Recursive approach for multiple step-ahead software fault prediction through long short-term memory (LSTM)

Abstract

The advancement of technologies demands a sustainable solution. To ensure the software system’s sustainability, diminishing the software faults before the implementation requires utmost attention, along with an effective procedure to predict the faults. A software system’s maximum number of faults can be neutralized if it can be predicted at the earliest possible time. Therefore, we applied Long short-term memory (LSTM) to predict the faults of multi-time stamps ahead using a recursive approach. The Min-Max scaler and one of the power transformation methods, Box-Cox are used to normalize the software fault data. The traditional software reliability growth models (SRGMs) are also used to predict faults. The performance of the LSTM and SRGMs models are compared based on their prediction accuracy evaluation. The observed prediction error of LSTM models is much lower than the SRGMs.

Subject Classification:

• Primary 62M20
• 92B20
• 91B62
• Secondary 62M10

Keywords:

• Software reliability
• Long short-term memory
• Software reliability growth model
• Min-max scaler
• Box-cox transformation
• Multi-steps ahead fault prediction
• Time series