8 citations · 13 across the 17 of their papers we have counts for
18 papers
Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems
Balkrishna Giri, Md Toufique Hasan, Jussi Rasku +2
Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) outputs in external knowledge, but RAG systems usually trust whatever they retrieve, creating a Security-Rel…
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 a software development practice in which developers state intent in natural language and large language models generate code. It is often framed as one-shot promptin…
Epic-Organized vs. Requirement-Aligned Gherkin: An Empirical Evaluation of LLM-Based Acceptance Criteria Generation
Shahbaz Siddeeq, Mateen Abbasi, Jussi Rasku +4
Automated authoring of Gherkin Behavior-Driven Development (BDD) acceptance criteria remains a manual bottleneck in requirements engineering. This study investigates whether epic-o…
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…
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…
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…
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…
CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology
Zeeshan Rasheed, Muhammad Waseem, Kai-Kristian Kemell +5
Existing Large Language Model (LLM)-based multi-agent systems are capable of executing tasks and providing data-driven recommendations, thereby enabling automation and decision sup…