186 citations · 265 across the 12 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…
QITE: Assembly-Level, Cross-Platform Testing of Quantum Computing Platforms
Matteo Paltenghi, Michael Pradel
Quantum computing platforms are susceptible to quantum-specific bugs (e.g., incorrect ordering of qubits or incorrect implementation of quantum abstractions), which are difficult t…
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…