# GAssert: A Fully Automated Tool to Improve Assertion Oracles

arXiv: https://arxiv.org/abs/2103.02901 · HTML: https://mlsanity.com/paper/2103.02901/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [The Future of AI-Driven Software Engineering](https://mlsanity.com/paper/2406.07737/) (2024)
- [Agentic Generation and Evolution of Knowledge Models](https://mlsanity.com/paper/2606.03662/) (2026)

## Abstract (arXiv)

This demo presents the implementation and usage details of GASSERT, the first tool to automatically improve assertion oracles. Assertion oracles are executable boolean expressions placed inside the program that should pass (return true) for all correct executions and fail (return false) for all incorrect executions. Because designing perfect assertion oracles is difficult, assertions are prone to both false positives (the assertion fails but should pass) and false negatives (the assertion passes but should fail). Given a Java method containing an assertion oracle to improve, GASSERT returns an improved assertion with fewer false positives and false negatives than the initial assertion. Internally, GASSERT implements a novel co-evolutionary algorithm that explores the space of possible assertions guided by two fitness functions that reward assertions with fewer false positives, fewer false negatives, and smaller size.