yadav. "CodeSense AI-Powered Code Analysis and Learning Platform." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.180.

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References


  1. Codiga Team (2022).“AI-Based Code Analysis and Review Platform.”Documentation: Codiga Official Website.

  2. Chen, M., et al. (2021).“Evaluating Large Language Models Trained on Code.”
    Journal: arXiv / OpenAI Research.

  3. GitHub Inc. (2021). “CodeQL: Semantic Code Analysis Engine.” Documentation: GitHub Security Lab.

  4. Feng, Z., et al. (2020). “CodeBERT: A Pre-Trained Model for Programming and Natural Languages.”, Journal: Findings of EMNLP.

  5. Bhave, A., & Sinha, A. (2019). “Program Comprehension Tools: A Survey.”. Journal: International Journal of Software Engineering and Knowledge Engineering.

  6. Alon, U., et al. (2019). “Code2Vec: Learning Distributed Representations of Code.”. Journal: ACM SIGPLAN Notices, DOI: 10.1145/3360580

  7. Allamanis, M., et al. (2018). “A Survey of Machine Learning for Big Code and Naturalness.”. Journal: ACM Computing Surveys (CSUR). DOI: 10.1145/3212695

  8. Gulwani, S., et al. (2017). “Automated Complexity Analysis Using Program Synthesis.”
    Journal: ACM SIGPLAN Conference Proceedings.

  9. Vaswani, A., et al. (2017). “Attention Is All You Need.”. Journal: Advances in Neural Information Processing Systems (NeurIPS).

  10. Anderson, J. R., et al. (2014). “Intelligent Tutoring Systems for Programming Education.” Journal: Artificial Intelligence in Education.

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  • All submissions are screened under plagiarism detection.
  • Review follows editorial policy.
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  • Peer Review Type: Double-Blind Peer Review
  • Published on: Apr 10 2026
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