Reliability engineering dates back to reliability studies in the 20th century; since then, various models have been defined and used. Software engineering plays a key role from several viewpoints, but the main concern is that we're moving toward a more connected world, including enterprises and mobile devices. The three articles in this special issue illustrate current trends in this domain.

Reliability Engineering

Handbook of Probabilistic ModelsProbablistic Risk Assessment and Management for Engineers and ScientistsHandbook on Resilience of Socio-Technical SystemsProbability and Risk AnalysisEncyclopedia of Quantitative Risk Analysis and AssessmentFoundations of Risk AnalysisScientific Review of the Proposed Risk Assessment Bulletin from the Office of Management and BudgetReliability Engineering and Risk AnalysisLandslide Risk AssessmentRisk Analysis for Prevention of Hazardous Situations in Petroleum and Natural Gas EngineeringUncertainty and RiskRisk-Based EngineeringOverview of risk-informed decision-making processesMathematical Risk AnalysisRisk AnalysisSafety, Reliability and Risk AnalysisMisconceptions of RiskDeepwater Foundations and Pipeline GeomechanicsRisk and Reliability Analysis: Theory and ApplicationsRisk and Uncertainty in Dam SafetyRisk AssessmentBreakthroughs in Decision Science and Risk AnalysisProbabilistic Risk Analysis for Quality Assurance of Bored Pile FoundationsUncertainty characterization in risk analysis for decision-making practiceUncertainty in Risk AssessmentRisk Analysis in Finance and InsuranceModern Statistical and Mathematical Methods in ReliabilityProbability Methods for Cost Uncertainty AnalysisKnowledge in Risk Assessment and ManagementImproving Homeland Security DecisionsRisk Analysis Foundations, Models, and MethodsRisk Analysis in EngineeringRisk Analysis in EngineeringProbabilistic Safety Assessment and ManagementRisk Analysis and SocietyEnvironmental and Health Risk Assessment and ManagementMulticriteria and Multiobjective Models for

Probabilistic Risk Analysis: Foundations and Methods

Specification Searches: Ad Hoc Inference with Nonexperimental Data

We review basic modeling approaches for failure and mainte- nance data from repairable systems. In particular we consider imperfect re- pair models, defined in terms of virtual age processes, and the trend-renewal process which extends the nonhomogeneous Poisson process and the renewal process. In the case where several systems of the same kind are observed, we show how observed covariates and unobserved heterogeneity can be included in the models. We also consider various approaches to trend testing. Modern reliability data bases usually contain information on the type of failure, the type of maintenance and so forth in addition to the failure times themselves. Basing our work on recent literature we present a framework where the ob- served events are modeled as marked point processes, with marks labeling the types of events. Throughout the paper the emphasis is more on modeling than on statistical inference.

On the statistical modeling and analysis of repairable systems

Modeling and analysis of reliability of multi-release open source software incorporating both fault detection and correction processes

Due to demand of new features and highly reliable software system, the software industries are speeding their up-gradations/add-ons in the software. The life of software is very short in the environment of perfect competition. Therefore the software developers have to come up with successive up gradations to survive. The reported bugs from the existing software and Features added to the software at frequent time intervals lead to complexity in the software system and add to the number of faults in the software. The developer of the software can lose on market share if it neglects the reported bugs and up gradation in the software and on the other hand a software company can lose its name and goodwill in the market if the reported bugs and functionalities added to the software lead to an increase in the number of faults in the software. To capture the effect of faults due to existing software and generated in the software due to add-ons at various points in time, we develop a multi up-gradation, multi release software reliability model. This model uniquely identifies the faults left in the software when it is in operational phase during the testing of the new code i.e. developed while adding new features to the existing software. Due to complexity and incomplete understanding of the software, the testing team may not be able to remove/correct the fault perfectly on observation/detection of a failure and the original fault may remain resulting in the phenomenon known as imperfect debugging, or get replaced by another fault causing error generation The model developed is validated on real data sets with software which has been released in the market with new features four times.

Multi up-gradation software reliability growth model with imperfect debugging

A reliability decision framework for multiple repairable units

This paper proposes a model selection framework for analysing the failure data of multiple repairable units when they are working in different operational and environmental conditions. The paper pr ...

Reliability Modelling of Multiple Repairable Units

An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications.

https://www.mdpi.com/1424-8220/19/3/511/pdf

Mobile User Indoor-Outdoor Detection Through Physical Daily Activities.

Several factors such as reliability, availability, and cost may consider in the maintenance modeling. In order to develop an optimal inspection program, it is necessary to consider the simultaneous effect of above factor in the model structure. In addition, for finding the optimal maintenance interval it is necessary to make trade-offs between several factors, which may conflicting each other as well. The study comprises of mathematical formulating an optimal interval problem based on Multi-Attribute Utility Theory (MAUT). The aim of the proposed research is to develop a methodology with supporting tools for determination of optimal inspection in a maintenance planning to assure and preserve a desired level of performance measure such as reliability, availability, risk, etc. For verification and validation purposes, the proposed methodology (analysis approach) and tools (models) will be applied in a real case which given by the literature.

Amir Soleimani Garmabaki

Papers

Multi up-gradation software reliability growth model with imperfect debugging

A reliability decision framework for multiple repairable units

Reliability Modelling of Multiple Repairable Units

Mobile User Indoor-Outdoor Detection Through Physical Daily Activities.

Maintenance Optimization Using Multi-attribute Utility Theory