9 citations · 52 across the 29 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…