Just-in-Time Defect Prediction for Self-driving Software via a Deep Learning Model

Abstract

computing application. We use pre-trained unified cross-modal pre-training for code representation (UniXCoder) to embed commit messages and code changes. We use bidirectional-LSTM(Bi-LSTM) for context and semantic learning. As a result of the experiment, it was confirmed that the proposed JIT4EA performed better than state-of-the-art methods and could reduce the code inspection effort.Edge computing is applied to various applications and is typically applied to autonomous driving software. As the self-driving system becomes complicated and the proportion of software increases, accidents caused by software defects increase. Just-in-time (JIT) defect prediction is a technique that identifies defects during the software development phase, which helps developers prioritize code inspection. Many researchers have proposed various JIT models, but it is difficult to find a case in which JIT defect prediction was performed on edge computing applications. In particular, due to the characteristic of self-driving software, which is frequently updated, there is a high risk of inducing defects into the update process. In this work, we propose a JIT defect prediction model via deep learning for edge computing applications called JIT4EA. Our research goal is to develop an effective model to predict defects in edge computing applications. To do this, we perform defect prediction on self-driving software, a representative edge