What You See Is What You Get: Attention-based Self-guided Automatic Unit Test Generation

TL;DR

This paper introduces AUGER, an attention-based self-guided approach for automatic unit test generation that improves defect detection confidence and error triggering efficiency by combining defect prediction with targeted test generation.

Contribution

AUGER is a novel two-stage method that integrates defect detection and error triggering, significantly enhancing test effectiveness over state-of-the-art approaches.

Findings

AUGER improves F1-score and precision in defect detection by up to 35.3% and 40.4%.

It triggers 23 to 84 more errors than existing methods.

Demonstrates good generalization on real-world datasets.

Abstract

Software defects heavily affect software's functionalities and may cause huge losses. Recently, many AI-based approaches have been proposed to detect defects, which can be divided into two categories: software defect prediction and automatic unit test generation. While these approaches have made great progress in software defect detection, they still have several limitations in practical application, including the low confidence of prediction models and the inefficiency of unit testing models. To address these limitations, we propose a WYSIWYG (i.e., What You See Is What You Get) approach: Attention-based Self-guided Automatic Unit Test GenERation (AUGER), which contains two stages: defect detection and error triggering. In the former stage, AUGER first detects the proneness of defects. Then, in the latter stage, it guides to generate unit tests for triggering such an error with the help of critical information obtained by the former stage. To evaluate the effectiveness of AUGER, we conduct a large-scale experiment by comparing with the state-of-the-art (SOTA) approaches on the widely used datasets (i.e., Bears, Bugs.jar, and Defects4J). AUGER makes great improvements by 4.7% to 35.3% and 17.7% to 40.4% in terms of F1-score and Precision in defect detection, and can trigger 23 to 84 more errors than SOTAs in unit test generation. Besides, we also conduct a further study to verify the generalization in practical usage by collecting a new dataset from real-world projects.