Past meetings (Autumn 2023)
- On 9th October Aboubaker Ibrahim presents Using Large Language Models (LLMs) To Increase Unit Test Coverage in JavaScript his MSc CS dissertation supervised by Franco Raimondi.
Click here for Abstract. Unit testing is an essential procedure during software development, but it can be time-consuming and error-prone. Manual unit testing often requires developers to spend hours writing test cases that only cover a limited range of code paths. This can lead to bugs that are not detected until late in the development process, or even after the software has been deployed. Automated unit testing can help to address these challenges by generating test cases automatically. This can save developers time and effort, and it can also help to ensure that a wider range of code paths are covered. In this talk I will present a tool that can increase unit test coverage for JavaScript (JS) code using large language models (LLMs). LLMs have been shown to be effective in generating natural language text, and they can be used to generate test cases that are both efficient and effective. The tool is evaluated on a set of JS projects, and the results show that it can be very effective in increasing test coverage.Click here for Abobaker’s Bio. Abubakar Ibrahim is a MSc student in Computer Science and the talk presents the results obtained in his dissertation. Before joining Middlesex, he obtained a degree in Computer Engineering from St. Petersburg State Electrotechnical University in St Petersburg, Russia. He was then a full stack developer for two years at DESelect in Antwerp, Belgium.
Past meetings (Winter/Spring 2023)
- On 23rd January Balbir Barn presented his paper Towards the Essence of Specifying Socio Technical Digital Twins published in the Innovations in Software Engineering Conference ISEC 2023.
Click here for Abstract. Digital Twins are now mainstream technology in the engineering domain. Capabilities and underpinning concepts are well understood and augmented by proven theories from the physical sciences. Nonetheless the design of digital twins in engineering still remains essential a craft. As digital twin technology merges with more traditional computational modelling approaches such as that found in simulation, new application domains are emerging and public policy experts see significant potential in DT for understanding their complex system areas. Such domains have a significant sociotechnical component and as such a new type of digital twin is required, together with a means of specifying such a digital twin. This paper proposes a specification language/method for this purpose. Requirements elicitation for this language utilises a tabletop paper template that serves as a boundary object between domain experts and technical experts. The language is conformant with accepted practice in simulation methods and its semantics provides a route to implementation of a digital twin. We argue that the language is a contribution to a breadcrumb trail for future work in this emerging application area for digital twins.
- 6th February: Faten Subhi Abdelrahman Alzalah talks on her paper Stock market prediction based on the sentiment of video news and TSI via machine learning models.
Click here for Abstract. Scientists have long been interested in forecasting stock market fluctuations. Traditional data like financial textual news, stock prices, and comments are simply no longer sufficient because they don’t provide a comprehensive picture. In this study, the efficacy of using financial video news stories versus the use of conventional text news stories to forecast the stock market is examined. We used the Granger causality test to evaluate the robustness of the causal connection between share prices, text news sentiment, video news sentiments, and the Twitter sentiment index. Several models for sentiment analysis of S&P 500 stock were assessed using LR, SVM, LSTM, ATT-LSTM, and CNN models. This study is distinctive because it compares the use of financial video news stories, conventional text news stories, and the Twitter Sentiment Index (TSI) to forecast stock market movements. The experimental results suggest that there is a stronger causal connection between video news sentiment and stock market fluctuation compared to conventional text news sentiments. The result shows that we can more accurately predict market changes using video news than we can with traditional news.
- 20th February: Luca Piras talks on
A Model-Based Privacy-by-Design Platfom for GDPR Compliance
Click here for Abstract. The introduction of the European General Data Protection Regulation (GDPR) has brought significant benefits to citizens, but it has also created challenges for organisations, which are facing with difficulties interpreting it and properly applying it. An important challenge is compliance with the Privacy by Design and by default (PbD) principles, which require that data protection is integrated into processing activities and business practices from the design stage. The European Data Protection Board (EDPB) released an official document with PbD guidelines, and there are various efforts to provide approaches to support these. However, organizations are still facing difficulties in identifying a flow for executing, in a coherent, linear, and effective way, these activities, and a complete Platform / Toolkit for supporting this. In this talk, we present: (i) the identification of the most important PbD activities and strategies, (ii) the design of a coherent, linear and effective flow for them, and (iii) describe our comprehensive supporting platform, the DEFeND EU Project platform, and its most important core part, and toolkit, called DSM. Specifically, within DEFeND, we identified candidate tools, fulfilling specific GDPR aspects, and integrated them in a comprehensive toolkit: the DEFeND Data Scope Management service (DSM). The aim of DSM is to support organizations for continuous GDPR compliance through Model-Based Privacy by Design analysis. In this talk, we present the DEFeND Platform, important PbD activities and strategies individuated, then describe its most important toolkit, DSM, with its design, flow, and related case studies performed within pilots from the healthcare, banking, public administration, and energy sectors. We conclude by presenting current results, ongoing works, future works, and future lines of research.Click here for Luca’s Bio. Luca Piras is a Lecturer of Software Engineering, Privacy and Gamification Engineering at Middlesex University (MDX), London (UK), member of the MDX Software Engineering, Theory and Algorithms (SETA) Research Group, and Module Leader of “Web-Based Mobile App Development” Module. Previously, he was a Lecturer of Privacy, Security, Acceptance and Gamification Engineering at Robert Gordon University (RGU), Aberdeen (UK), member of the RGU Cybersecurity Research Group, and Module Coordinator of the Modules: “Computer Security and Cryptography”, “Managing a Network Group Project” and “Routing and Switching”. He is an official program committee member of different International Conferences and official reviewer of International Journals such as the Elsevier Journal of Systems and Software (JSS), the IEEE Transactions on Software Engineering Journal (TSE), the Springer Empirical Software Engineering Journal (EMSE), and the Springer International Journal on Software and Systems Modeling (SoSyM). His main research interest concerns Privacy and Security Engineering by using Requirements Engineering and Goal Modeling techniques. His research is focused on conceptual modeling, analysis and design of concepts related to privacy and security by design, with particular interest to supporting organisations in achieving GDPR compliance, through semi-automated software tools able to guide and support requirements analysts in visual/modeling ways. He has also another important research interest related to Acceptance Requirements Analysis based on Gamification for making Software Systems more engaging. This research focuses on characterising software users (by analysing also behavioural and human-science related aspects), for supporting requirements analysts to identify the most suitable game elements and mechanisms (able to motivate the intended users) for designing software systems functionalities enriched with gamification solutions (more details in the related scientific papers, PhD Thesis available at http://eprints-phd.biblio.unitn.it/3424/1/Luca_Piras_PhD_Thesis_FINAL_VERSION.pdf and at the website https://pirasluca.wordpress.com/home/acceptance/).
- 6th March: Raja Nagarajan talks on Quantum Computing
- 20th March: Can Baskent talks on Deviant Games for Deviant Logic
- 17th April: Can Baskent reports on the AI UK meeting in London
- On 15th May Florian Kammü@ller presented his paper Explanation of Black Box AI for GDPR related Privacy using Isabelle published in Data Privacy Management DPM2022, co-located with ESORICS’22, 2023.
Click here for Abstract. In this paper, we present a methodology for constructing explanations for AI classification algorithms. The methodology consists of constructing a model of the context of the application in the Isabelle Infrastructure framework (IIIf) and an algorithm that allows to extract a precise logical rule that specifies the behaviour of the black box algorithm thus allowing to explain it. The explanation is given within the rich logical model of the IIIf. It is thus suitable for human audiences. We illustrate this and validate the methodology on the application example of credit card scoring with special relation to the right of explanation as given by the GDPR.
Click here for Abstract.
I will talk about a case study I am doing with Quantinuum (https://www.quantinuum.com) and SMOAD Networks (https://smoad.io)
on “Using Quantum Random Numbers to enhance the security of SD-WAN Networks”.
Click here for Raja’s Bio.
Rajagopal Nagarajan is Professor of Foundations of Computing at Middlesex. He received his PhD from Imperial College London and has held positions at the University of Warwick, ATC-NY, USA, the University of Calgary, Canada and the University of California, Berkeley, USA. Nagarajan (together with Gay and other colleagues) has pioneered the use of model checking, equivalence checking, theorem proving and testing for the verification of quantum systems and has many of the first publications in these areas. He was a Co-Investigator in the EU Sixth Framework Integrated Project SeCoQC which built one of the earliest secure quantum communication networks.
Click here for Abstract.
Game semantics is a semantic method that explains truth using game theoretical primitives, such as strategies and wins. In this talk, I will give a programmatic overview of my work on the game semantics for non-classical logics. I will discuss some well-known (propositional) non-classical logics and offer a game semantics for each. This will allow me to observe how semantic games change based on the underlying logic and how we can use games to develop logical systems. The long term goal of this project is to influence the work on game semantics for programming languages, foundational game theory as well as philosophical non-classical logics.