109 citations · 119 across the 8 of their papers we have counts for
8 papers
Good Vibrations? A Qualitative Study of Co-Creation, Communication, Flow, and Trust in Vibe Coding
Veronica Pimenova, Sarah Fakhoury, Christian Bird +2
Vibe coding, a term coined by Andrej Karpathy in February 2025, has quickly become a compelling and controversial natural language programming paradigm in AI-assisted software deve…
What's in a Proof? Analyzing Expert Proof-Writing Processes in F* and Verus
Rijul Jain, Shraddha Barke, Gabriel Ebner +3
Proof-oriented programming languages (POPLs) empower developers to write code alongside formal correctness proofs, providing formal guarantees that the code adheres to specified re…
DiffSpec: Differential Testing with LLMs using Natural Language Specifications and Code Artifacts
Nikitha Rao, Elizabeth Gilbert, Harrison Green +4
Differential testing can be an effective way to find bugs in software systems with multiple implementations that conform to the same specification, like compilers, network protocol…
3DGen: AI-Assisted Generation of Provably Correct Binary Format Parsers
Sarah Fakhoury, Markus Kuppe, Shuvendu K. Lahiri +2
Improper parsing of attacker-controlled input is a leading source of software security vulnerabilities, especially when programmers transcribe informal format descriptions in RFCs…
Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming
Saikat Chakraborty, Gabriel Ebner, Siddharth Bhat +4
Proof-oriented programs mix computational content with proofs of program correctness. However, the human effort involved in programming and proving is still substantial, despite th…
LLM-Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluation
Sarah Fakhoury, Aaditya Naik, Georgios Sakkas +2
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, g…
Ranking LLM-Generated Loop Invariants for Program Verification
Saikat Chakraborty, Shuvendu K. Lahiri, Sarah Fakhoury +6
Synthesizing inductive loop invariants is fundamental to automating program verification. In this work, we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable…
Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?
Madeline Endres, Sarah Fakhoury, Saikat Chakraborty +1
Informal natural language that describes code functionality, such as code comments or function documentation, may contain substantial information about a programs intent. However,…