Analyzing the Influence of Processor Speed and Clock Speed on Remaining Useful Life Estimation of Software Systems

Prognostics and Health Management (PHM) is a discipline focused on predicting the point at which systems or components will cease to perform as intended, typically measured as Remaining Useful Life (RUL). RUL serves as a vital decision-making tool for contingency planning, guiding the timing and nature of system maintenance. Historically, PHM has primarily been applied to hardware systems, with its application to software only recently explored. In a recent study we introduced a methodology and demonstrated how changes in software can impact the RUL of software. However, in practical software development, real-time performance is also influenced by various environmental attributes, including operating systems, clock speed, processor performance, RAM, machine core count and others. This research extends the analysis to assess how changes in environmental attributes, such as operating system and clock speed, affect RUL estimation in software. Findings are rigorously validated using real performance data from controlled test beds and compared with predictive model-generated data. Statistical validation, including regression analysis, supports the credibility of the results. The controlled test bed environment replicates and validates faults from real applications, ensuring a standardized assessment platform. This exploration yields actionable knowledge for software maintenance and optimization strategies, addressing a significant gap in the field of software health management

A Review of Software Reliability Testing Techniques

In the era of intelligent systems, the safety and reliability of software have received more attention. Software reliability testing is a significant method to ensure reliability, safety and quality of software. The intelligent software technology has not only offered new opportunities but also posed challenges to software reliability technology. The focus of this paper is to explore the software reliability testing technology under the impact of intelligent software technology. In this study, the basic theories of traditional software and intelligent software reliability testing were investigated via related previous works, and a general software reliability testing framework was established. Then, the technologies of software reliability testing were analyzed, including reliability modeling, test case generation, reliability evaluation, testing criteria and testing methods. Finally, the challenges and opportunities of software reliability testing technology were discussed at the end of this paper

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

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.Comment: This research methodology has opened up new and practical applications in the software domain. In the coming decades, we can expect a significant amount of attention and practical implementation in this area worldwid

A decision-making tool for real-time prediction of dynamic positioning reliability index

PhD ThesisThe Dynamic Positioning (DP) System is a complex system with significant levels of integration between many sub-systems to perform diverse control functions. The extent of information managed by each sub-system is enormous. The sophisticated level of integration between sub-systems creates an array of possible failure scenarios. A systematic analysis of all failure scenarios would be time-consuming and for an operator to handle any such catastrophic situation is hugely demanding. There are many accidents where a failure in a DP system has resulted in fatalities and environmental pollution. Therefore, the reliability assessment of a DP system is critical for safe and efficient operation. The existing methods are time-consuming, involving a lot of human effort which imposes built-in uncertainty and risk in the system during complex operation. This thesis has proposed a framework for a state-of-the-art decision-making tool to assist an operator and prevent incidents by introducing a new concept of Dynamic Positioning – Reliability Index (DP-RI). The DP-RI concept covers three phases, leading to technical suggestions for the operator during complex operations, which are defined as Data, Knowledge, Intelligence, and Action. The proposed framework covers analytics including descriptive, diagnostic, predictive and prescriptive analytics. The first phase of the research involves descriptive and diagnostic analytics by performing big data analytics on the available databases to identify the sub-systems which play critical roles in DP system functionality. The second phase of the research involves a novel approach where predictive analytics are used for the weight assignment of the sub-systems, dynamic reliability modelling and offline and realtime forecasting of DP-RI. The third phase introduces innovative prescriptive analytics to provide possible technical solutions to the operator in a short time during failures in the system to enable them to respond quickly and prevent DP incidents. Thus, the DP-RI acts as an innovative state-of-the-art decision-making tool which can suggest possible solutions to the DPO by using analytics on the knowledge database. The results proved that it is a useful tool if implemented on an actual vessel with diligent integration with the DP control system.Singapore Economic Development Board (EDB) and DNV GL Singapore Pte Ltd