Neural code search revisited: Enhancing code snippet retrieval through natural language intent

G Heyman, T Van Cutsem - arXiv preprint arXiv:2008.12193, 2020 - arxiv.org
In this work, we propose and study annotated code search: the retrieval of code snippets
paired with brief descriptions of their intent using natural language queries. On three …

Noisy pair corrector for dense retrieval

H Zhang, Y Gong, X He, D Liu, D Guo, J Lv… - arXiv preprint arXiv …, 2023 - arxiv.org
Most dense retrieval models contain an implicit assumption: the training query-document
pairs are exactly matched. Since it is expensive to annotate the corpus manually, training …

A system-wide debugging assistant powered by natural language processing

P Dogga, K Narasimhan, A Sivaraman… - Proceedings of the ACM …, 2019 - dl.acm.org
Despite advances in debugging tools, systems debugging today remains largely manual. A
developer typically follows an iterative and time-consuming process to move from a reported …

Towards Cloud-Scale Debugging

P Dogga - 2024 - escholarship.org
Cloud computing is an integral part of today's world: it primarily enables individuals and
enterprises to provision and manage resources such as compute, storage, etc., for their …

[PDF][PDF] Machine Learning as a Mean to Uncover Latent Knowledge from Source Code

E Cergani - 2020 - core.ac.uk
I tend to always question even minor things in life and try to find the answers from simple
facts. Maybe this is why I became interested into data analysis. The idea of finding new facts …