5 papers
Graphical-Probabilistic Modeling of Generative Flows in LLM-Native Software Systems
VÃctor A. Braberman, Flavia Bonomo-Braberman
Engineering LLM-native software remains a challenging and immature field. Current practice is largely exploratory, relying on experimentation and heuristic techniques such as promp…
TDAD: Test-Driven Agentic Development - Reducing Code Regressions in AI Coding Agents via Graph-Based Impact Analysis
Pepe Alonso, Sergio Yovine, Victor A. Braberman
AI coding agents can resolve real-world software issues, yet they frequently introduce regressions -- breaking tests that previously passed. Current benchmarks focus almost exclusi…
Scaling GR(1) Synthesis via a Compositional Framework for LTL Discrete Event Control
Hernan Gagliardi, Victor Braberman, Sebastian Uchitel
We present a compositional approach to controller synthesis of discrete event system controllers with linear temporal logic (LTL) goals. We exploit the modular structure of the pla…
Generative transformations and patterns in LLM-native approaches for software verification and falsification
VÃctor A. Braberman, Flavia Bonomo-Braberman, Yiannis Charalambous +3
The emergence of prompting as the dominant paradigm for leveraging Large Language Models (LLMs) has led to a proliferation of LLM-native software, where application behavior arises…
Towards a Probabilistic Framework for Analyzing and Improving LLM-Enabled Software
Juan Manuel Baldonado, Flavia Bonomo-Braberman, VÃctor Adrián Braberman
Ensuring the reliability and verifiability of large language model (LLM)-enabled systems remains a significant challenge in software engineering. We propose a probabilistic framewo…