Reliability Growth Research Articles

A Novel Software Reliability Growth Model Based on Generalized Imperfect Debugging NHPP Framework

A Novel Software Reliability Growth Model Based on Generalized Imperfect Debugging NHPP Framework

Research on Reliability Growth of Shock Absorption System in Rapid Secure Device of Shipboard Helicopter

Research on Reliability Growth of Shock Absorption System in Rapid Secure Device of Shipboard Helicopter

Using Modified Diffusion Models for Reliability Estimation of Open Source Software

Software development is a highly unpredictable process, and ensuring software quality and reliability before releasing it to the market is crucial. One of the common practices during software development is the reuse of code. It can be achieved by utilizing libraries, frameworks, and other reusable components. Practically, when a fault is detected in replicated code, developers must check for similar faults in other copies, as there is a dependency between faults. To prevent recurrence of observed failures, developers must remove the corresponding leading fault and any related dependent faults. Many software reliability growth models (SRGMs) have been proposed and studied in the past, but most SRGMs assume that developers usually detect only one fault causing a failure. In actuality, it is necessary to consider the possibility of detecting multiple faults that may share similarities or dependencies. Additionally, some SRGMs rely on specific assumptions that may not always be valid, such as perfect debugging and/or immediate debugging. In this study, the modified diffusion models are proposed to handle these unrealistic situations, and are expected to better capture the dynamics of open source software (OSS) development. Experiments using real OSS data show that the proposed models can accurately describe the fault correction process of OSS. Finally, an optimal software release policy is proposed and studied. This policy takes into account some factors, including the remaining number of faults in the software, the expenses associated with identifying and rectifying those faults, and the level of market demand for the software. By considering these factors, developers can determine the optimal time to release the software to the market.

Multi-release software reliability assessment: testing coverage-based approach

Software development companies continue to improve their products to stay up with the market's growing needs by adding new features and fixing previously identified bugs. Software reliability growth models (SRGMs) with testing effort function are the incredibly valuable and have been widely utilised by software engineers. Many researchers have built SRGMs that incorporate the concept testing coverage in the model building for the multi-release software system. The proposed model intends to give three different models of multi-release software reliability modelling with testing coverage function. Three models describe testing coverage by exponential function, delayed S-shaped function and logistic function respectively, with the testing effort function assumed to be Weibull in nature. Real-world data is used to estimate parameters in order to validate the proposed SRGM of four releases from Tandem Computers and the model goodness-of-fit is assessed. The results suggest that the proposed model matches the failure data effectively.

Effect of change in environment on reliability growth modeling integrating fault reduction factor and change point: a general approach

Effect of change in environment on reliability growth modeling integrating fault reduction factor and change point: a general approach

Software Reliability Growth Model with Dependent Failures and Uncertain Operating Environments

Software is used in various industries, and its reliability has become an extremely important issue. For example, in the medical industry, software is used to provide medical services to underprivi-leged individuals. If a problem occurs with the software reliability, incorrect medical information may be provided. The software reliability is estimated using a software reliability growth model. However, most software reliability growth models assume that the failures are independent. In addition, it is assumed that the test and operating environments are the same. In this study, we propose a new software reliability growth model that assumes that software failures are dependent and uncertain operating environments. A comparison of the proposed model against existing NHPP SRMEs using actual datasets shows that the proposed model achieves the best fit.

Application of artificial intelligence in early fault detection of transmission line-a case study in India

<span lang="EN-US">Reliable energy is ensured by the power quality, safety and security. For reliability and economic growth of transmission utilities, it is necessary to maintain continuity of supply, which is challenging under deregulated system. It is essential for utilities to conduct regular maintenance of transmission lines before supply interrupts. To protect line from fault, it is necessary to detect fault on line, its classification and location at the earliest. Various smart techniques along with application of artificial intelligence (AI) in power system are under investigation. This paper tries to find solution by identifying practical common faults occurred on transmission lines, and also suggests the suitable maintenance methodology. It uses the artificial neural network (ANN) method and live line maintenance technique (LLMT) for pre identification of a fault and subsequent predictive maintenance. Paper compares results of combination of ANN with LLMT and cold line maintenance technique (CLMT). Comparison of statistical analysis shows combine model of ANN and LLMT results in minimize outage time, failure rate which can improve system availability and increases revenue.</span>

Application-Oriented Reliability Testing of Power Electronic Components and Converters

Power electronics have been and will continue to be an enabling technology for energy production, storage, transmission, distribution, and consumption. Power electronic converters are usually the critical links in electrical energy systems, affecting system security, safety, energy efficiency, and cost-of-ownership. As a result, the reliability requirements for power electronic components and converter systems generally become more stringent, for example, in e-mobility, renewable energy generation, and power system applications. Testing is one of the vital reliability engineering tools to investigate failure mechanisms, identify weakest points, and demonstrate robustness margins. It contributes to reliability growth along the product development process and the likelihood of failure reduction in field operation. The required resources (i.e., testing time, sample size, and testing facility) and the relevance of the testing results to field operation are two crucial considerations in implementing a reliability test. This article introduces the emerging application-oriented testing concepts and facilities through several component-level and converter-level examples.

Performance analysis of Pythagorean fuzzy entropy and distance measures in selecting software reliability growth models using TOPSIS framework

PurposeThe purpose of this paper is to create a numerical technique to tackle the challenge of selecting software reliability growth models (SRGMs).Design/methodology/approachA real-time case study with five SRGMs tested against a set of four selection indexes were utilised to show the functionality of TOPSIS approach. As a result of the current research, rating of the different SRGMs is generated based on their comparative closeness.FindingsAn innovative approach has been developed to generate the current SRGMs selection under TOPSIS environment by blending the entropy technique and the distance-based approach.Originality/valueIn any multi-criteria decision-making process, ambiguity is a crucial issue. To deal with the uncertain environment of decision-making, various devices and methodologies have been explained. Pythagorean fuzzy sets (PFSs) are perhaps the most contemporary device for dealing with ambiguity. This article addresses novel tangent distance-entropy measures under PFSs. Additionally, numerical illustration is utilized to ascertain the strength and authenticity of the suggested measures.

Maintenance effort expense modeling based on cyclic Wiener processes of two types for edge OSS computing

The appropriate control of maintenance effort in the edge computing based on open-source software (OSS) relates to stable and reliable operation, because the fault detection phenomenon depends on the effort expenditure. Actually, several software reliability growth models with testing-effort have been proposed in the past. In this paper, we propose cyclic Wiener process models to consider the network environment under the edge OSS operation. Then, we assume that the sawtooth wave and square one as the cyclic noises. Thereby, the proposed models will be able to comprehend two characteristics of OSS and network environment. Then, it will be useful to assess the operation effort expenditures with network environment of edge OSS service. Moreover, actual effort data sets are analyzed to show numerical examples of the proposed model considering the network environment under the edge OSS operation.

Modelling of NHPP Based Software Reliability Growth Model from the Perspective of Testing Coverage, Error Propagation and Fault Withdrawal Efficiency

This paper presents a software reliability growth model framework modeled from a non-homogenous poisson process (NHPP). Invariably, software testing continues to be a paramount measure for validating the standard of software. Test coverage measures appraise and estimate the proportion and gradation of testing in software. Therefore, presenting a correct picture of the test coverage becomes a prime requisite to guarantee software reliability. As an enhancement over the existing models, the proposed model integrates testing coverage (TC), error propagation, and fault withdrawal efficiency while keeping the number of parameters restrained to make the framework more reliable for parameter estimation. A relative analysis to assess the efficacy of the proposed model and some existing models has been carried out on the failure data obtained from three real-world software applications using six comparison criteria. Finally, the weighted criteria rank method has been used to rank the models and assess their performance. In addition, sensitivity analysis has been carried out to demonstrate the effect of the parameters of the proposed model on the mean value function.

Default Detection Rate-Dependent Software Reliability Model with Imperfect Debugging

From the perspective of FDR (fault detection rate), which is an indispensable component in reliability modeling, this paper proposes two kinds of reliability models under imperfect debugging. This model is a relatively flexible and unified software reliability growth model. First, this paper examines the incomplete phenomenon of debugging and fault repair and established a unified imperfect debugging framework model related to FDR, which is called imperfect debugging type I. Furthermore, it considers the introduction of new faults during debugging and establishes a unified imperfect debugging framework model that supports multiple FDRs, called imperfect debugging type II. Finally, a series of specific reliability models are derived by integrating multiple specific FDRs into two types of imperfect debugging framework models. Based on the analysis of the two kinds of imperfect debugging models on multiple public failure data sets, and the analysis of model performance differences from the perspective of fitting metrics and prediction research, a fault detection rate function that can better describe the fault detection process is found. By incorporating this fault detection rate function into the two types of imperfect debugging models, a more accurate model is obtained, which not only has excellent performance and is superior to other models but also describes the real testing process more accurately and will guide software testers to quantitatively improve software reliability.

Ranking of Key Components of CNC Machine Tools Based on Complex Network

Complex networks have become a center of interdisciplinary research. The robustness and reliability of complex systems can be determined by analyzing the characteristics of networks. The computer numerical control machine tool (CNCMT) is a typical complex system, and reliability is the key factor that affects the processing quality and performance of the CNCMT. The process of quantitatively determining the key functional components of CNCMT and carrying out reliability growth experiments on them remains difficult. A new importance-ranking method for the key functional components of the CNCMT is proposed in this study. First, the real characteristics of the CNCMT are analyzed, and the complex network of the CNCMT is constructed. Then, we study various indicators of the network and determine that the network has small-world characteristics. Finally, a variety of complex network characteristic indicators are fused, the weight of each indicator is determined based on the analytic hierarchy process (AHP), the importance of each node is quantitatively determined, and the fused indicators are compared with a single indicator.

Recursive approach for multiple step-ahead software fault prediction through long short-term memory (LSTM)

The advancement of technologies demands a sustainable solution. To ensure the software system’s sustainability, diminishing the software faults before the implementation requires utmost attention, along with an effective procedure to predict the faults. A software system’s maximum number of faults can be neutralized if it can be predicted at the earliest possible time. Therefore, we applied Long short-term memory (LSTM) to predict the faults of multi-time stamps ahead using a recursive approach. The Min-Max scaler and one of the power transformation methods, Box-Cox are used to normalize the software fault data. The traditional software reliability growth models (SRGMs) are also used to predict faults. The performance of the LSTM and SRGMs models are compared based on their prediction accuracy evaluation. The observed prediction error of LSTM models is much lower than the SRGMs.

On general uncertainty data and general uncertainty variable for reliability growth analysis of major aerospace equipment

PurposeThe purpose of this paper is to explore new ways and lay a solid foundation to solve the problem of reliability growth analysis of major aerospace equipment with various uncertainty data through propose new concepts of general uncertainty data (GUD) and general uncertainty variable (GUV) and build the operation system of GUVs.Design/methodology/approachThe characteristics of reliability growth data of major aerospace equipment and the limitations of current reliability growth models have been analyzed at first. The most commonly used uncertainty system analysis methods of probability statistics, fuzzy mathematics, grey system theory and rough set theory have been introduced. The concepts of GUD and GUV for reliability growth data analysis of major aerospace equipment are proposed. The simplified form of GUV based on the “kernel” and the degree of uncertainty of GUV is defined. Then an operation system of GUVs is built.Findings(1) The concept of GUD; (2) the concept of GUV; (3) The novel operation rules of GUVs with simplified form.Practical implicationsThe method exposed in this paper can be used to integrate complex reliability growth data of major aerospace equipment. The reliability growth models based on GUV can be built for reliability growth evaluation and forecasting of major aerospace equipment in practice. The reliability evaluation example of a solid rocket motor shows that the concept and idea proposed in this paper are feasible. The research of this paper opens up a new way for the analysis of complex uncertainty data of reliability growth of major aerospace equipment. Moreover, the operation of GUVs could be extended to the case of algebraic equation, differential equation and matrix which including GUVs.Originality/valueThe new concepts of GUD and GUV are given for the first time. The novel operation rules of GUVs with simplified form were constructed.

A highly accelerated stress reliability growth test method

AbstractReliability growth test (RGT) is one of the main methods to improve product reliability. In order to effectively solve the problem of short development cycles faced by enterprises, the accelerated reliability growth test (ARGT) is developed. Since the traditional ARGT is carried out separately under all of the accelerated stress levels, and the time of RGT is too long under low accelerated stress levels. So, a highly accelerated stress reliability growth test (HARGT) is proposed, where the RGT is designed to carry out at the highest accelerated stress level. Firstly, AMSAA model is adopted to track the reliability growth process under the highest accelerated stress level, and its parameters are determined by the maximum likelihood estimation method. Secondly, taking temperature as the accelerated stress, the relationship of the reliability with different accelerated stress levels is established under the accelerated life test (ALT) in the first stage. Based on the Arrhenius model and combined with the acceleration coefficient, the product reliability at the end of the test is extrapolated to the product reliability at the normal stress level. The parameters of the Arrhenius model are got through the least square estimation method, and the acceleration coefficient and equation are also obtained. Finally, a case study on the communication service system is given to show the effectiveness of the proposed method.

Generalized power Weibull software reliability growth model

Software usage in various industries and homes forms part of our daily lives. Thus, performing quality control checks on developed software by software engineers is essential to both the developer and the users. The general idea of software reliability as a quality control check is very important to both the producer and the consumer. This paper offers a Generalized Power Weibull software reliability growth model that takes environmental factors into account. The applications of the proposed software reliability growth model reveals that it performs better than some of the existing competing models. Finally, a sequential probability ratio test for the proposed model was developed for monitoring of reliability data.

Soft Hunting Algorithm for Auto-Tuning Software Reliability Growth Models

Nature-inspired algorithms are popular for auto-tuning software reliability growth models in recent decades due to their derivative-free natural tendency to circumvent the local optima problem. These methods have indeed exhibited enormous effectiveness in estimating software dependability. The goal of this study is to present a new nature-inspired approach for parameter improvement of system reliability predictions based on the hunting abilities of smoother-coated otters. The otters’ most notable characteristic is their ability to hunt in groups. In this study, clever otter hunting behavior is used to enhance reliability engineering parameters of the model. Otters work well together and have a strong sense of teamwork. Matte finish coated otters’ smart fish scavenging capacity distinguishes them from many other swarm intelligence-based techniques. Three stages of otter searching are accomplished: traveling in a V formation in the direction of the prey’s movements, advancing forcefully via the stream, and then assaulting the prey on the beach. Three software dependability models are utilized to validate the applicability of the suggested approach. The study’s findings demonstrate that the suggested algorithm outperformed the ABC, GA, and PSO algorithms by 75% and 50%, respectively, in terms of reduced SSE and lower MSE. The smooth-coated otter’s cognitive foraging behavior gives great gain capabilities in parameters software cost estimation system reliability analysis. The outcomes are encouraging for auto-tuning software reliability growth models. Smooth-covered otter optimization can also be used to solve other efficiency challenges.

A model of software fault detection and correction processes considering heterogeneous faults

AbstractNumerous software reliability growth models (SRGMs) are proposed in the last four decades to track the reliability growth of software during the testing phase. In many existing models, it is assumed that the faults in the software are of the same type. However, this assumption may not be realistic. Software contains different faults and the detection/correction of different faults can require different efforts. In this paper, we propose a more general software reliability model considering heterogeneous faults, where the parameters for the detection time and correction delay of different faults are assumed to be faults specific and allowed to follow arbitrary distributions. Considering different parameters makes the model more flexible and adaptable to different practical software development situations. Some specific models are derived under different assumptions on the distributions of fault detection time, fault correction delay, and the corresponding parameters. Both MLE and LSE are proposed for parameter estimation. Illustrative examples with real data set are studied to compare the proposed models with homogeneous faults model, and the results have shown advantages of our models. The optimal software release policy based on the proposed model is also studied.

A Software Reliability Model Using Fault Removal Efficiency

With the increase of human dependency over computer software, considerable effort has been given to determine software reliability effectively. A huge variety of software reliability growth models (SRGMs) have been developed to explain statistically how system reliability varies over time by monitoring the failure data sets during the testing process. The paper proposes a new SRGM based on taking into account the fault removal efficiency which is the ratio of corrected and detected faults during the testing process. The new model is compared to some known model from the relevant literature for two certain data sets and it turns out to perform better in terms of four GOF benchmarks.