Efi Papatheocharous

The increasing sophistication and frequency of cyber attacks, particularly in sectors utilizing Cyber-Physical Systems (CPS) and Operational Technology (OT), necessitate rapid and effective detection and response mechanisms. Traditional static defenses are inadequate against advanced adversaries capable of dynamically altering their tactics, making cyber-resilience and self-adaptation critical system properties. This paper explores the concepts of cyber-resilience, self-adaptation, self-healing, and evolvability as critical attributes for systems designed to withstand and recover from cyber threats. We conducted a comprehensive review of 1815 articles, identifying 66 relevant techniques applicable to CPS and OT. These techniques were systematically categorized based on 17 characteristics essential for describing their properties and implementation requirements. Building on these findings, we further introduce a decision support framework, READY, which groups these characteristics into five classes: applicability, technical characteristics, effects, compliance, and evaluation criteria. READY provides developers with the necessary insights to make informed decisions when selecting self-adaptation techniques tailored to their specific challenges. The READY framework can be utilized during the architectural design of new or existing products, serving as a checklist for architects, engineers, product owners, and developers across various domains, including automotive, Industry 4.0, and other real-time systems.

Context: The state-of-the-art and practice on software quality is growing constantly and presents several challenges. The ever-growing body of knowledge on the topic, obfuscates the situation further, the lack of explicit structure makes it difficult to identify which properties exist today and how they can be evaluated, two critical aspects to increase software quality. Objective: A step to disambiguate software properties descriptions is made via a Property Model Ontology (PMO) and steps towards the evaluation of the structure are carried out together with experts. The objectives of this paper are: 1) present in detail the PMO, 2) describe the research process used to develop and evaluate the PMO, and, 3) exemplify the usage of the PMO through real instantiations obtained from practitioners and researchers. Method: Expert interviews and qualitative research methods are used to evaluate the PMO and develop a proof of concept. Results: The PMO consists of concepts describing extra-functional properties (EFPs) and their evaluation methods, i.e., how to measure the properties. The PMO is instantiated in a modelling environment through a metamodel and an online web-content management system. Conclusions: Consensus on the definition and structure of EFPs is achieved and a common understanding on how they can be reused in practice.

Distracted driving is known to be one of the leading causes of vehicle accidents. With the increase in the number of sensors available within vehicles, there exists an abundance of data for monitoring driver behaviour, which, however, has so far only been comparable across vehicle manufacturers to a limited extent due to proprietary solutions. A special role in distraction is played by smart devices, usually used while driving, such as smartphones and smartwatches. They are repeatedly a source of distraction for drivers through calls, messages, notifications and apps usage. However, such devices can also be used for driver behaviour monitoring (like driver distraction detection), as current developments show. As vehicle manufacturer-independent devices, which are usually equipped with adequate sensor technology, they can provide significant advantages and opportunities. This work illustrates the opportunities in using smartphones and wearables to detect driver distraction. The overall architecture description of the concept, called Smart Devices Distracted Driving Detection, is presented together with a series of initial experiments of a proof-of-concept. Artificial Intelligence and more especially Machine Learning is used to assess driving distractions using smart devices in a comprehensive manner.

Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents. With the increasing number of sensors available in vehicles, there is an abundance of data available to monitor driver behaviour, but it has only been available to vehicle manufacturers and, to a limited extent, through proprietary solutions. Recently, research and practice have shifted the paradigm to the use of smartphones for driver monitoring and have fuelled efforts to support driving safety. This systematic review paper extends a preliminary, previously carried out author-centric literature review on smartphone-based driver monitoring approaches using snowballing search methods to illustrate the opportunities in using smartphones for driver distraction detection. Specifically, the paper reviews smartphone-based approaches to distracted driving behaviour detection, the smartphone sensors and detection methods applied, and the results obtained.

Quality requirements deal with how well a product should perform the intended functionality, such as start-up time and learnability. Researchers argue they are important and at the same time studies indicate there are deficiencies in practice. Our goal is to review the state of evidence for quality requirements. We want to understand the empirical research on quality requirements topics as well as evaluations of quality requirements solutions. We used a hybrid method for our systematic literature review. We defined a start set based on two literature reviews combined with a keyword-based search from selected publication venues. We snowballed based on the start set. We screened 530 papers and included 84 papers in our review. Case study method is the most common (43), followed by surveys (15) and tests (13). We found no replication studies. The two most commonly studied themes are (1) differentiating characteristics of quality requirements compared to other types of requirements, (2) the importance and prevalence of quality requirements. Quality models, QUPER, and the NFR method are evaluated in several studies, with positive indications. Goal modeling is the only modeling approach evaluated. However, all studies are small scale and long-term costs and impact are not studied. We conclude that more research is needed as empirical research on quality requirements is not increasing at the same rate as software engineering research in general. We see a gap between research and practice. The solutions proposed are usually evaluated in an academic context and surveys on quality requirements in industry indicate unsystematic handling of quality requirements.

Software cost estimation is a key process in project management. Estimations in the initial project phases are made with a lot of uncertainty that influences estimation accuracy which typically increases as the project progresses in time. Project data collected during the various project phases can be used in a progressive time‐dependent fashion to train software cost estimation models. Our motivation is to reduce uncertainty and increase confidence based on the understanding of patterns of effort distributions in development phases of real‐world projects. In this work, we study effort distributions and suggest a four‐stage progressive software cost estimation model, adjusting the initial effort estimates during the development life‐cycle based on newly available data. Initial estimates are reviewed on the basis of the experience gained as development progresses and as new information becomes available. The proposed model provides an early, a post‐planning, a post‐specifications, and a post‐design estimate, while it uses industrial data from the ISBSG (R10) dataset. The results reveal emerging patterns of effort distributions and indicate that the model provides effective estimations and exhibits high explanatory value. Contributions in lessons learned and practical implications are also provided.