Software Reliability Evaluation Research Articles

A Neural Network Approach for Software Reliability Prediction

The increasing reliance on computer software has raised significant concerns regarding software reliability evaluation. Over the past four decades, various software reliability models have been developed, encompassing both parametric and nonparametric approaches. However, no single model has demonstrated effectiveness in handling all types of datasets. In response to this challenge, the deep neural network (DNN), a powerful deep learning model, has emerged as a promising solution. By leveraging the flexibility and adaptability of artificial neural networks (ANN), the DNN model exhibits remarkable prediction performance by capturing training variables and exploring deeper layers. This study presents an approach that utilizes a DNN model based on an ANN architecture with multiple activation functions. This approach aims to estimate software reliability and predict the frequency of software flaws. Through extensive experimental analysis and validation, the proposed DNN model surpasses the predictive accuracy achieved by existing parametric and nonparametric models. These results highlight the potential of the DNN model in effectively addressing the complexities involved in software reliability evaluation. By combining the power of deep learning with the adaptability of ANN, the proposed model offers a promising and accurate solution for software reliability prediction.

An Open-Source Software Reliability Model Considering Learning Factors and Stochastically Introduced Faults

In recent years, software development models have undergone changes. In order to meet user needs and functional changes, open-source software continuously improves its software quality through successive releases. Due to the iterative development process of open-source software, open-source software testing also requires continuous learning to understand the changes in the software. Therefore, the fault detection process of open-source software involves a learning process. Additionally, the complexity and uncertainty of the open-source software development process also lead to stochastically introduced faults when troubleshooting in the open-source software debugging process. Considering the phenomenon of learning factors and the random introduction of faults during the testing process of open-source software, this paper proposes a reliability modeling method for open-source software that considers learning factors and the random introduction of faults. Least square estimation and maximal likelihood estimation are used to determine the model parameters. Four fault data sets from Apache open-source software projects are used to compare the model performances. Experimental results indicate that the proposed model is superior to other models. The proposed model can accurately predict the number of remaining faults in the open-source software and be used for actual open-source software reliability evaluation.

Change point software belief reliability growth model considering epistemic uncertainties

Various software reliability growth models (SRGMs) have been developed to evaluate software reliability. Since software faults are caused by bugs in the logic of the code, which are introduced due to human errors, software faults embody lots of epistemic uncertainties. Under the framework of uncertainty theory, a software belief reliability growth model (SBRGM) was proposed. Actually, during software testing fault detection rate function may show multiple phases due to changes of testing strategies and resources allocation. Thus, fault detection rate function may be discontinuous at some time known as the change point. Noting that the previous SBRGM did not consider the change point, this paper construct a new SBRGM incorporating change point, named CPSBRGM. Based on the new proposed model, reliability for software is evaluated under the framework of belief reliability theory. Method to estimate unknown parameters in the proposed model is also provided. The new proposed model is validated on three datasets and compared with the previous SBRGM. Results show that the new proposed model can clearly distinguish the change point during software testing, and is more in line with the actual software test situation than previous model without considering change points.

On a cost and availability analysis for software systems via phase type non-homogeneous Poisson process

To build highly reliable software products, software testing is required. The software engineer has a problem determining whether to complete the testing process and when to release the software system to the market. As the focus on high software increases, project managers must consider the cost of testing, testing availability time, and release time strategy. This research article utilizes a phase type non-homogeneous Poisson process to investigate the cost analysis and operational availability of a software reliability model. The software failure inter-arrival time is considered to follow a phase type distribution. The phase type software reliability model is valuable in reducing the time and effort required to select the appropriate models for software reliability evaluation. An explicit expression for the expected, bi-criterion cost analysis and operational availability is derived using standard results of the renewal reward theorem. The optimal software release policy T* is obtained analytically. Numerical illustrations and sensitivity analysis have been presented to illustrate the cost and availability analysis’s efficiency and demonstrate conformity with the study’s observations.

A VIKOR-based group decision-making approach to software reliability evaluation

Software reliability evaluation is important to attribute of software quality. A new group decision-making is provided to evaluate software reliability, where the model is based on an extended VIsekriterijumska optimizacija i KOmpromisno Resenje technique. The individual utility and individual regret in general VIKOR technique are extended to group utility and group regret. A specific regret matrix is provided in new VIKOR-based GDM method. A new ranking method is provided in a static environment. Another new ranking method is provided in a dynamic environment. To implement a user-based evaluation for software reliability, the picture fuzzy set is used for handling the questionnaire information. It is noted that the existing projection measure is not always reasonable in picture fuzzy setting. To solve this problem, a new normalization projection measure is provided in picture fuzzy setting. And a new GDM method is established for software reliability evaluation. The feasibility and practicability developed method in this work are illustrated by an experimental analysis.

A Novel Method for Software Reliability Assessment via Neuro-Fuzzy System

Nowadays, the utilization of software engineering in various areas of technology is remarkably increased. As a matter of fact, it is used in many critical applications such as eye surgery, autopilot systems of airplanes, centralized traffic control (CTC), and so on. Therefore, the reliability of software is very important, and it plays an essential role in the lifetime of the software. Software reliability is one of the main characteristics of software quality. Moreover, the rapid assessment of the reliability of the application is essential during the software life cycle. In this paper, Iuse the neuro-fuzzy methods to assess the software's reliability in order to cope with uncertainties in measuring the actual parameters of the software. By designing neuro-fuzzy inference systems and applying four parameters of the ISO/IEC 9126quality model(i.e., the maturity of software, fault-tolerant, recoverability, and reliability compliance) and finding the parameters of a fuzzy system by exploiting approximation techniques from neural networks, Ipresent an integrated assessment model for evaluation of software reliability. The case study used in this paper to evaluate the proposed method is the software income tax calculator. By applying the input parameters, Iobserve that the software reliability is 0.65. software reliability in our proposed method is more exact than software reliability in the fuzzy multi-criteria and fuzzy method because The weights of the input parameters have been set by experts and software developers, and simulations are carried out using MATLAB tool (ANFIS). Simulations confirm that the proposed method provides acceptable results.

Software Belief Reliability Growth Model Based on Uncertain Differential Equation

Software reliability plays an important role in modern society. To evaluate software reliability, software reliability growth models (SRGMs) investigate the number of software faults in the testing phase. Obviously, testing progresses are inevitably influenced by dynamic indeterministic fluctuations such as the testing effort expenditure, testing efficiency and skill, testing method, and strategy. To model these dynamic fluctuations, several probability theory-based SRGMs are proposed. However, probability theory is suitable for dealing with aleatory uncertainty, but fails to deal with epistemic uncertainty widely existing in software faults. Therefore, this article considers software reliability from a new perspective under the framework of uncertainty theory, which is a new mathematical system different from probability theory, and proposes a software belief reliability growth model (SBRGM) based on uncertain differential equations for the first time. Based on this SBRGM, properties of essential software reliability metrics are investigated under belief reliability theory, which is a brand-new reliability theory. Parameter estimations for unknown parameters in SBRGM are presented. Furthermore, some numerical examples and real data analyses illustrate our methodology in detail, and show that it performs better than several famous probability-based SRGMs in terms of fitting ability and prediction ability. Finally, an optimal software release policy is discussed.

Software Reliability Prediction by using Deep Learning Technique

The importance of software systems and their impact on all sectors of society is undeniable. Furthermore, it is increasing every day as more services get digitized. This necessitates the need for evolution of development and quality processes to deliver reliable software. For reliable software, one of the important criteria is that it should be fault-free. Reliability models are designed to evaluate software reliability and predict faults. Software reliability prediction is always an area of interest in the field of software engineering. Prediction of software reliability can be done using numerous available models but with the inception of computational intelligence techniques, researchers are exploring new techniques such as machine learning, genetic algorithm, deep learning, etc. to develop better prediction models. In the current study, a software reliability prediction model is developed using a deep learning technique over twelve real datasets from different repositories. The results of the proposed model are analyzed and found quite encouraging. The results are also compared with previous studies based on various performance metrics.

Open source software reliability model with nonlinear fault detection and fault introduction

AbstractIn recent years, open source software (OSS) has been widely used and developed. The reliability of OSS has also become a hot research topic. Due to the complexity, dynamics, and uncertainty of the development and testing process of OSS, it is also a difficult task to accurately evaluate the reliability of OSS. In this paper, we consider the nonlinear changes of fault detection and fault introduction in the development and testing of OSS and propose a reliability model of OSS based on the nonlinear changes of fault detection and fault introduction. Experimental results show that the proposed model is more in line with the actual changes of fault detection and fault introduction in the processes of OSS development and testing. Compared with other models, the proposed model has better fitting and predictive performance. We also give a simple OSS optimal release method considering the testing‐effort and reliability changes in the processes of OSS development and testing. The proposed model and optimized release method can effectively assist OSS developers to evaluate software reliability and determine the optimal release time.

W46. CRISPR/CAS9-MEDIATED CREATION OF A CELLULAR MODEL TO INVESTIGATE THE FUNCTION OF EGR1 TRANSCRIPTION FACTOR ASSOCIATED WITH SCHIZOPHRENIA

In complex software, such as various intelligent monitoring and control systems, the requirements for its reliability and safety increase. However, traditional reliability models cannot longer describe its reliability behavior with sufficient accuracy. This paper presents an approach to software reliability assessment using high-order Markov chains on an example of CubeSat nanosatellites flight software. The method uses the developed algorithm of high-order Markov process representation through an equivalent first-order process. The simulation results show that high-order Markov chains can increase the accuracy of the failure rate assessment up to 50%. Hence, the usage of the high-order reliability models is obvious for accurate and reliable evaluation of software reliability and safety.

White-Box and Black-Box Reliability Modeling Framework: Integration Through Analytical Model and User Profile Validation via Deep Learning — A Practitioner’s Approach

Software reliability evaluation of complex systems is always a challenging task with conventional methods comprising both functional as well as nonfunctional aspects of real-world applications. Prevailing model frameworks moreover apply a nonfunctional approach (black-box model) that is modeled on defect data or through a functional approach (white-box model) that uses component or state-based interactions. Also, other challenges involve integrating both approaches, and validating user profiles of software operation. Further, reliability assessment is one among the most important and desirable qualities of service requirements of software systems, particularly in monitoring critical business transactions. Here, we propose a model framework to evaluate the overall reliability estimation involving both functional and nonfunctional model analyses using: (a) white-box assessment based on intercomponent analysis via component-based Cheung’s model and user profile validations with one of the identified deep learning techniques and (b) black-box modeling evaluation via generalized stochastic Petri nets based on orthogonal defect classification. A newly introduced deep learning model using white-box analysis is validated with pertinent usage profiles to establish a new trend in artificial neural networks and as well with software reliability estimation. Additionally, we introduce and present a quantitative technique — analytical hierarchy process — to integrate reliability assessment and provide weights to the white-box and as well for black-box approaches to quantify overall reliability estimation. The proposed framework is illustrated with an application case study.

Modeling Software Fault-Detection and Fault-Correction Processes by Considering the Dependencies between Fault Amounts

Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.

Modelling software reliability growth through generalized inflection S-shaped fault reduction factor and optimal release time

The fault reduction factor (FRF) is a significant parameter for controlling the software reliability growth. It is the ratio of net fault correction to the number of failures encountered. In literature, many factors affect the behaviour of FRF, namely fault dependency, debugging time-lag, human learning behaviour and imperfect debugging. Besides this, several distributions, for example, inflection S-shaped, Weibull and Exponentiated-Weibull, are used as FRF. However, these standard distributions are not flexible to describe the observed behaviour of FRFs. This paper proposes three different software reliability growth models (SRGMs), which incorporate a three-parameter generalized inflection S-shaped (GISS) distribution as FRF. To model realistic SRGMs, time lags between fault detection and fault correction processes are also incorporated. This study proposed two models for the single release, whereas the third model is designed for multi-release software. Moreover, the first model is in perfect debugging, while the rest of the two are in an imperfect debugging environment. The extensive experiments are conducted for the proposed models with six single release and one multi-release data-sets. The choice of GISS distribution as an FRF improves the software reliability evaluation in comparison with the existing systems in the literature. Finally, the development cost and optimal release time are calculated in a perfect debugging environment.

Application of EM Algorithm to NHPP-Based Software Reliability Assessment with Generalized Failure Count Data

Software reliability models (SRMs) are widely used for quantitative evaluation of software reliability by estimating model parameters from failure data observed in the testing phase. In particular, non-homogeneous Poisson process (NHPP)-based SRMs are the most popular because of their mathematical tractability. In this paper, we focus on the parameter estimation algorithm for NHPP-based SRMs and discuss the EM algorithm for generalized fault count data. The presented algorithm can be applied for failure time data, failure count data, and their mixture. The paper derives the EM-step formulas for basic 12 NHPP-based SRMs and demonstrate a numerical experiment to present the convergence property of our algorithms. The developed algorithms are suitable for an automatic tool for software reliability evaluation.

Recent Researches and Technologies on Software Reliability Evaluation

Background: With the rapid development of computer and Internet technologies, the measurement of software reliability has become a key point. Software reliability modeling is an important way to measure software reliability. However, there are many disadvantages in the classic model. Therefore, this paper reviews current researches in software reliability and discusses ways to improve the model from a new perspective. Objective: This paper reviews the current researches on software reliability, analyzes the existing patented technology, and points out some future directions to improve the current models to provide a more accurate and efficient way for software reliability measurement. Methods: This study introduces the general methods of software reliability evaluation, analyzes the disadvantages of these methods in practical application, draws lessons from the patent, and puts forward some new ideas to solve the problems. Results: Disadvantages of current researches are identified based on literature review. In addition, two suggestions are given to improve the current models. Conclusion: Although the software reliability has made great progress, there are still many things to be studied in the future. The problems of software reliability in real life should be gradually solved.

Model of Open Source Software Reliability with Fault Introduction Obeying the Generalized Pareto Distribution

Open source software reliability is an important factor affecting the quality of open source software. The developed reliability models of open source software cannot meet the actual evaluation of open source software reliability because of the complexity, dynamics, and uncertainty of open source software development. Considering the dynamic changes of fault introduction in open source software development, we propose an open source software reliability model with fault introduction based on the generalized Pareto distribution. We use three Apache open source software projects to validate the proposed model. Least squares estimation is used to estimate the model parameter values. Experimental results indicate that the proposed model has better fitting and predictive performance than other existing models. The generalized Pareto distribution of the fault introduction is consistent with that in actual open source software development. Thus, the proposed model can assist developers and managers in evaluating the reliability of open source software in the actual process of open source software development.

Reliability assessment of CubeSat nanosatellites flight software by high-order Markov chains

In complex software, such as various intelligent monitoring and control systems, the requirements for its reliability and safety increase. However, traditional reliability models cannot longer describe its reliability behavior with sufficient accuracy. This paper presents an approach to software reliability assessment using high-order Markov chains on an example of CubeSat nanosatellites flight software. The method uses the developed algorithm of high-order Markov process representation through an equivalent first-order process. The simulation results show that high-order Markov chains can increase the accuracy of the failure rate assessment up to 50%. Hence, the usage of the high-order reliability models is obvious for accurate and reliable evaluation of software reliability and safety.

A New Reliability Model Based on Lindley Distribution with Application to Failure Data

Software reliability is an important feature that influences systems’ reliability. Software reliability models are a common tool to evaluate software reliability quantitatively. Various reliability models have been suggested based on the NHPP (nonhomogeneous Poisson process). In this article, a new NHPP model based on the Lindley distribution is proposed. The mathematical formulas for its measures of reliability are obtained and graphically illustrated. The proposed model’s parameters are estimated using both the NLSE (nonlinear least squares estimation) and the WNLSE (weighted nonlinear least squares estimation) methods. The model is then validated based on several different reliability datasets. The methods of estimation are evaluated and compared using three different criteria. The performance of the new model is also evaluated and compared, both objectively and subjectively, with three previously suggested models. The application results show that our new model demonstrates good performance in our selected failure data.

WITHDRAWN: Developing a software reliability evaluation model for wireless sensor networks based on analysis of software fault growth

Abstract Due to potential of various application in emergency and security monitoring fields, fault tolerance monitoring in wireless sensor networks (WSNs) software reliability became one of the most fundamental concern in computer field. In WSNs Previous literature had been proposed about how to improve network reliability. However the work done on establishing evaluating models of reliability for WSNs are insufficient yet. In this article, a model for wireless sensor networks (WSNs) is developed for the reliability evaluation. This network state based model can effectively do the evaluation of the probability of successful transmission of data in WSNs with the consideration of the work states of both the sensor node’s different modules. For WSNs we did the evacuation for reliability of in both tasks i.e., single, multi-task modes. At the same time, how different network topologies, affect the reliability of WSNs is also discussed respectively. To evaluate the reliability of WSNs the simulation attempts are shown in results that impacts network state on reliability and the effectiveness in different reliable mechanisms.

Data-driven software reliability evaluation under incomplete knowledge on fault count distribution

In this article, we consider data-driven approaches for software reliability evaluation without specifying the fault count distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. A comprehensive non-parametric method based on the kernel estimation is provided with several kernel functions and bandwidth estimations, in addition to the non-parametric bootstrap. The resulting data-driven methodologies can give useful probabilistic information on the software reliability prediction under the incomplete knowledge on fault count distribution.