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.

A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification

In this paper we present a novel solution that combines the capabilities of Large Language Models (LLMs) with Formal Verification strategies to verify and automatically repair software vulnerabilities. Initially, we employ Bounded Model Checking (BMC) to locate the software vulnerability and derive a counterexample. The counterexample provides evidence that the system behaves incorrectly or contains a vulnerability. The counterexample that has been detected, along with the source code, are provided to the LLM engine. Our approach involves establishing a specialized prompt language for conducting code debugging and generation to understand the vulnerability's root cause and repair the code. Finally, we use BMC to verify the corrected version of the code generated by the LLM. As a proof of concept, we create ESBMC-AI based on the Efficient SMT-based Context-Bounded Model Checker (ESBMC) and a pre-trained Transformer model, specifically gpt-3.5-turbo, to detect and fix errors in C programs. Our experimentation involved generating a dataset comprising 1000 C code samples, each consisting of 20 to 50 lines of code. Notably, our proposed method achieved an impressive success rate of up to 80% in repairing vulnerable code encompassing buffer overflow and pointer dereference failures. We assert that this automated approach can effectively incorporate into the software development lifecycle's continuous integration and deployment (CI/CD) process.