CHICOT: A Developer-Assistance Toolkit for Code Search with High-Level Contextual Information

Authors

  • Terufumi Morishita Hitachi
  • Yuta Koreeda Hitachi
  • Atsuki Yamaguchi Hitachi
  • Gaku Morio Hitachi
  • Osamu Imaichi Hitachi
  • Yasuhiro Sogawa Hitachi

DOI:

https://doi.org/10.1609/aaai.v38i21.30575

Keywords:

Artificial Intelligence, Intelligent graphical user interfaces, Natural language processing and speech recognition, Systems that integrate different AI technologies

Abstract

We propose a source code search system named CHICOT (Code search with HIgh level COnText) to assist developers in reusing existing code. While previous studies have examined code search on the basis of code-level, fine-grained specifications such as functionality, logic, or implementation, CHICOT addresses a unique mission: code search with high-level contextual information, such as the purpose or domain of a developer's project. It achieves this feature by first extracting the context information from codebases and then considering this context during the search. It provides a VSCode plugin for daily coding assistance, and the built-in crawler ensures up-to-date code suggestions. The case study attests to the utility of CHICOT in real-world scenarios.

Downloads

Published

2024-03-24

How to Cite

Morishita, T., Koreeda, Y., Yamaguchi, A., Morio, G., Imaichi, O., & Sogawa, Y. (2024). CHICOT: A Developer-Assistance Toolkit for Code Search with High-Level Contextual Information. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23817-23819. https://doi.org/10.1609/aaai.v38i21.30575