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  Published Paper Details:

  Paper Title

CODE SENSEI - A.I CODING ASSISTANT

  Authors

  Rayyan Azmi,  Vaibhav Bankar,  Sandesh Yadav,  Mrs. Manjiri Gogate

  Keywords

: "Artificial Intelligence", "Assistant", "Online learning", "Generative model", "Encoder-Decoder".

  Abstract


Programmers face situations where they have to rely on messy documentation, other developers and online search for basic programming commands and queries when they encounter any new programming environment. This leads to the waste of time of developers and decreases productivity. Code Sensei is a revolutionary tool aimed at streamlining the coding process for developers of all levels. By leveraging advanced machine learning algorithms, it offers real-time code suggestions, analysis, and generation. This enhances productivity, reduces coding errors, and ultimately saves valuable development time & which assists the programmers with basic programming queries that they face when they are new to a programming environment. It allows students to undertake programming exercises and receive interactive guidance in getting their programs to compile and run. This is the tool developed to assist students and instructors in solving their practical lab session problems, which comes with features such as error handling and solve common problems related to coding.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT24A3300

  Paper ID - 254451

  Page Number(s) - l41-l46

  Pubished in - Volume 12 | Issue 3 | March 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Rayyan Azmi,  Vaibhav Bankar,  Sandesh Yadav,  Mrs. Manjiri Gogate,   "CODE SENSEI - A.I CODING ASSISTANT", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 3, pp.l41-l46, March 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A3300.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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