Emanuel S. Grant

Software engineering is a fundamental sub-discipline of computer science, yet the teaching of the subject is not standardized enough to facilitate the sharing of teaching resources across institutions. This is highlighted by the diversity of topics which may be taught in software engineering courses. This issue is more evident in the realm of safety critical systems. If this problem can be alleviated by the establishment of a framework for developing software engineering teaching material (lectures, examinations, assignments, and course projects), pedagogy assessment then there would be an environment for the sharing and structuring of common software engineering artifacts for curricula design and development.

Artificial intelligence (AI) is applied at a pace that challenges the verification of its suitability to the domains of application. This situation arises from the proliferation of AI development being conducted from a data science point of view rather than a software engineering approach. The situation leads to the question of whether software development course curricula are addressing the necessary educational needs for graduates to respond to the challenges of applying AI development in emerging domains. The challenge has two parts: the first is the use of AI in developing software systems, and the second is the development of AI systems. By looking at the first part of this challenge, we propose a pedagogy for introducing AI tools in software engineering education and structuring a methodology for AI application development to establish software engineering principles. This article is exploratory. We reviewed the existing literature to identify the commonalities of approaches to select a required set of topics, course outcomes, and structure for a curriculum on AI in software development.

Currently, AI is applied at a pace that challenges the verification of its suitability to the domains of application. A team of computer science researchers will combine their knowledge and skills to formulate a pedagogical strategy for a graduate-level program in software engineering (SE) that is developed from a foundational set of learning theories from which a set of principles is derived. These principles are used to define a set of learning techniques that are then amalgamated into methods for a comprehensive teaching and learning methodology. The graduate program will address the use of artificial intelligence (AI) in SE education, as the trending approach in 21st century application development. Academic and international industrial software development standards are incorporated into the course and program curricula to diminish the gap between classroom education and professional practices. Fundamental to this approach is the use of established learning theories for SE and AI in designing the educational modules. The outcome of this proposed research will be a graduate program in software engineering.

The verification and validation of software system class models are crucial aspects of software engineering, aiming to ensure correctness, reliability, and quality in software systems. This research explores advancements in verification and validation techniques, focusing on UML class diagram models. The study proposes a methodology to enhance the reliability and security of software models by simplifying UML class diagrams, eliminating semantic overload, and validating models using the Object Constraint Language (OCL). The practical and theoretical values of this research are discussed, highlighting benefits for developers, system administrators, end-users, cybersecurity professionals, organizations, and academic communities. The work addresses challenges and limitations in current verification and validation techniques, emphasizing the complexity of modern software systems. The proposed methodology is implemented using the USE tool, demonstrating its effectiveness in validating UML-based specifications and OCL constraints.

Worldwide sports activities have one of the largest supporter/fan bases of all the many areas of entertainment. The sport of soccer is one of the richest sports in the world and commands the position of being the most popular sport in the world. That single data point forms the basis for an interesting set of data analytical research questions. First on that list would be: How is soccer determined to be the most popular sport in the world? How do the factors of advertising/marketing, televising/streaming, teams’ win/loss, players, location, etc. contribute to the popularity of the sport? What impact does the popularity of soccer have on the players’ performance? Can data analytics predict teams’ performance? The report presented herein is a documentation and review of prior research efforts that have applied soft computing methods to predict teams’ win/loss probability in the sport of soccer. This review comments on the success of the data analytics applied and makes an assessment of how this can affect the popularity of the sport.

Phishing, a type of social engineering attack, continues to be a problem, and anti-phishing approaches must be implemented to stymie these attacks. With the rise of new educational methods that go beyond lecture-based, the question is whether there is a modern approach to education. One method that has existed for years is that of simulated phishing training, but it is thought that there needs to be a more engaging approach to this area of education. Gamification in phishing education is a more recent but not extremely well studied approach, which leads to the question – Which is better for phishing education of users, simulated phishing exercises or gamified phishing training? In this report, five research articles in each category are described and summarized, and then an analysis is conducted within the groups and between the two groups. The analysis is focused on quantitative metrics that appear in the selected research articles; there is a lesser focus on the qualitative analysis conducted in the reviewed reports. From this comparative analysis, gamified phishing training appears to be a more suitable method of educating users, as it reduces susceptibility and engages students at a higher level of learning.

Electrical microgrids are deemed to be the future of modern power systems. Microgrids are sophisticated, decentralized, and self-sufficient small-scale power systems consisting of various resources ranging from wind turbines, solar photovoltaics, electric vehicles, smart energy storage, and complex communication infrastructure. However, renewable energy sources such as solar photovoltaics and wind-based generators are highly intermittent, and if not appropriately planned, they can compromise the stability of the grid. Formal methods can define and analyze the functionality and behavior of any system and show if the system design is correct before the actual system is implemented. Although formal methods have been around for many years, it is surprising that little to none are utilized in the design of safety-critical electrical power systems. Currently, in modeling microgrids, few to no attempts of formalization are being used to improve the design reliability and reduce system operating costs and time. This work demonstrates how complex systems such as microgrids can be modeled elegantly using a formal specification method. In this work, the Z state-based formal specification language (Z-Method) is used to model and verify microgrid designs. In this work, 3-interconnected microgrid systems with a high penetration level of solar and wind-based renewable energy sources with plugin hybrid electric vehicles (PHEV) as battery energy storage systems (BESS) are modeled using the Z-method. To the best of authors, the knowledge presented formal method is one of the first reported attempts in modeling microgrid communities using Software Engineering formalism.

Current concerns with the operation failure and mal-performance of safety critical software systems have heightened a review of methodologies and strategies applied in the development of these systems. Prior reviews were focused on the technical and business policies and procedures applied in development such systems but it is now recognized that there needs to be a broader review of the causes associated with the undesired performance of safety critical software systems. The existence of stringent specifications for the development of safety critical systems in multiple application domains have not been sufficient for ensuring the highest level of confidence in these systems. This report examines the impact ethical consideration may have on increasing public confidence in the application of safety critical software systems in the daily activities of the general population. The work is centered on software development in the avionic industry but the findings are applicable to the more general area of safety critical software operations.