Software Fault Localization using CNN-LSTM Model
Vishwank Vikram Singh, Manjubala Bisi · 2024
Software fault localization is a critical task which identify the faulty statements in a program. Traditional fault localization techniques are unable to capture complex relationships within program execution data. In recent years, Convolutional Neural Networks (CNNs) has shown promise in enhancing fault localization performance. In this paper, we propose a CNN-LSTM model for software fault localization. We perform extensive experiments on four programs and observe that proposed CNN-LSTM model consistently outperforms existing methods, achieving higher accuracy and reliability in localizing software faults.