A Reflection on Change Classification in the Era of Large Language Models

Abstract

Change classification, today known as Just-in-Time Defect Prediction, is a technique for predicting software bugs at the change level of granularity. Several ideas came together to form change classification: predictions on code changes, using word-level textual features, use of machine learning classifiers, and leveraging open source code repositories. While change classification has led to a robust line of research, it has not yet had significant industrial adoption. A key recommendation is to explore explainability features so developers can better understand why a prediction is being made. We explore how large language models can advance this work by providing prediction explanations and bug fix suggestions. © 1976-2012 IEEE