It requires historical datasets from diverse environments to build accurate and robust software reliability growth models. However, it is very difficult to collect such datasets, particularly in the industrial domain, due to confidentiality and security considerations. To solve this issue, we conducted a thorough investigation of historical datasets for software reliability growth modeling. We searched IEEE Xplore, a prominent digital library, using the keyword “Software Reliability Growth Model” for publications up to 2022 and gathered 127 non-redundant historical datasets. In this paper, we present a comprehensive analysis of these datasets, which helps advance software reliability growth modeling. We applied seven representative software reliability growth models and found that the Generalized Goel model, which has a concave curve, was the most frequently chosen optimal model for Failure-Count type datasets. However, It is notable that S-shaped curve models remained preferred for the majority of datasets. Categorizing datasets by collection phases, application types, source types, and data flow trends resulted in varied optimal model selections across classifications. These insights contribute to the advancement of software reliability growth modeling, fostering informed decision-making in software development and maintenance.

Traditional Software Reliability Growth Models (SRGMs) depend on unrealistic assumptions, which makes it difficult to capture the complexities of modern software development. Recent advancements in artificial intelligence have introduced new modeling techniques, but these methods are complex and require careful algorithm selection and hyperparameter tuning. Automated Machine Learning (AutoML) has emerged as a promising solution to streamline this process. However, its application in the field of software reliability growth modeling remains unexplored. In this study, we explore the effectiveness of AutoML in enhancing software reliability growth modeling and compare its performance with traditional SRGMs. We employ two prominent AutoML packages, Auto-sklearn and H2O AutoML, and leverage twelve project datasets to answer three research questions: (1) the impact of various AutoML package options on software reliability growth modeling, (2) the identification of the optimal AutoML approach for modeling software reliability growth, and (3) the overall effectiveness of AutoML in software reliability growth modeling. We found that using ensemble options enhanced predictive performance across multiple projects. Auto-sklearn with the ensemble option emerged as the most effective approach when evaluated based on End-point Prediction values, while H2O AutoML with the ensemble option demonstrated superior performance based on Mean Squared Error values. Additionally, AutoML packages with ensemble options demonstrated more accurate predictive performance compared to traditional SRGMs across the majority of datasets. Our study highlights the potential of AutoML to enhance software reliability growth modeling and provides insights for future research and practical applications in software engineering.

Edge-Cloud system requires massive infrastructures located in closer to the user to minimize latencies in handling Big data. Ansible is one of the most popular Infrastructure as Code (IaC) tools crucial for deploying these infrastructures of the Edge-cloud system. However, Ansible also consists of code, and its code quality is critical in ensuring the delivery of high-quality services within the Edge-Cloud system. On the other hand, the Large Langue Model (LLM) has performed remarkably on various Software Engineering (SE) tasks in recent years. One such task is Automated Program Repairing (APR), where LLMs assist developers in proposing code fixes for identified bugs. Nevertheless, prior studies in LLM-based APR have predominantly concentrated on widely used programming languages (PL), such as Java and C, and there has yet to be an attempt to apply it to Ansible. Hence, we explore the applicability of LLM-based APR on Ansible. We assess LLMs’ performance (ChatGPT and Bard) on 58 Ansible script revision cases from Open Source Software (OSS). Our findings reveal promising prospects, with LLMs generating helpful responses in 70% of the sampled cases. Nonetheless, further research is necessary to harness this approach’s potential fully.

Standards conformance and integrated development are key features of modern automation systems. This paper introduces an integrated development environment (IDE) that enables high-speed EtherCAT communication and standardized motion programming. On the basis of our previous work, we extended and customized open-source software components to provide a completely open architecture IDE, that is compliant with the relevant industrial standards. The major components of our IDE are IEC 61131-3 and PLCopen TC6 compliant editor, EtherCAT communication support, and PLCopen TC2 compliant motion control library. Since the whole procedures from the project setup to run-time monitoring are highly automated, the presented IDE allows developers to integrate coordinated motion with control logics seamlessly and efficiently.

Due to the rapid evolution of mobile processors and wireless networks, many personal mobile devices are able to support wide spectrum of Internet applications. In such systems, it is highly desirable to provide high-speed wireless communication while restraining the CPU from wasting its resources. Previous study has revealed that the bottlenecks of the communication on wireless devices are the data movements over not only the wireless link but also the bus between the memory and network controller. To overcome this performance limitation, we consider reducing the size of data traversing the bus and wireless link. In this paper, we suggest an efficient kernel-level TCP data compression scheme, which is transparent to the existing applications and can provide high-speed wireless communication. A challenging issue is that the performance gain should amortize the data compression overhead. The experimental results on realistic wireless Internet scenarios show that the modified Linux kernel can achieve better performance up to 60% and 72% than the original TCP over wireless LAN and WiMAX, respectively. Moreover, we show that the suggested scheme can save more CPU resources in spite of data compression overhead.