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Nikita Khramov

dblp:391/4913 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0004-3968-4443ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Program verification · 91% Program synthesis and code generation · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program verification › proof generation
LLM-based proof generation
0.812024
CoqPilot, a plugin for LLM-based generation of proofs · ASE 2024
Program verification
proof assistants
0.812024
CoqPilot, a plugin for LLM-based generation of proofs · ASE 2024
Program verification › proof assistants
proof automation
0.812024
CoqPilot, a plugin for LLM-based generation of proofs · ASE 2024
Program synthesis and code generation
code generation with language models
0.212024
CoqPilot, a plugin for LLM-based generation of proofs · ASE 2024

Methods — techniques the papers use, named apart from their topics

non-machine-learning proof search · 0.8large language model · 0.8
YearPublicationVenuePosition
2024 CoqPilot, a plugin for LLM-based generation of proofs
abstract
We present CoqPilot, a VS Code extension designed to help automate writing of Coq proofs. The plugin collects the parts of proofs marked with the admit tactic in a Coq file, i.e., proof holes, and combines LLMs along with non-machine-learning methods to generate proof candidates for the holes. Then, CoqPilot checks if each proof candidate solves the given subgoal and, if successful, replaces the hole with it. The focus of CoqPilot is twofold. Firstly, we want to allow users to seamlessly combine multiple Coq generation approaches and provide a zero-setup experience for our tool. Secondly, we want to deliver a platform for LLM-based experiments on Coq proof generation. We developed a benchmarking system for Coq generation methods, available in the plugin, and conducted an experiment using it, showcasing the framework's possibilities. Demo of CoqPilot is available at: https://youtu.be/oB1Lx-So9Lo. Code at: https://github.com/JetBrains-Research/coqpilot
Andrei Kozyrev, Gleb Solovev, Nikita Khramov, Anton Podkopaev
ASE3