Background of the Group

The Software Engineering and Business Intelligence (SEngBI) group is a multidisciplinary research community at the Faculty of Computing, Sabaragamuwa University of Sri Lanka

The mission of SEngBI is to bridge the gap between research and real-world applications in the fields of Software Engineering and Business Intelligence, with a strong focus on AI-driven innovation, data-driven decision making, and sustainable software ecosystems. The group addresses current and future challenges in software-intensive systems while applying modern business intelligence methods to domains such as finance, health, education, digital government, and social impact.

Members of the SEngBI

• Prof. S. Vasanthapriyan – Professor in Computer Science, Faculty of Computing

• Dr. S. Thuseethan – Lecturer, Charles Darwin University, Australia

• Mr. P. Vigneshwaran – Postgraduate Researcher, Charles Darwin University, Australia

Collaborators

• Prof. Roshan G. Ragel – Faculty of Engineering, University of Peradeniya

• Dr. Supumali Ahangama – Faculty of Information Technology, University of Moratuwa

Objectives

• Develop modern methods, frameworks, and AI-augmented tools for time- and cost-efficient evolution of high-quality software systems.
• Play an internationally leading role in AI-assisted software engineering (AIOps, Copilot systems, LLM-based code generation) and business intelligence research, with a strong focus on experimentation and validation.
• Organize conferences, workshops, and benchmarking challenges to advance global research in software engineering and business intelligence.
• Foster strong academic and industry collaborations to bridge research with practical applications, particularly in Sri Lanka’s digital transformation journey and the emerging global knowledge economy.

Area of focus

Software Engineering

Research in this direction targets:

• AI-assisted software engineering (Copilot-driven programming, intelligent debugging).
• Agile and hybrid project management at scale.
• Software architecture modeling for cloud-native, edge, and quantum systems.
• Software quality assurance using machine learning and automated verification.
• Sustainable software engineering and green computing practices.
• Empirical methods (surveys, experiments, case studies) with strong industry collaboration.

Business Intelligence

Research in this direction lies at the intersection of AI, big data, and decision science, including:

• Financial risk modeling with generative AI and explainable machine learning (XAI).
• Social media and multimodal analytics for misinformation detection, sentiment analysis, and trend forecasting.
• Data-driven marketing and consumer behavior modeling using deep learning and network science.
• Open-source and digital innovation ecosystems.
• Decision support systems powered by real-time analytics, digital twins, and IoT- enabled intelligence.

Research Projects (2025)

Software Engineering

• AI-assisted agile project management for Sri Lankan software companies (ongoing).
• Comparative study of Scrum, Kanban, and hybrid agile models enhanced by analytics (ongoing).
• Knowledge sharing and collaboration in globally distributed agile teams with AI support (ongoing).
• Empirical study of software testing automation and AI-based quality assurance in Sri Lankan firms (ongoing).
• Sustainable software practices: measuring and reducing the carbon footprint of software systems (new).

Business Intelligence

• Enhancing organizational performance through business intelligence, analytics, and generative AI: a dynamic capabilities perspective (ongoing).
• Social media analytics for early detection of violence, extremism, and misinformation in Sri Lanka (ongoing).
• Network-based financial fraud detection using AI-driven anomaly detection (new).
• Developing real-time decision support systems for SMEs through business intelligence dashboards (new).
• Exploring the role of AI ethics and explainability in decision-making frameworks (new).