Towards a Probabilistic Framework for Analyzing and Improving LLM-Enabled Software

Ensuring the reliability and verifiability of large language model (LLM)-enabled systems remains a significant challenge in software engineering. We propose a probabilistic framework for systematically analyzing and improving these systems by modeling and refining distributions over clusters of semantically equivalent outputs. This framework facilitates the evaluation and iterative improvement of Transference Models -- key software components that utilize LLMs to transform inputs into outputs for downstream tasks. To illustrate its utility, we apply the framework to the autoformalization problem, where natural language documentation is transformed into formal program specifications. Our case illustrates how probabilistic analysis enables the identification of weaknesses and guides focused alignment improvements, resulting in more reliable and interpretable outputs. This principled approach offers a foundation for addressing critical challenges in the development of robust LLM-enabled systems.

Tasks People Prompt: A Taxonomy of LLM Downstream Tasks in Software Verification and Falsification Approaches

Prompting has become one of the main approaches to leverage emergent capabilities of Large Language Models [Brown et al. NeurIPS 2020, Wei et al. TMLR 2022, Wei et al. NeurIPS 2022]. During the last year, researchers and practitioners have been playing with prompts to see how to make the most of LLMs. By homogeneously dissecting 80 papers, we investigate in deep how software testing and verification research communities have been abstractly architecting their LLM-enabled solutions. More precisely, first, we want to validate whether downstream tasks are an adequate concept to convey the blueprint of prompt-based solutions. We also aim at identifying number and nature of such tasks in solutions. For such goal, we develop a novel downstream task taxonomy that enables pinpointing some engineering patterns in a rather varied spectrum of Software Engineering problems that encompasses testing, fuzzing, debugging, vulnerability detection, static analysis and program verification approaches.