Bayesian Software Prediction Models. Volume III. Availability Analysis of Software Systems Under Imperfect Maintenance.

1978 ◽  
Author(s):  
K. Okumoto ◽  
Amrit L. Goel
Keyword(s):  
Prediction Models ◽  
Software Systems ◽  
Imperfect Maintenance ◽  
Availability Analysis

Availability analysis and maintenance optimization for multiple failure mode systems considering imperfect repair

2021 ◽  
pp. 1748006X2110127
Author(s):  
Qingan Qiu ◽  
Baoliang Liu ◽  
Cong Lin ◽  
Jingjing Wang
Keyword(s):  
Failure Modes ◽  
Maintenance Cost ◽  
Imperfect Maintenance ◽  
Maintenance Policy ◽  
Cost Rate ◽  
Availability Analysis ◽  
Long Run ◽  
Optimal Maintenance ◽  
Maintenance Policies ◽  
Periodic Inspections

This paper studies the availability and optimal maintenance policies for systems subject to competing failure modes under continuous and periodic inspections. The repair time distribution and maintenance cost are both dependent on the failure modes. We investigate the instantaneous availability and the steady state availability of the system maintained through several imperfect repairs before a replacement is allowed. Analytical expressions for system availability under continuous and periodic inspections are derived respectively. The availability models are then utilized to obtain the optimal inspection and imperfect maintenance policy that minimizes the average long-run cost rate. A numerical example for Remote Power Feeding System is presented to demonstrate the application of the developed approach.

Building Defect Prediction Models in Practice

2014 ◽  
pp. 540-565 ◽  
Author(s):  
Rudolf Ramler ◽  
Johannes Himmelbauer ◽  
Thomas Natschläger
Keyword(s):  
Data Mining ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Development Project ◽  
Defect Prediction ◽  
Software Systems ◽  
Software Development Project ◽  
Future Version ◽  
Large Software ◽  
Defect Prediction Models

The information about which modules of a future version of a software system will be defect-prone is a valuable planning aid for quality managers and testers. Defect prediction promises to indicate these defect-prone modules. In this chapter, building a defect prediction model from data is characterized as an instance of a data-mining task, and key questions and consequences arising when establishing defect prediction in a large software development project are discussed. Special emphasis is put on discussions on how to choose a learning algorithm, select features from different data sources, deal with noise and data quality issues, as well as model evaluation for evolving systems. These discussions are accompanied by insights and experiences gained by projects on data mining and defect prediction in the context of large software systems conducted by the authors over the last couple of years. One of these projects has been selected to serve as an illustrative use case throughout the chapter.

METRIC SELECTION FOR SOFTWARE DEFECT PREDICTION

2011 ◽  
Vol 21 (02) ◽  
pp. 237-257 ◽  
Author(s):  
HUANJING WANG ◽  
TAGHI M. KHOSHGOFTAAR ◽  
JASON VAN HULSE ◽  
KEHAN GAO
Keyword(s):  
Empirical Study ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Life Cycles ◽  
Defect Prediction ◽  
Software Systems ◽  
Software Projects ◽  
Ensemble Technique ◽  
Defect Prediction Models ◽  
Software Practitioners

Real-world software systems are becoming larger, more complex, and much more unpredictable. Software systems face many risks in their life cycles. Software practitioners strive to improve software quality by constructing defect prediction models using metric (feature) selection techniques. Finding faulty components in a software system can lead to a more reliable final system and reduce development and maintenance costs. This paper presents an empirical study of six commonly used filter-based software metric rankers and our proposed ensemble technique using rank ordering of the features (mean or median), applied to three large software projects using five commonly used learners. The classification accuracy was evaluated in terms of the AUC (Area Under the ROC (Receiver Operating Characteristic) Curve) performance metric. Results demonstrate that the ensemble technique performed better overall than any individual ranker and also possessed better robustness. The empirical study also shows that variations among rankers, learners and software projects significantly impacted the classification outcomes, and that the ensemble method can smooth out performance.

Reliability parameter estimation of repairable systems with imperfect maintenance, repair and overhaul

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Garima Sharma ◽  
Rajiv Nandan Rai
Keyword(s):  
Parameter Estimation ◽  
Data Sets ◽  
Imperfect Maintenance ◽  
Repairable Systems ◽  
Time Data ◽  
Content Type ◽  
Availability Analysis ◽  
Reliability Parameters ◽  
Reliability Modelling ◽  
Proposed Model

PurposeIndustries generally require good maintenance, repair and overhaul (MRO) facilities. Maintenance activities at MRO cover the normal scheduled check-ups known as scheduled preventive maintenance (SPM) whereas an overhaul reviews and rejuvenates the complete system at a scheduled time. The literature is reasonably stocked with reliability modelling of repairable systems considering both the corrective maintenance (CM) and SPM as imperfect. However, in all these situations the overhaul is modelled as perfect repair. Thus, the purpose of this research paper is to develop a mathematical model for the estimation of reliability parameters considering the complete MRO as imperfect.Design/methodology/approachThe paper proposes arithmetic reduction of age (Kijima I) based virtual age model to estimate reliability parameters by considering the complete MRO as imperfect and provides the likelihood and log-likelihood functions for parameter estimation of the proposed model and also presents the various extensions of the proposed model.FindingsFor analysis, two real-time data sets of two components, i.e. turbostarter and plunger pump are considered. The analysis mainly focuses on intensity function and availability of components. The availability analysis of the components directly affects the cost analysis. It is very important to analyze the realistic trend of availability, and the comparative analysis shows that the assumption of perfect overhaul overestimates and minimal overhaul underestimates the performance of the components whereas assumption of imperfect overhaul portraits more sensible deteriorating and availability trend of the components.Originality/valueThe proposed methodology in this paper is a novice and not available in the literature.

scholarly journals Availability Analysis of Software Systems with Rejuvenation and Checkpointing

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 846
Author(s):  
Junjun Zheng ◽  
Hiroyuki Okamura ◽  
Tadashi Dohi
Keyword(s):  
Steady State ◽  
Human Error ◽  
System Modeling ◽  
Regenerative Process ◽  
State System ◽  
Software Systems ◽  
System Availability ◽  
Software Rejuvenation ◽  
Availability Analysis ◽  
Availability Model

In software reliability engineering, software-rejuvenation and -checkpointing techniques are widely used for enhancing system reliability and strengthening data protection. In this paper, a stochastic framework composed of a composite stochastic Petri reward net and its resulting non-Markovian availability model is presented to capture the dynamic behavior of an operational software system in which time-based software rejuvenation and checkpointing are both aperiodically conducted. In particular, apart from the software-aging problem that may cause the system to fail, human-error factors (i.e., a system operator’s misoperations) during checkpointing are also considered. To solve the stationary solution of the non-Markovian availability model, which is derived on the basis of the reachability graph of stochastic Petri reward nets and is actually not one of the trivial stochastic models such as the semi-Markov process and the Markov regenerative process, the phase-expansion approach is considered. In numerical experiments, we illustrate steady-state system availability and find optimal software-rejuvenation policies that maximize steady-state system availability. The effects of human-error factors on both steady-state system availability and the optimal software-rejuvenation trigger timing are also evaluated. Numerical results showed that human errors during checkpointing both decreased system availability and brought a significant effect on the optimal rejuvenation-trigger timing, so that it should not be overlooked during system modeling.

scholarly journals A Comparative Analysis of Filter-Based Feature Selection Methods for Software Fault Prediction

2021 ◽  
pp. 1-7
Author(s):  
Thị Minh Phương Hà ◽  
Thi My Hanh Le ◽  
Thanh Binh Nguyen
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Information Gain ◽  
Feature Selection Method ◽  
Computation Time ◽  
Fault Prediction ◽  
Software Systems ◽  
Selection Methods ◽  
Software Fault Prediction

The rapid growth of data has become a huge challenge for software systems. The quality of fault predictionmodel depends on the quality of software dataset. High-dimensional data is the major problem that affects the performance of the fault prediction models. In order to deal with dimensionality problem, feature selection is proposed by various researchers. Feature selection method provides an effective solution by eliminating irrelevant and redundant features, reducing computation time and improving the accuracy of the machine learning model. In this study, we focus on research and synthesis of the Filter-based feature selection with several search methods and algorithms. In addition, five filter-based feature selection methods are analyzed using five different classifiers over datasets obtained from National Aeronautics and Space Administration (NASA) repository. The experimental results show that Chi-Square and Information Gain methods had the best influence on the results of predictive models over other filter ranking methods.

Building Defect Prediction Models in Practice

2017 ◽  
pp. 324-350
Author(s):  
Rudolf Ramler ◽  
Johannes Himmelbauer ◽  
Thomas Natschläger
Keyword(s):  
Data Mining ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Development Project ◽  
Defect Prediction ◽  
Software Systems ◽  
Software Development Project ◽  
Future Version ◽  
Large Software ◽  
Defect Prediction Models

The information about which modules of a future version of a software system will be defect-prone is a valuable planning aid for quality managers and testers. Defect prediction promises to indicate these defect-prone modules. In this chapter, building a defect prediction model from data is characterized as an instance of a data-mining task, and key questions and consequences arising when establishing defect prediction in a large software development project are discussed. Special emphasis is put on discussions on how to choose a learning algorithm, select features from different data sources, deal with noise and data quality issues, as well as model evaluation for evolving systems. These discussions are accompanied by insights and experiences gained by projects on data mining and defect prediction in the context of large software systems conducted by the authors over the last couple of years. One of these projects has been selected to serve as an illustrative use case throughout the chapter.

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