Use of deep learning in early software bug detection
DOI:
https://doi.org/10.22581/muet1982.0331Keywords:
Deep Learning , CNN , Random Forest , Software Bug Detection , Ensemble Approach , Voting EnsembleAbstract
In the software maintenance and development process, software bug identification is a significant challenge because this is connected with the successful software in its whole state. To increase software efficiency, dependability, and quality early software problem identification is important. Therefore, an efficient software bug detection model is a critically significant challenging operation, and that has been developed in this work by designing the efficient model while using the promises dataset as input for bug identification. An ensemble technique approach is proposed to increase the performance of detecting software bugs for moderate to large datasets. This was achieved by using an ensemble parallel technique for training the Convolutional Neural Network and Random Forest algorithm in a parallel way. Then, the predictions from both models are aggregated using an ensemble voting technique. The performance of the implied technique is evaluated using various kinds of metrics, including accuracy, F-measure, recall, and precision. Experimental findings show significant improvements in prediction accuracy and F1 scores compared to the standalone CNN model, including increases of 33.06% for Camel, 13.43% for Jedit, 50.85% for Xerces, and 18.35% for Synapse. These findings highlight the potential of ensemble learning techniques for enhancing software bug detection.
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