Artificial Intelligence (AI) is transforming many domains, including software engineering. AI-based tools are gaining popularity and are increasingly being integrated into software development workflows, automating complex tasks such as code writing and reviewing. When it comes to coding tasks, some evidence suggests that tools like Copilot boost developers’ productivity (e.g.,developers can handle a larger number of pull requests per week). However, it remains unclear whether this comes at the expense of code ownership (i.e.,the developer’ ability to argue about their implementation choices). To partially address this gap, we present an experiment aimed at investigating the impact of AI-based assistants on developers’ productivity and behavior in the context of code writing (e.g.,developing a program from scratch or evolving an existing code). Our focus is on the interplay between productivity and code ownership. We asked 69 participants (34 BSc and 13 MSc students, 8 researchers, and 14 professional developers) to perform two code writing tasks, one with the support of AI and one without. Then, we compared the two treatments in terms of: (i) time spent on the coding task and percentage of the task completeness—both being productivity proxies; and (ii) ability of the participants to answer questions about the code they implemented—code ownership proxy. While the time-based analyses did not provide strong evidence on the impact of AI on the investigated dependent variables, participants using AI achieved a much higher task completeness (>2× in terms of median), confirming a positive impact on their productivity. However, such a boost did not come for free. Indeed, we also observed a loss in code ownership when participants used AI, with lower ability to answer technical questions (–12.5%).

In smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key.

We describe compositional architectures and certifications in the research project certMILS. Compositional architectures enable re-use of certified COTS (commercial off-the-shelf) components with a well-defined delegation of responsibilities between component developers and system integrators during cyber physical system design and certification. We show how we used a Common Criteria certified MILS (Multiple Independent Levels of Safety / Security) platform for compositional designs and IEC 62443-4-1/62443-4-2 security evaluations and certifications for composed systems from the domains of smart grid, railway, and subway, that are safety- and security-critical.