Demonstration of a Response Time Based Remaining Useful Life (RUL) Prediction for Software Systems

TL;DR

This paper introduces a novel method for predicting the remaining useful life (RUL) of software systems using response time and usage data, enabling proactive maintenance and management decisions.

Contribution

It applies prognostic health management (PHM) concepts to software, developing a model to predict software RUL based on performance and usage parameters, validated with real data.

Findings

The model accurately predicts software RUL using response time data.

Validation shows strong correlation between predicted and actual RUL.

PHM can be effectively applied to software systems for maintenance planning.

Abstract

Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software. While software does not decay over time, it can degrade over release cycles. Software health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and software performance prediction, exist, but all of these represent diagnostic models built upon historical data, none of which can predict an RUL for software. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, this paper addresses how PHM can be used to make decisions for software systems such as version update and upgrade, module changes, system reengineering, rejuvenation, maintenance scheduling, budgeting, and total abandonment. This paper presents a method to prognostically and continuously predict the RUL of a software system based on usage parameters (e.g., the numbers and categories of releases) and performance parameters (e.g., response time). The model developed has been validated by comparing actual data, with the results that were generated by predictive models. Statistical validation (regression validation, and k-fold cross validation) has also been carried out. A case study, based on publicly available data for the Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to software systems and RUL can be calculated to make system management decisions.