Open Source Software Reliability Research Articles

A Method of Reliability Assessment Based on Trend Analysis for Open Source Software

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.

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.

Modelling Open-Source Software Reliability Incorporating Swarm Intelligence-Based Techniques

In the software industry, two software engineering development best practices coexist: open-source and closed-source software. The former has a shared code that anyone can contribute, whereas the latter has a proprietary code that only the owner can access. Software reliability is crucial in the industry when a new product or update is released. Applying meta-heuristic optimization algorithms for closed-source software reliability prediction has produced significant and accurate results. Now, open-source software dominates the landscape of cloud-based systems. Therefore, providing results on open- source software reliability - as a quality indicator - would greatly help solve the open-source software reliability growth- modelling problem. The reliability is predicted by estimating the parameters of the software reliability models. As software reliability models are inherently nonlinear, traditional approaches make estimating the appropriate parameters difficult and ineffective. Consequently, software reliability models necessitate a high- quality parameter estimation technique. These objectives dictate the exploration of potential applications of meta- heuristic swarm intelligence optimization algorithms for optimizing the parameter estimation of nonhomogeneous Poisson process-based open-source software reliability modelling. The optimization algorithms are firefly, social spider, artificial bee colony, grey wolf, particle swarm, moth flame, and whale. The applicability and performance evaluation of the optimization modelling approach is demonstrated through two real open-source software reliability datasets. The results are promising

A Method of OSS Reliability Assessment Based on Public Repository Analysis

Open Source Software (OSS) is developed by various developers. Moreover, OSS user is increasing all over the world. There are various methods to maintain the software. Large-scale OSS projects mainly use bug tracking system (BTS) for bug management. Recently, OSS project uses public software repository to publish software such as GitHub. Public software repository has a function to report software issue. In the past, several researchers have proposed reliability assessment method using BTS. In this research, we propose a reliability assessment method based on deep learning using software repository analysis.

Open-source Software Reliability Modeling with Stochastic Impulsive Differential Equations

In reality, sudden updates of software, attacks of hackers, influence of the Internet market, etc. can cause a surge in the number of open-source software (OSS) faults (this moment is the time when impulse occurs), which results in impulsive phenomenon. For the existing software reliability models, dynamic process of software fault is considered to be continuous when assessing reliability, but continuity of the process can be disrupted with appearance of random impulses. Thus, to more accurately assess software reliability, we proposed an OSS reliability model with SIDE. In the model, dynamic process of software fault is divided into a continuous and a skipped part, described the continuous part of the process with SDE, and described destruction of the continuity caused by unpredictable random events with random impulses. Finally, the proposed model is verified with two datasets from real OSS project, and the results show that the proposed model is more in line with reality and has better fitting effect than the existing models.

Reliability model of open source software considering fault introduction with generalized inflection S-shaped distribution

Recently, the open source software (OSS) reliability has become one of hot issues. Owing to the uncertainty and complexity of OSS development, testing and debugging environments, OSS are completed dynamically. When detected faults are removed for OSS, they are likely to introduce new faults. Moreover, under the different OSS debugging environments, fault introduction will show different changes. For example, the fault introduction rate shows a decrease change, or increasing first and then decreasing change over time. Considering the complex and dynamic changes in fault introduction, an OSS reliability model that fault introduction obeys a generalized inflection S-shaped distribution is proposed in this paper. Experimental results indicate that the fitting and predictive performance of the proposed model is good. The established model in this paper can adapt the dynamical and complicated changes of fault introduction during OSS debugging. Moreover, the established model can accurately forecast the number of remaining faults in OSS, and assist developers to evaluate the actual OSS reliability.

Modelling reliability growth for multi‐version open source software considering varied testing and debugging factors

AbstractDue to the continuous dependence of society on technology and the fast growth of Open Source Software (OSS), there is a need for the software industry to shift to multi‐release software development. To include the revisions in user demands and testing environment, factors that affect OSS reliability have to be considered. Here, we propose a quantitative method for assessing the reliability of multi‐release OSS by using Software Reliability Growth Models based on the Non‐Homogenous Poisson Process. Various factors like imperfect debugging, error generation, change‐point have been considered. The model has been estimated on Statistical Package for Social Sciences using three releases of the Apache dataset. It is concluded that the results obtained are improved than the existing ones.

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.

Software Reliability Prediction of Open Source Software Using Soft Computing Technique

Background: In software development, reliability performs a significant role. It is the nonfunctional requirement of the software. Before using the Open Source Software (OSS) for software development, it is essential to check the quality of the open source software. It is challenging to identify that which OSS is suitable for development of software. Conventional software reliability prediction models are suitable for the Commercial Off The Shelf (COTS) software but it is not sufficient to predict the software reliability of open source software as it has some extra features such as source code is freely available and it is modifiable also. Methods: Most of the researchers have given the mathematical model based on crisp set theory to estimate the reliability of software. Proposed methodology does not rely upon a scientific/mathematical model. In this approach, fuzzy logic based soft computing approach has been used to analyze the reliability of OSS. The goal of this paper is to propose a fuzzy logic soft computing technique based model using three reliability metrics for estimating the security of open source software. Results: The software reliability model is tested on few software applications, and the outcomes affirm the productivity of the model. Conclusion: In order to assess open source software reliability a fuzzy logic based soft computing technique has been proposed.

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.

OSS Effort Expense Optimization Based on Wiener Process Model and GA

Various open source software are managed by using several bug tracking systems. In particular, the open source software extends to the cloud service and edge computing. Recently, OSF Edge Computing Group is launched by OpenStack. There are big data behind the internet services such as cloud and edge computing. Then, it is important to consider the impact of big data in order to assess the reliability of open source software. Various optimal software release problems have been proposed by specific researchers. In the typical optimal software release problems, the cost parameters are defined as the known parameter. However, it is difficult to decide the cost parameter because of the uncertainty. The purpose of our research is to estimate the effort parameters included in our models. In this paper, we propose an estimation method of effort parameter by using the genetic algorithm. Then, we show the estimation method in section 3. Moreover, we analyze actual data to show numerical examples for the estimation method of effort parameter. As the research results, we found that the OSS managers would be able to comprehend the human resources required before the OSS project in advance by using our method.

EM Algorithm for Estimating Reliability of Multi-Release Open Source Software Based on General Masked Data

Multi-release is critical for modern open source software product in order to satisfy more customer requirements. Masked data, a kind of missing data, is the system failure data when the exact cause of the failures might be unknown. That is, the cause of the system failures may be any one of the objects. However, due to the influence of the test strategy in real project, the cause of the system failures may be a subset of the system objects, not any one of the objects. In this paper, the mathematical description of general masked data is presented based on the traditional masked data. Furthermore, a novel multi-release open source software (OSS) reliability model based on general masked data is proposed. Different from traditional multi-release OSS reliability model, the proposed approach is based on additive model with general masked data other than change point model. And then, the maximum likelihood estimation (MLE) process of the model parameters is derived in detail, and expectation maximization (EM) algorithm is used to solve the extremely complicated problem of the log-likelihood function. Finally, two data sets from real open source software project are applied to the proposed approach, and the results show that the proposed reliability model is useful and powerful.

ELS algorithm for estimating open source software reliability with masked data considering both fault detection and correction processes

Masked data are the system failure data when the exact cause of the failures might be unknown. That is, the cause of the system failures may be any one of the components. Additionally, to incorporate more information and provide more accurate analysis, modeling software fault detection and correction processes have attracted widespread research attention recently. However, stochastic fault correction time and masked data brings more difficulties in parameter estimation. In this paper, a framework of open source software growth reliability model with masked data considering both fault detection and correction processes is proposed. Furthermore, a novel Expectation Least Squares (ELS) method, an EM-like (Expectation Maximization) algorithm, is used to solve the problem of parameter estimation, because of its mathematical convenience and computational efficiency. It is note that the ELS procedure is easy to use and useful for practical applications, and it just needs more relaxed hidden assumptions. Finally, three data sets from real open source software project are applied to the proposed framework, and the results show that the proposed reliability model is useful and powerful.

Підходи до побудови моделі загроз для аналізу безпеки відкритого програмного кода

Гарантування безпеки програмного продукту з відкритим вихідним кодом є актуальною проблемою, бо навіть у проектах з закритим вихідним кодом можуть бути присутні open source бібліотеки, що робить можливим появу вразливості у них. Серед методів, що використовують для виявлення вразливостей, варто виділити моделювання загроз, бо цей метод дозволяє вже на ранніх етапах розробки програмного коду прийняти заходи, що знизять витрати на ліквідацію вразливостей та спростять їх усунення і зміни до архітектури додатку. Підбір відповідного підходу при побудові моделі загроз залежить від специфіки проекту, ресурсів, а також кваліфікації адміністраторів. 

OSS Project Stability Assessment Support Tool Considering EVM Based on Wiener Process Models

The method of earned value management is traditionally applied to the progress assessment of actual software projects in many IT companies. Also, Open Source Software (OSS) are used under the various situations because of cost reduction, standardization, and quick delivery. In particular, many OSS are developed and managed by using fault big data recorded on the bug tracking systems. Several research papers in terms of OSS reliability assessment have been published in the past. The fault is caused by the poor handling of effort control. Therefore, we can make a stable and safety operation for OSS system if the OSS project is appropriately managed by using the software effort. The OSS effort estimation model by using a conventional stochastic differential equation model is discussed in this paper. Then, we propose the optimal maintenance problem based on the earned value requirement. In particular, we develop the OSS project stability support tool. Furthermore, several performance illustrations of the developed software tool are shown by using the effort data under actual OSS project.

Open Source Software Reliability Model with the Decreasing Trend of Fault Detection Rate

Abstract Software reliability assessment methods have been changed from closed to open source software (OSS). Although numerous new approaches for improving OSS reliability are formulated, they are not used in practice due to their inaccuracy. A new proposed model considering the decreasing trend of fault detection rate is developed in this study to effectively improve OSS reliability. We analyse the changes of the instantaneous fault detection rate over time by using real-world software fault count data from two actual OSS projects, namely, Apache and GNOME, to validate the proposed model performance. Results show that the proposed model with the decreasing trend of fault detection rate has better fitting and predictive performance than the traditional closed source software and other OSS reliability models. The proposed model for OSS can further accurately fit and predict the failure process and thus can assist in improving the quality of OSS systems in real-world OSS projects.

Design Quality Metrics on the Package Maintainability and Reliability of Open Source Software

Design Quality Metrics on the Package Maintainability and Reliability of Open Source Software

A New Software Reliability Growth Model: Multigeneration Faults and a Power-Law Testing-Effort Function

Software reliability growth models (SRGMs) based on a nonhomogeneous Poisson process (NHPP) are widely used to describe the stochastic failure behavior and assess the reliability of software systems. For these models, the testing-effort effect and the fault interdependency play significant roles. Considering a power-law function of testing effort and the interdependency of multigeneration faults, we propose a modified SRGM to reconsider the reliability of open source software (OSS) systems and then to validate the model’s performance using several real-world data. Our empirical experiments show that the model well fits the failure data and presents a high-level prediction capability. We also formally examine the optimal policy of software release, considering both the testing cost and the reliability requirement. By conducting sensitivity analysis, we find that if the testing-effort effect or the fault interdependency was ignored, the best time to release software would be seriously delayed and more resources would be misplaced in testing the software.

Rate-Based Queueing Simulation Model of Open Source Software Debugging Activities

Open source software (OSS) approach has become increasingly prevalent for software development. As the widespread utilization of OSS, the reliability of OSS products becomes an important issue. By simulating the testing and debugging processes of software life cycle, the rate-based queueing simulation model has shown its feasibility for closed source software (CSS) reliability assessment. However, the debugging activities of OSS projects are different in many ways from those of CSS projects and thus the simulation approach needs to be calibrated for OSS projects. In this paper, we first characterize the debugging activities of OSS projects. Based on this, we propose a new rate-based queueing simulation framework for OSS reliability assessment including the model and the procedures. Then a decision model is developed to determine the optimal version-updating time with respect to two objectives: minimizing the time for version update, and maximizing OSS reliability. To illustrate the proposed framework, three real datasets from Apache and GNOME projects are used. The empirical results indicate that our framework is able to effectively approximate the real scenarios. Moreover, the influences of the core contributor staffing levels are analyzed and the optimal version-updating times are obtained.

Reliability analysis and optimal version-updating for open source software

Context Although reliability is a major concern of most open source projects, research on this problem has attracted attention only recently. In addition, the optimal version-dating for open source software considering its special properties is not yet discussed. Objective In this paper, the reliability analysis and optimal version-updating for open source software are studied. Method A modified non-homogeneous Poisson process model is developed for open source software reliability modeling and analysis. Based on this model, optimal version-updating for open source software is investigated as well. In the decision process, the rapid release strategy and the level of reliability are the two most important factors. However, they are essentially contradicting with each other. In order to consider these two conflicting factors simultaneously, a new decision model based on multi-attribute utility theory is proposed. Results Our models are tested on the real world data sets from two famous open source projects: Apache and GNOME. It is found that traditional software reliability models provide overestimations of the reliability of open source software. In addition, the proposed decision model can help management to make a rational decision on the optimal version-updating for open source software. Conclusion Empirical results reveal that the proposed model for open source software reliability can describe the failure process more accurately. Furthermore, it can be seen that the proposed decision model can assist management to appropriately determine the optimal version-update time for open source software.