186 citations · 336 across the 13 of their papers we have counts for
13 papers
AfterVibe: What Remains When the Conversation Ends
Matteo Paltenghi, Satish Chandra
We present AfterVibe, a framework that recovers natural-language specifications from a vibe coding session. Given a code artifact and the conversation trajectory that produced it,…
REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage
Smriti Jha, Matteo Paltenghi, Chandra Maddila +3
Production deployment of AI coding agents requires fast, reproducible evaluation signals. Existing industrial practices trade off speed and fidelity: online A/B testing takes weeks…
Wink: Recovering from Misbehaviors in Coding Agents
Rahul Nanda, Chandra Maddila, Smriti Jha +3
Autonomous coding agents, powered by large language models (LLMs), are increasingly being adopted in the software industry to automate complex engineering tasks. However, these age…
Change And Cover: Last-Mile, Pull Request-Based Regression Test Augmentation
Zitong Zhou, Matteo Paltenghi, Miryung Kim +1
Software is in constant evolution, with developers frequently submitting pull requests (PRs) to introduce new features or fix bugs. Testing PRs is critical to maintaining software…
IterTestQ: Assembly-Level, Cross-Platform Testing of Quantum Computing Platforms
Matteo Paltenghi, Michael Pradel
Quantum computing platforms are susceptible to quantum-specific bugs, such as incorrectly ordering qubits or incorrectly implementing quantum abstractions. These bugs are difficult…
Scaling Parameter-Constrained Language Models with Quality Data
Ernie Chang, Matteo Paltenghi, Yang Li +7
Scaling laws in language modeling traditionally quantify training loss as a function of dataset size and model parameters, providing compute-optimal estimates but often neglecting…
A Survey on Testing and Analysis of Quantum Software
Matteo Paltenghi, Michael Pradel
Quantum computing is getting increasing interest from both academia and industry, and the quantum software landscape has been growing rapidly. The quantum software stack comprises…
Analyzing Quantum Programs with LintQ: A Static Analysis Framework for Qiskit
Matteo Paltenghi, Michael Pradel
As quantum computing is rising in popularity, the amount of quantum programs and the number of developers writing them are increasing rapidly. Unfortunately, writing correct quantu…
Fuzz4All: Universal Fuzzing with Large Language Models
Chunqiu Steven Xia, Matteo Paltenghi, Jia Le Tian +2
Fuzzing has achieved tremendous success in discovering bugs and vulnerabilities in various software systems. Systems under test (SUTs) that take in programming or formal language a…
Where to Look When Repairing Code? Comparing the Attention of Neural Models and Developers
Dominik Huber, Matteo Paltenghi, Michael Pradel
Neural network-based techniques for automated program repair are becoming increasingly effective. Despite their success, little is known about why they succeed or fail, and how the…
Follow-up Attention: An Empirical Study of Developer and Neural Model Code Exploration
Matteo Paltenghi, Rahul Pandita, Austin Z. Henley +1
Recent neural models of code, such as OpenAI Codex and AlphaCode, have demonstrated remarkable proficiency at code generation due to the underlying attention mechanism. However, it…
MorphQ: Metamorphic Testing of the Qiskit Quantum Computing Platform
Matteo Paltenghi, Michael Pradel
As quantum computing is becoming increasingly popular, the underlying quantum computing platforms are growing both in ability and complexity. Unfortunately, testing these platforms…
Bugs in Quantum Computing Platforms: An Empirical Study
Matteo Paltenghi, Michael Pradel
The interest in quantum computing is growing, and with it, the importance of software platforms to develop quantum programs. Ensuring the correctness of such platforms is important…