Codesplain: AI Powered Documentation App

Anurag Biswal*, Krishna Agarwal**, Nishita Tripathy***, Somesh Kumar Dewangan****, Siddhartha Choubey*****
*-***** Shri Shankaracharya Technical Campus Bhilai, Junwani, Chhattisgarh, India.
Periodicity:April - June'2024
DOI : https://doi.org/10.26634/jse.18.4.20712

Abstract

Codesplain is a groundbreaking AI-powered tool poised to transform the landscape of code documentation by harnessing the power of advanced analysis techniques. Through its sophisticated algorithms, Codesplain delves into code repositories sourced from platforms like GitHub and GitLab, meticulously parsing and deciphering the intricacies of codebases. What sets Codesplain apart is its ability to generate exhaustive and intelligible documentation, offering insights that facilitate seamless collaboration among developers. By distilling complex code structures into comprehensible narratives, Codesplain not only streamlines the development process but also serves as a repository of invaluable knowledge, safeguarding critical insights for future reference. This paper delves into the multifaceted functionality of Codesplain, elucidating its myriad benefits and profound impact on organizational codebases. From fostering a culture of knowledge preservation to catalyzing innovation through expedited development cycles, Codesplain emerges as a transformative force, poised to redefine the standards of code documentation practices.

Keywords

Codesplain, Documentation, Tool, Github, Gitlab, Automation, Repositories.

How to Cite this Article?

Biswal, A., Agarwal, K., Tripathy, N., Dewangan, S. K., and Choubey, S. (2024). Codesplain: AI Powered Documentation App. i-manager’s Journal on Software Engineering, 18(4), 20-27. https://doi.org/10.26634/jse.18.4.20712

References

[3]. Bennett, K. P., & Parrado-Hernández, E. (2006). The interplay of optimization and machine learning research. The Journal of Machine Learning Research, 7, 1265- 1281.
[7]. Hunt, E. B. (1975). Artificial Intelligence. Academic Press.
[9]. Myers, G. J., & Sandler, C. (2004). The Art of Software Testing. John Wiley & Sons.
[10]. Petre, M. (2013, May). UML in practice. In 2013 35th International Conference on Software Engineering (ICSE) (pp. 722-731). IEEE.
[11]. Shull, F., Singer, J., & Sjøberg, D. I. (Eds.). (2007). Guide to Advanced Empirical Software Engineering. Springer Science & Business Media.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.