Software fault prediction using deep learning techniques

Abstract

Software fault prediction (SFP) techniques are used to identify faults at the early stages of the software development life cycle (SDLC). We find machine learning techniques as commonly used techniques for SFP as compared to deep learning methods which can produce more accurate results. Deep learning offers exceptional results in a variety of domains such as computer vision, natural language processing, speech recognition, etc. In this study, we use three deep learning methods, namely, Long Short Term Memory (LSTM), Bidirectional LSTM (BILSTM), and Radial Basis Function Network (RBFN) to predict software faults and compare our results with existing models to show how our results are more accurate. In our study, we use Chidamber and Kemerer (CK) metrics-based datasets to conduct experiments and test our proposed algorithm. We conclude that LSTM and BILSTM perform better whereas RBFN is faster in producing the required results. We use k-fold cross validation to do the model evaluation. Our proposed models provide a more accurate and efficient SFP mechanism to software developers.

Predicting the Number of Software Faults using Deep Learning

“…Authors in [22] designed a CNN to predict the number of software faults, utilizing SMOTEND to overcome the problem of imbalanced data. In the most recent considered studies [30,31], other DL techniques were implemented to predict software faults, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BILSTM), and Radial Basis Function Network (RBFN) and to classify the modules into faulty or non-faulty. LSTM and BILSTM gave the better performance with 93.53% and 93.75% accuracy, but RBFN was the fastest [30].…”

“…In the most recent considered studies [30,31], other DL techniques were implemented to predict software faults, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BILSTM), and Radial Basis Function Network (RBFN) and to classify the modules into faulty or non-faulty. LSTM and BILSTM gave the better performance with 93.53% and 93.75% accuracy, but RBFN was the fastest [30]. Utilizing DL models based on Recurrent Neural Networks (RNNs), the accuracy reached 95% [31].…”

“…Additionally, the acquired results were compared with those of well-known ML models to showcase the effectiveness of DL. As far as is known, only the studies [22][23][24][25][26][27][28][29][30][31][32][33][34] have considered DL algorithms to predict the number of faults at the class level.…”

FEDRak: Federated Learning-Based Symmetric Code Statement Ranking Model for Software Fault Forecasting

“…Srinivasa Kumar et al [17] conducted a study on software fault detection and recovery for business operations using an independent program to examine and recover back to normal functionality using the test cases more conventionally. Batool, and Khan [18] have proposed a software fault detection model using deep learning models based on Long Short-Term Memory (LSTM) and Bi-directional Long Short-Term Memory (Bi-LSTM) [19] and Radial Basis Function Network (RBFN) for fault prediction. And the performances of the deep learning models are compared with other conventional approaches, and the experimental results have proven that LSTM and Bi-LSTM have yielded better accuracy of 93.66% and 93.45%, respectively.…”