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

• URL: http://arxiv.org/abs/2307.12237v1
• Date: Sun, 23 Jul 2023 06:06:38 GMT
• Title: Demonstration of a Response Time Based Remaining Useful Life (RUL) Prediction for Software Systems
• Authors: Ray Islam (Mohammad Rubyet Islam), Peter Sandborn
• Abstract summary: Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation.
• Score: 0.966840768820136
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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