Currently, when digital infrastructure is gaining importance in social life, ensuring the security and reliability of information systems is becoming a priority. The paper presenting the literature review was prepared based on the methodology of Systematic Literature Review (SLR). It analyzes the application of machine learning in the field of cybersecurity, with an emphasis on new methods of detecting and mitigating threats in cyberspace using deep learning techniques and graph neural networks. It discusses how these innovations can affect the development of information technologies, offering advanced techniques for supporting the IT security.
This paper presents the development and evolution of an educational module centered on collaborative problem-based learning, model-based software development, and end-to-end timing analysis for vehicular embedded systems, highlighting our experiences in integrating state-of-the-art research and industry practices over the years. For the past eight years, the module has been taught to both industry professionals and academic students. In the industry, it has been presented through seminars and workshops organized within the industrial settings of two vehicle manufacturers and a provider of vehicular software development tools. In an academic context, this module has been delivered as part of a PhD course. Furthermore, it has been incorporated into 11 instances of master's courses across four European universities. When offered in an industry context, the module is kept concise and more focused on hands-on activities and practical use cases. In contrast, when the module is delivered in an academic setting, it is supplemented with additional lectures and discussions on its topics. Interestingly, the feedback received from participants, especially those from the industry, has not only contributed to refining this educational module but has also advanced the state of the art in modeling and timing analysis of embedded software architectures.
With the easy accessibility of generative AI tools, software engineering students are increasingly using these tools in their coursework. However, the impact of these tools on the students' development of core computational thinking skills remains unclear. In this paper, we present our ongoing research that investigates whether access to ChatGPT during the learning process influences students' understanding and retention of essential computational skills. We specifically look at their ability to decompose complex problems (decomposition) and abstract key elements into classes and objects (abstraction). In a controlled, between-subject experiment, we divide students into two groups: one with ChatGPT assistance during the learning phase and a control group relying solely on traditional resources. Participants first undergo a study session to learn and apply decomposition and abstraction skills through various tasks, followed by a retention test session within a week from their study session. We measure the time taken to complete the tasks as well as the correctness of the solutions submitted by participants for each task. Participants also complete a post-study survey to provide insights into their experiences and to gauge their confidence in their solutions and acquired skills. We also analyze screen recordings and ChatGPT interaction logs to better understand the participants' usage strategies and interaction patterns. Our study aims to contribute to the ongoing discussion regarding the pedagogical role of generative AI in Software Engineering education and to offer guidance on effectively integrating these tools into teaching and learning practices.
With the high amount of data in applications like healthcare, education, and finance, there is a requirement for sophisticated analytical methods that can tune in real-time and generate precise forecasts. In this paper, a proposed methodology is introduced that is a novel model combining both artificial intelligence (AI) and machine learning (ML) for predictive analytics enhancement in a dynamic setting. Technologies presented are AI-assisted pre-processing of data that detects faults and extracts relevant features, and machine learning algorithms that dynamically tune the model parameters. The resilience mechanism is the most critical component in the working of the proposed methodology that utilizes adaptive learning rates and anomaly detection to enable the model to detect noise and anomalies. Furthermore, a feedback loop is included in the model to enable continuous optimization based on performance metrics to increase accuracy. The following work discusses the design of the proposed methodology, includes an algorithm, discusses its applicability in different industries, and the benefits of the proposed model compared to existing predictive analytics models, specifically in the ability to rapidly update data and detect anomalies.
Plagiarism activities such as copying code, algorithms, or documentation without consent and lack of attribution are rising in industry and academia. While this can be attributed to the rise of generative AI, a lack of awareness about plagiarism and its implications among soon-to-be software engineers and practitioners raises serious concerns about academic integrity and adds another dimension to this challenge. This research proposes exploring "replicating a study" as a pedagogical tool to impart ethical considerations to software engineering undergraduate students. Replicating a study involves recreating and validating existing research findings utilizing datasets from the original study, contributing to a deeper understanding of engineering concepts. Thus, while working on a replication study, students can be prompted to explore and understand professional ethics such as obtaining informed consent, permission to reuse data, and giving credit to original authors. Using preliminary results from such an experiment with two undergraduate student groups, in this ongoing research, we explore and solicit input to modify the methodology for a more extensive study.
