CodeCompass: NLP-Driven Navigation to Optimal Repositories

In the dynamic landscape of open-source development, the challenge of efficiently aligning developers with projects that resonate with their expertise persists. Recent techniques, such as leveraging the GitHub API and employing advanced Natural Language Processing (NLP) models like BERT, have shown...

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Published in:2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) pp. 393 - 401
Main Authors: Phatangare, Sheetal, Matkar, Aakash, Jadhav, Akshay, Shaikh, Al Hussain, Bonde, Anish
Format: Conference Proceeding
Language:English
Published: IEEE 03.05.2024
Subjects:
Accuracy
Bert Model
Bidirectional control
Biological system modeling
Developer skill alignment
Ecosystems
Large language models
Natural Language Processing (NLP)
Open-Source Recommendation
React-based interface
Semantics
Transformers
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Abstract In the dynamic landscape of open-source development, the challenge of efficiently aligning developers with projects that resonate with their expertise persists. Recent techniques, such as leveraging the GitHub API and employing advanced Natural Language Processing (NLP) models like BERT, have shown promise in enhancing project recommendation systems. However, existing solutions often struggle with accurately capturing the nuanced semantic relationships within issues and achieving a high level of personalization for developers. In response to these challenges, this research introduces CodeCompass, a novel solution aimed at addressing these shortcomings. By analysing users' historical contributions, including commit history and issue engagement, CodeCompass harnesses the power of NLP techniques like stemming and lemmatization to unravel the intricate semantic relationships embedded within GitHub issues. The system further employs the BERT model to capture contextual information and refine recommendations with higher accuracy. Despite the advancements in technology, challenges persist in accurately discerning developers' preferences and aligning them with suitable projects. The proposed CodeCompass system seeks to overcome these challenges by providing personalized project recommendations that not only match developers' technical expertise but also resonate with their past experiences in the GitHub community. Through a React-based website interface, CodeCompass aims to streamline developer engagement in the open-source community, fostering collaboration and innovation.
AbstractList In the dynamic landscape of open-source development, the challenge of efficiently aligning developers with projects that resonate with their expertise persists. Recent techniques, such as leveraging the GitHub API and employing advanced Natural Language Processing (NLP) models like BERT, have shown promise in enhancing project recommendation systems. However, existing solutions often struggle with accurately capturing the nuanced semantic relationships within issues and achieving a high level of personalization for developers. In response to these challenges, this research introduces CodeCompass, a novel solution aimed at addressing these shortcomings. By analysing users' historical contributions, including commit history and issue engagement, CodeCompass harnesses the power of NLP techniques like stemming and lemmatization to unravel the intricate semantic relationships embedded within GitHub issues. The system further employs the BERT model to capture contextual information and refine recommendations with higher accuracy. Despite the advancements in technology, challenges persist in accurately discerning developers' preferences and aligning them with suitable projects. The proposed CodeCompass system seeks to overcome these challenges by providing personalized project recommendations that not only match developers' technical expertise but also resonate with their past experiences in the GitHub community. Through a React-based website interface, CodeCompass aims to streamline developer engagement in the open-source community, fostering collaboration and innovation.
Author Bonde, Anish
Shaikh, Al Hussain
Phatangare, Sheetal
Matkar, Aakash
Jadhav, Akshay
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  organization: Vishwakarma Institute of Technology,Department of Computer Engineering,Pune,India
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SubjectTerms Accuracy
Bert Model
Bidirectional control
Biological system modeling
Developer skill alignment
Ecosystems
Large language models
Natural Language Processing (NLP)
Open-Source Recommendation
React-based interface
Semantics
Transformers
Title CodeCompass: NLP-Driven Navigation to Optimal Repositories
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