activity
20242026
most citedDeveloping Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report

8 citations · 15 across the 12 of their papers we have counts for

collaborators

13 papers

cs.CL2026

An Evaluation of AI-Supported Evidence-Based Learning for Public Speaking Skill Development

Sashini Hettiarachchi, Shahbaz Siddeeq, Mika Saari +1

Public speaking is an essential skill in academic and professional contexts, but it often causes anxiety. Although several AI-based speech coaching tools exist, they typically prov…

cs.SE2026

AI Sandbox: Technical Report

Muhammad Waseem, Md Aidul Islam, Md Nasir Uddin Shuvo +8

Collaborative AI experimentation across industry and academia requires platforms that enable rapid prototyping while preserving controlled access, tenant separation, and transparen…

cs.SE2026

TDD Governance for Multi-Agent Code Generation via Prompt Engineering

Tarlan Hasanli, Shahbaz Siddeeq, Bishwash Khanal +3

Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained workflows…

cs.AI2026

Agentic Frameworks for Reasoning Tasks: An Empirical Study

Zeeshan Rasheed, Abdul Malik Sami, Muhammad Waseem +3

Recent advances in agentic frameworks have enabled AI agents to perform complex reasoning and decision-making. However, evidence comparing their reasoning performance, efficiency,…

cs.SE2026

LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review

Zeeshan Rasheeda, Muhammad Waseema, Kai-Kristian Kemella +2

Large Language Models (LLMs) have enabled multi-agent systems to perform autonomous code generation for complex tasks. Despite the recent growth in research and industrial applicat…

cs.SE2026

Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned

Muhammad Waseem, Md Aidul Islam, Md Nasir Uddin Shuvo +8

Collaborative AI experimentation in industry-academia requires environments that support rapid trials while maintaining controlled access, organisational isolation, and traceable w…

cs.CL2026★ 2 cited

Towards AI Evaluation in Domain-Specific RAG Systems: The AgriHubi Case Study

Md. Toufique Hasan, Ayman Asad Khan, Mika Saari +2

Large language models show promise for knowledge-intensive domains, yet their use in agriculture is constrained by weak grounding, English-centric training data, and limited real-w…

cs.SE2025★ 2 cited

AI and Agile Software Development: A Research Roadmap from the XP2025 Workshop

Zheying Zhang, Tomas Herda, Victoria Pichler +20

This paper synthesizes the key findings from a full-day XP2025 workshop on "AI and Agile: From Frustration to Success", held in Brugg-Windisch, Switzerland. The workshop brought to…

cs.CL2025

Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice

José Siqueira de Cerqueira, Kai-Kristian Kemell, Rebekah Rousi +3

The rapid proliferation of Large Language Models (LLMs) has raised significant trustworthiness and ethical concerns. Despite the widespread adoption of LLMs across domains, there i…

cs.SE2024★ 3 cited

The EU AI Act is a good start but falls short

Chalisa Veesommai Sillberg, Jose Siqueira De Cerqueira, Pekka Sillberg +2

The EU AI Act was created to ensure ethical and safe Artificial Intelligence (AI) development and deployment across the EU. This study aims to identify key challenges and strategie…

cs.RO2024

GPT versus Humans: Uncovering Ethical Concerns in Conversational Generative AI-empowered Multi-Robot Systems

Rebekah Rousi, Niko Makitalo, Hooman Samani +5

The emergence of generative artificial intelligence (GAI) and large language models (LLMs) such ChatGPT has enabled the realization of long-harbored desires in software and robotic…

cs.SE2024

TimeLess: A Vision for the Next Generation of Software Development

Zeeshan Rasheed, Malik Abdul Sami, Jussi Rasku +16

Present-day software development faces three major challenges: complexity, time consumption, and high costs. Developing large software systems often requires battalions of teams an…

cs.SE2024★ 8 cited

Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report

Ayman Asad Khan, Md Toufique Hasan, Kai Kristian Kemell +2

This paper presents an experience report on the development of Retrieval Augmented Generation (RAG) systems using PDF documents as the primary data source. The RAG architecture com…