Multimodal Learning for Just-In-Time Software Defect Prediction in Autonomous Driving Systems

TL;DR

This paper introduces a multimodal learning approach using transformers for just-in-time defect prediction in autonomous driving software, improving safety and reliability by effectively integrating diverse data types.

Contribution

It presents a novel multimodal transformer-based model that combines code, change metrics, and contextual data for defect prediction in autonomous driving systems.

Findings

Outperforms existing deep learning models on open-source datasets

Effectively integrates multiple data modalities using attention mechanisms

Enhances software safety and reliability in autonomous driving

Abstract

In recent years, the rise of autonomous driving technologies has highlighted the critical importance of reliable software for ensuring safety and performance. This paper proposes a novel approach for just-in-time software defect prediction (JIT-SDP) in autonomous driving software systems using multimodal learning. The proposed model leverages the multimodal transformers in which the pre-trained transformers and a combining module deal with the multiple data modalities of the software system datasets such as code features, change metrics, and contextual information. The key point for adapting multimodal learning is to utilize the attention mechanism between the different data modalities such as text, numerical, and categorical. In the combining module, the output of a transformer model on text data and tabular features containing categorical and numerical data are combined to produce the predictions using the fully connected layers. Experiments conducted on three open-source autonomous driving system software projects collected from the GitHub repository (Apollo, Carla, and Donkeycar) demonstrate that the proposed approach significantly outperforms state-of-the-art deep learning and machine learning models regarding evaluation metrics. Our findings highlight the potential of multimodal learning to enhance the reliability and safety of autonomous driving software through improved defect prediction.