Software Defect Prediction and Automatic Repair Based on Machine Learning

Abstract

Software defects can have a significant impact on quality, reliability, and development costs if not addressed properly. With the increasing complexity of modern software, traditional testing methods struggle to keep up. Machine learning and deep learning have emerged as promising new approaches for software defect prediction (SDP) and automatic repair. These approaches leverage historical data to identify patterns associated with defects. Deep learning models can automatically learn complex feature representations from raw code. However, imbalanced defect data can bias models, necessitating techniques such as data augmentation or cost-sensitive learning. Memory leaks are a common defect that can gradually deplete resources if left unrepaired. Long short-term memory (LSTM) networks are well-suited for sequential code analysis and can exploit long-term dependencies to learn complex patterns and identify leak scenarios. This research aims to advance SDP and repair through machine learning techniques.