24 citations · 236 across the 51 of their papers we have counts for
52 papers
Checkpoints Are Not Enough: Trust Calibration in CoSLR, a Human-AI System for Systematic Literature Reviews
MD Aidul Islam, Malik Abdul Sami, Muhammad Waseem +4
Systematic Literature Reviews (SLRs) are essential for evidence-based research but remain time-consuming, requiring researchers to manage large volumes of publications across plann…
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…
Vibe Coding in Software Development: A Multivocal Literature Review
Shahbaz Siddeeq, Muhammad Waseem, Kai-Kristian Kemell +3
Vibe coding is an AI-assisted software development approach in which developers express intent in natural language and large language models generate code. To our knowledge, no rev…
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,…
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…
The Landscape of Generative AI in Information Systems: A Synthesis of Secondary Reviews and Research Agendas
Aleksander Jarzębowicz, Adam Przybyłek, Jacinto Estima +14
The post-ChatGPT surge has rapidly reframed IS research and practice. As organizations and society grapple with GenAI adoption, a body of secondary studies and research agendas has…
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…
Adopt-an-AI-Researcher: Bootstrapping Industry-Academia Collaboration to Identify and Prioritise Generative AI Use Cases
Malik Abdul Sami, Zeeshan Rasheed, Meri Olenius +4
Organisations want to use generative AI (GenAI), but many do not know where to begin. Researchers understand AI capabilities, while employees understand their organisation's proces…
Vibe Coding in Practice: Flow, Technical Debt, and Guidelines for Sustainable Use
Muhammad Waseem, Aakash Ahmad, Kai-Kristian Kemell +4
Vibe Coding (VC) is a form of software development assisted by generative AI, in which developers describe the intended functionality or logic via natural language prompts, and the…
From Specification to Service: Accelerating API-First Development Using Multi-Agent Systems
Saurabh Chauhan, Zeeshan Rasheed, Malik Abdul Sami +6
This paper presents a system that uses Large Language Models (LLMs)-based agents to automate the API-first development of RESTful microservices. This system helps to create an Open…
VAPU: System for Autonomous Legacy Code Modernization
Valtteri Ala-Salmi, Zeeshan Rasheed, Abdul Malik Sami +5
In this study, we present a solution for the modernization of legacy applications, an area of code generation where LLM-based multi-agent systems are proving essential for complex…
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…
Assessing Small Language Models for Code Generation: An Empirical Study with Benchmarks
Md Mahade Hasan, Muhammad Waseem, Kai-Kristian Kemell +3
The recent advancements of Small Language Models (SLMs) have opened new possibilities for efficient code generation. SLMs offer lightweight and cost-effective alternatives to Large…
LLM-based Multi-Agent System for Intelligent Refactoring of Haskell Code
Shahbaz Siddeeq, Muhammad Waseem, Zeeshan Rasheed +7
Refactoring is a constant activity in software development and maintenance. Scale and maintain software systems are based on code refactoring. However, this process is still labor…
Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
Md Toufique Hasan, Muhammad Waseem, Kai-Kristian Kemell +3
Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accu…
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…
LLM-Generated Microservice Implementations from RESTful API Definitions
Saurabh Chauhan, Zeeshan Rasheed, Abdul Malik Sami +4
The growing need for scalable, maintainable, and fast-deploying systems has made microservice architecture widely popular in software development. This paper presents a system that…
Distributed Approach to Haskell Based Applications Refactoring with LLMs Based Multi-Agent Systems
Shahbaz Siddeeq, Zeeshan Rasheed, Malik Abdul Sami +6
We present a large language models (LLMs) based multi-agent system to automate the refactoring of Haskell codebases. The multi-agent system consists of specialized agents performin…
Autonomous Legacy Web Application Upgrades Using a Multi-Agent System
Valtteri Ala-Salmi, Zeeshan Rasheed, Abdul Malik Sami +6
The use of Large Language Models (LLMs) for autonomous code generation is gaining attention in emerging technologies. As LLM capabilities expand, they offer new possibilities such…
Large Language Models for Code Generation: The Practitioners Perspective
Zeeshan Rasheed, Muhammad Waseem, Kai Kristian Kemell +5
Large Language Models (LLMs) have emerged as coding assistants, capable of generating source code from natural language prompts. With the increasing adoption of LLMs in software de…
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…
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…
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…
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…