International Journal of Reliability, Quality and Safety Engineering

Detecting fraudulent activities in credit card transactions can be challenging due to issues like high dimensionality and class imbalance that are often present in the datasets. To address these challenges, data reduction techniques such as data sampling and feature selection have become essential. In this study, we compare four approaches for data reduction: using data sampling alone, employing feature selection alone, applying data sampling followed by feature selection, and using feature selection followed by data sampling. Additionally, we include results using all features. We build classification models using five Decision Tree-based classifiers and Logistic Regression, and evaluate their performance using two performance metrics: the Area Under the Receiver Operating Characteristic Curve (AUC) and the Area under the Precision–Recall Curve (AUPRC). In this work, we adopt ensemble supervised feature selection (SFS) techniques and Random Undersampling (RUS) for data reduction. The experimental results demonstrate that all four data reduction techniques have the potential to improve the performance of classifiers. These results are valuable since the classifiers available are dependent upon application domains, computing environments, and licensing agreements. However, these techniques can be applied independently of all these dependencies. We recommend utilizing the ensemble SFS followed by RUS (SFS–RUS) approach as the preferred data reduction method due to its ability to run feature selection and data sampling in parallel. Additionally, we find that XGBoost and CatBoost outperform other classifiers.

The Concept of bugs’ infusion has been a very prominent area of research concerning Software Reliability Growth Modeling. The literature is evidence of some very useful proposals concerning this domain in terms of dealing with these issues based on error generation. However, whenever this topic is touched, researchers have considered a constant rate of error generation. In this work, authors have utilized two forms of bug infusions in the code. Furthermore, by using an MCDM technique of MAUT, the release time of the software under consideration has been determined. Results provide a satisfactory orientation for using the proposed models based on the infusion of bugs/errors and obtaining optimal release time.

Software reliability growth model (SRGM) is used as one of the reliability assessment methods to assess the software reliability. In SRGM, the degree of reliability growth may fluctuate greatly according to change in the internal state of the software. It is called the change point (CP). Several researchers proposed the SRGM considering CP. In the open source software (OSS), there are many projects that continue development even after the software is released. Therefore, major updates with breaking changes may occur in it. The major updates can be a factor that causes a CP because it greatly changes the internal state of the OSS. This paper focuses on the relationship between CP and software updates. We collect OSS fault data from a bug tracking system. Moreover, we examine the behavior of SRGM before and after software updates. Furthermore, we discuss the applicability of SRGM for CP in OSS. Also, we compare the proposed model based on CP with the model without CP. As a result, we have confirmed that the SRGM can evaluate the reliability in the environment with major updates. Moreover, the proposed method performs better than without considering CP model. Especially, the exponential model’s mean value function is the suitable method to assess the OSS reliability for the proposed method.

In this study, we focus on the reliability evaluation of the linear consecutive-$k$-out-of-$n$:G system, where the system is working if and only if at least $k$ consecutive components work. The multiple state of the system is considered including not only the working and failure state, but also the intermediate state approaching failure, and the reliability evaluation is discussed including the system failure probability, the expected number of failed components and the expected time to the considered intermediate state. We also apply these reliability results to the maintenance problems, and discussed the condition-based maintenance for the system which is based on the state of the system, and give the results of the optimal maintenance interval which minimizes the expected cost rate.

Air transport networks play a significant role in the modern tourism industry, and their operational performance and stability can be measured by reliability analysis. This study, from the perspective of a travel agency, attempts to assess the reliability of a stochastic air transport network (SATN) by considering multiple passenger demands generated by both the starting airport and several connection airports. The SATN is modeled as a stochastic flow network comprised of several nodes and arcs where nodes represent airports, and arcs represent flights connecting two airports. Each flight is associated with a predetermined stochastic capacity following a specific probability distribution, departure time, arrival time, and discount fare. Accordingly, network reliability refers to the probability that the network can successfully transport passengers from their respective source airports to the destination airport within the given travel time and travel cost limit. An algorithm in terms of the concept of lower boundary point is developed to compute network reliability. Furthermore, a simple example and a case study are presented to demonstrate the proposed reliability method and the managerial implications of reliability for travel agencies.

Software reliability modeling, as a non-functional characteristic of software quality to achieve high reliability, is of particular importance. To obtain software systems with high reliability, it is important to select the best scenario, from the various scenarios produced, in a short time during the software design stage. Since the scenario includes interactions between the user and use case, producing various scenarios and selecting the best among them in terms of reliability is a difficult and time-consuming task for software designers. Therefore, it is of great importance to use a modeling language that has a tool-based evaluation modeling approach and does the analysis automatically. In this paper, Alloy modeling language has been used to model and analyze software reliability. Compared to other lightweight modeling languages, Alloy has such advantages as being more concise, accurate and abstract. The primary objective of this paper aims to present a modeling framework based on the Alloy modeling language with which reliability of target systems can be efficiently examined. The results indicate that the use of Alloy analyzer facilitates the immediate production of various scenarios and allows for the selection of the best one in terms of reliability.

In the framework of statistical reliability analysis, we propose a model describing the change of reliability of a technical component after certain event, as for instance a shock, or the failure and maintenance. Two kinds of failure rate changes are considered, namely a shift of virtual age of analyzed component and the acceleration of its subjective time. The objective is to estimate both the baseline lifetime probability distribution and the magnitude of these changes. The estimation procedure is based on the maximum likelihood method, both parametric and nonparametric distributions of lifetime are considered. Further, a graphical method of the goodness-of-fit test is proposed. The method is then checked on artificial examples, finally a real data problem is solved.