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.

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.

In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half‐value period, and proposes a time‐weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K‐means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K‐means clustering algorithm is then proposed, by combining item‐based and user‐based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model.

Wireless sensor networks (WSNs) have been used widely across various industries and business fields that require the coverage of large geographical regions that are difficult for humans to reach. It is therefore important to be able to model, assess, and predict the reliability of WSNs. Masked data is a type of missing data used to represent system failure when the exact cause of the failure is unknown. This paper proposed a novel additive reliability model for a cluster‐based WSN system using general masked data and uses the expectation maximization (EM) algorithm to solve the problem of the maximum likelihood estimation (MLE). Moreover, the proposed model assumes that a WSN comprises several clusters, and the failure processes of these clusters are independent. The probability characteristics of the system are determined according to the topology of the WSN system to evaluate the system reliability. Finally, the proposed model is demonstrated to be powerful for estimating WSN system reliability using a simulated dataset.

The growing complexity of contemporary software systems, with multiple sub-modules, demands innovative modeling approaches, especially in addressing intricate failures and masked data generation. This paper proposed a stochastic differential equation (SDE)-based software reliability additive model tailored for multi-component systems dealing with masked data. However, in the presence of masked data, the challenge of parameter estimation arises due to the inherent complexity of the objective function and numerous parameters. To overcome this, we proposed the Expectation Least Square (ELS) algorithm, providing a strategic solution for the intricate task of parameter estimation with masked data. In order to verify the effectiveness of our proposed model, we conducted a comparative analysis with traditional reliability models using three actual failure data sets. The experimental results emphasize the superior performance of the proposed model and highlight the effectiveness of the ELS algorithm in accurately estimating software reliability.