by A group of computer scientists led by the University of Massachusetts Amherst has introduced a novel approach to automatically generating comprehensive proofs to prevent software bugs and validate the accuracy of the underlying code. The researchers named their innovative method “Baldur,” which capitalizes on the capabilities of large language models (LLMs) driven by artificial intelligence. When combined with the advanced tool Thor, Baldur achieves an unprecedented efficacy rate of nearly 66%. The team’s significant achievement was recently recognized with a Distinguished Paper award at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering.
Yuriy Brun, a professor at UMass Amherst’s Manning College of Information and Computer Sciences and the senior author of the paper, acknowledged the prevalent issue of software bugs, despite the widespread usage of software in our daily lives. He emphasized the potential range of negative impacts caused by faulty software, from minor inconveniences like formatting issues or frequent crashes to more severe consequences, such as security breaches or errors in critical software used for space exploration or healthcare devices.
Traditionally, there have been various methods to check software for errors. One common approach involves manual verification, where a human meticulously examines the code line by line to identify any mistakes. Alternatively, running the code and comparing its output to the expected results can be used. However, both methods are susceptible to human error, time-consuming, expensive, and impractical for complex systems.
A more rigorous but challenging approach is to create a mathematical proof demonstrating that the code functions as expected, and then use a theorem prover to validate the accuracy of the proof. This technique is known as machine-checking. However, manually creating these proofs is extremely time-consuming and requires extensive expertise. In fact, the length of these proofs can often exceed the length of the software code itself, according to Emily First, the lead author of the paper and a researcher who conducted this study during her doctoral dissertation at UMass Amherst.
With the emergence of LLMs, such as the well-known ChatGPT, an automatic generation of proofs became a possible solution. However, a significant challenge with LLMs is their tendency to provide incorrect answers without crashing or signaling an error. These LLMs tend to “fail silently,” leading to potential problems when using them for software verification.
To address this challenge, the researchers developed Baldur. First, who conducted the research as part of her work at Google, utilized Minerva, an LLM trained on a vast dataset of natural language texts. She fine-tuned Minerva on 118GB of scientific papers containing mathematical expressions. Furthermore, she applied additional fine-tuning on a language called Isabelle/HOL, which is commonly used for writing mathematical proofs. Baldur then generated complete proofs and collaborated with the theorem prover to validate its work. If an error was detected, the proof, along with information about the error, was fed back into the LLM. This iterative process enabled the LLM to learn from its mistakes and generate new, error-free proofs.
This approach significantly enhances the accuracy of software verification. While the state-of-the-art tool for automated proof generation, Thor, achieves a 57% success rate, pairing Baldur with Thor increases the rate to 65.7%.
Although there is still room for improvement, Baldur stands out as the most effective and efficient method currently available for validating software correctness. As AI capabilities continue to advance, Baldur’s effectiveness is expected to further increase.
The research paper detailing Baldur’s development is published as part of the Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering.
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1. Source: Coherent Market Insights, Public sources, Desk research
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