ReAssert: Deep Learning for Assert Generation

TL;DR

ReAssert leverages deep learning, specifically Reformer, to generate accurate and diverse JUnit asserts for software testing, outperforming previous methods like ATLAS in precision and uniqueness.

Contribution

This work introduces ReAssert, a novel deep learning approach that directly generates JUnit asserts from methods-under-test using code traceability and advanced models.

Findings

ReAssert achieves up to 44% exact match with ground truth asserts.

Reformer produces 71% unique asserts, the highest among tested models.

ReAssert outperforms ATLAS in accuracy and assert diversity.

Abstract

The automated generation of test code can reduce the time and effort required to build software while increasing its correctness and robustness. In this paper, we present RE-ASSERT, an approach for the automated generation of JUnit test asserts which produces more accurate asserts than previous work with fewer constraints. This is achieved by targeting projects individually, using precise code-to-test traceability for learning and by generating assert statements from the method-under-test directly without the need to write an assert-less test first. We also utilise Reformer, a state-of-the-art deep learning model, along with two models from previous work to evaluate ReAssert and an existing approach, known as ATLAS, using lexical accuracy,uniqueness, and dynamic analysis. Our evaluation of ReAssert shows up to 44% of generated asserts for a single project match exactly with the ground truth, increasing to 51% for generated asserts that compile. We also improve on the ATLAS results through our use of Reformer with 28% of generated asserts matching exactly with the ground truth. Reformer also produces the greatest proportion of unique asserts (71%), giving further evidence that Reformer produces the most useful asserts.