We compare the methods of OSS performability evaluation by the deep multimodal learning and multitask one. This paper focuses on the Web-based OSS such as cloud computing, edge computing, and Web server. Also, several practical execution examples based on the proposed deep models by actual big fault data. Furthermore, this paper discusses several deep multimodal and multitask learning for the OSS performability analyses.

Unresolved security weaknesses raised serious concerns since they have the potential to allow serious breaches and threaten software system. A vulnerability is a loophole or flaw in a security system that an attacker could take advantage of and cause harm or loss. Since significant portions of code are carried over into subsequent releases, the vulnerabilities may remain and resurface in later versions. These vulnerabilities are often introduced during development as a result of ongoing upgrades and the addition of new features. It is crucial to identify not only when vulnerabilities are introduced but how they are discovered over time because they remain across software generations. A systematic framework for investigating vulnerability discovery trends and forecasting the accumulation of defects has been made available by Vulnerability Discovery Modeling (VDM). However, a large amount of the work that has been done is limited to single-release software, ignoring the impact of shared code and inherited vulnerabilities. In this work, we prefer a multi-version VDM derived from generalized Weibull–logistic distribution. The proposed work intends to examine the impact of vulnerabilities in previous releases and their recurrence in upgraded systems via code inheritance. The model assesses the potential for unfixed vulnerabilities from previous iterations to reappear during new code testing and impact the current release. Real datasets from three distinct Windows Server releases are used to test and validate the model. The prediction performance shows that the newly proposed generalized Weibull-Logistic VDM performs better than existing multi-release VDM. The proposed model offers a more practical framework for evaluating persistent security threats in extensive software ecosystems by incorporating vulnerability inheritance into the discovery process.

The demand of open source software is increasing because of the low cost, high quality, and short delivery. In particular, open source software is managed by using the bug tracking system. This paper focuses on the method of reliability assessment based on the deep learning. Then, the Wiener process is applied to the output value of objective variables. Moreover, several sensitivity analyses of the parameter of Wiener process are shown as several numerical examples.

At present, the fault big data of open source software are opened as the open data set. In particular, the fault detection phenomenon depends on various situation of operation in OSS. Actually, various software reliability growth models have been actively proposed by several researchers in the past. This paper applies the deep learning approach to the OSS fault big data. Then, we propose several reliability assessment measures based on the deep learning. As an approach, the range of estimate expands by the Wiener process embedded for the data preprocessing. Furthermore, this paper proposes the performability as novel reliability assessment measure from the proposed deep learning model. In particular, we develop the prototype of 3D reliability assessment tool. Several illustration examples based on the developed prototype of 3D reliability assessment tool by using the actual fault big data sets are shown in this paper.

From the perspective of software quality management, the software reliability growth model (SRGM) is frequently used by many project managers. Especially, the nonhomogeneous Poisson process model is used for the commercial software, because it can express the fault convergence status of software. On the other hand, the software reliability growth curve of open source software (OSS) tends to show the distorted curve due to multiple factors such as the number of the users and the software update. It is difficult to evaluate the software reliability for OSS. Thus, it is needed to develop SRGM considering the factors of OSS. In the past, the number of the downloads was used to the OSS’s SRGM considering number of users. However, several software distributors only record the sum of the number of the downloads for all version. To perform evaluation with high accuracy, it needs to use the latest version only for calculating execution time because the older version contributes very little to debugging the latest version OSS. In this paper, we analyze the migration of downloads of the latest version from urllib3 which can analyze the number of downloads by detail version. Moreover, we propose the SRGM considering the package manager behavior.

Non-homogeneous Poisson process (NHPP) based modeling has gained lot of importance in the field of Software Reliability Engineering. Many related models have been proposed in the literature and several are in the pipeline. Yamada's two stage fault removal modeling framework has been extensively worked in the past; wherein last decade has also seen the impact and importance of modeling related to imperfect debugging phenomenon. But, a major limitation of Yamada's two stage modeling framework has been its limitation to capture the time dependent nature of faults in the software. In the present proposition, an attempt has been made to reach to this requirement by making use of the approximation method as a solution methodology. The employment of this alternative solution methodology yields solutions to all the three possible situations; that is, pure error generation, exponential form and linear form of error generation. In order to facilitate the analysis, proposed cases have been verified on real life data sets and results obtained are quiet encouraging.

Previously, many researchers proposed various software reliability growth model (SRGM) to evaluate software reliability. Also, they applied SRGM to open source software (OSS). On the other hand, almost all OSS continuing to develop. In this case, OSS’s latent faults fluctuates frequently. Moreover, it is difficult to evaluate accurately because this doesn’t consider by SRGM assumptions. It is known that as the number of software users increases, the number of the detected faults tends to increase. Especially, it is seen in like the number of the users changes fluctuates and the software update. In this paper, we analyze the impact of fluctuations in the number of software users on OSS using actual data from a fault tracking system. Moreover, we review the assumptions of non-homogeneous Poisson process (NHPP) based SRGM and propose the corrected time for model considering the number of number of users. Furthermore, we applied propose model to existing general NHPP-based SRGM’s mean value function. As a result, we confirmed that the number of users increases effect to the number of the detected faults. Furthermore, the propose model can evaluate software reliability accurately. Especially, logarithmic Poisson SRGM shows the highest estimation result.

We focus on the noise analysis by using stochastic differential equation model as training data of deep learning. Then, we use the solution process of stochastic differential equation for the software fault analysis to the training data. Also, several numerical examples are shown in this paper. Moreover, this paper shows several sensitivity analyses for the actual big fault data.

In recent years, open source software (OSS) has become ubiquitous in every field of our daily life. Hence, the OSS’s reliability is a significant challenge. The traditional models, like the software reliability growth model, can not handle a large scale of data efficiently, while the deep learning provides an effective method. This paper proposes a multi-input multi-output deep neural network to predict the fault detection time intervals and the fault modification time intervals simultaneously. Additionally, we present several numerical examples with a cross validation conducted with 3 types of data splits.

PurposeThe relationship between the various existing smell taxonomies and the smell impacting factors has been established. The ideology is to identify the most critical smell influencing factors in the vicinity of various software development environments.Design/methodology/approachTo fulfill the said task, the utilization of the amalgamation of two multicriteria decision-making techniques, namely, Entropy method and CODAS method, is presented.FindingsThrough this article, the most critical smell impacting criteria with respect to the smell taxonomies is identified. Furthermore, the behaviour of 4 software development principles was then analysed, and their working state has been successfully assessed.Originality/valueThe ideology to study design-related smells in the software system has been studied by a lot of researchers. Some of them have worked upon their detection and the corresponding refactoration process with the help of several algorithms like machine learning and artificial intelligence. But how and to what extent these design-related smells impact the software development environment has remained out of the limelight till now. Through this article, this research gap has been identified, and an attempt to fill it has been made.

The fault big data sets of many open source software (OSS) are recorded on the bug tracking systems. In the past, we have proposed the effort assessment method under the assumption that the fault detection phenomenon depends on the maintenance effort, because the number of software fault is influenced by the effort expenditure. The past research in terms of the effort assessment method of OSS is based on the effort data sets. On the other hand, we propose the deep learning approach to the OSS fault big data. In the past, the existing method without Wiener process cannot estimate within the range of existing data only. The proposed method assumes that the fault detection process follows the Wiener process such as the imperfect debugging and Markov property. Thereby, the proposed method can estimate the exceeding values by adding the white noise based on the Wiener process. Then, the proposed method make it possible for the OSS managers to assess the values exceeding from the existing data. Then, we show several reliability assessment measures based on the fault modification time based on the deep learning. Moreover, several numerical illustrations based on the proposed deep learning model are shown in this paper.

This paper focuses on the reliability of open source software (OSS). In particular, the proposed method is based on the deep learning by using the fault big data obtained from the bug tracking system. The immune system of human body takes on the role of the protection for the pathogenic microorganisms. Recently, the source code and complexity of OSS system become large year by year. Then, we assume that the OSS fault removal system is similar to the pathogenic microorganisms. We propose the OSS fault removal system based on deep learning inspired by immune system in this paper.

Recently, open source software (OSS) has been widely used in many fields due to the spread and development facilitated by networks. The characteristics of OSS include no cost and high performance, which make it a significant component of modern society. However, the number of reported faults is increasing due to its vulnerabilities. Detecting these faults requires substantial costs, and correcting the growing number of faults necessitates a large workforce. In this paper, we propose a method for Reliability Assessment of OSS using deep learning based on the human immune system. Additionally, we present several numerical examples based on the proposed method.

We have proposed the maintenance effort assessment model based on two Wiener processes for the operation of open source software (OSS) used in the edge computing in the past. In particular, we consider that this proposed model can assess the reliability by using three dimensional graph. Then, we have proposed two-dimensional modeling based on the effort management in the past. In this paper, we propose new expanded maintenance model considering OSS edge computing by expanding the existing two Wiener processes model in order to consider the network environment under the edge OSS operation. Especially, it is important to control the amount of maintenance effort expense in the long-term phase. Then, we propose the optimization method based on the past two-dimensional Wiener processes model. Thereby, it will be helpful 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 optimization method considering the network environment under the edge OSS operation.