Enhancing Prediction Reliability of Software Failure Using Recurrent Neural Networks

Okoro Christian Chukwuka, Abdulsalam Ya'u Gital, Imam Mustapha, Lawal Abdulrahman, Patrick Fatima, Patrick Obed, Shima Stephen

Abstract


The prediction of software failures is done by using the historical failures collected previously when they occur. Software failure is said to occur when the software runs in an operational profile. Controlling failures in software require that one can predict problems early enough to take preventive measure. Predicting defective code in the software development process is a crucial aspect of software analytics. Recently, a number of computational approaches were employed to solve this task, modern software is developed with more sizes and functions, and assessing software defects is a remarkably difficult task. These approaches have significantly facilitated the prediction of software defects; however, their performances e.g., mean square error (MSE) and squared correlation coefficient (R2) requires significant improvements. The recently developed deep learning model such as LSTM has shown to be suitable for good performance. This deep learning model do not only deepens the layer levels but can as well adapt to capture the training characteristics. A comprehensive, in-depth study ultimately shows the model can have suitable performance. Thus, this research work proposed a deep learning approach, specifically the novel deep recurrent neural network (MLP-LSTM) in order to enhance the performance of software defect prediction on five benchmark datasets obtained from NASA project available at PROMISE repository portal. We evaluate the performance of the proposed model against the state-of-the-art approach using mean square error (MSE) and error correlation (R2). Simulation results show that the proposed approach achieved an excellent performance in terms of MSE as against the existing work on the benchmark KC2 and MIS dataset. In addition, the proposed approach outperformed the existing approaches in all cases of the software defect datasets (CM1, JM1, KC1, KC2 and PC1) that were used for evaluating the experiment both in terms of MSE and R2.


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