4 citations · 15 across the 20 of their papers we have counts for
14 papers · 1 filter
When Passing Tests Hides Vulnerabilities: An Empirical Study of Silent Failures in Agentic Systems
Wenji Bai, Muhammad Waseem, Zeeshan Rasheed +2
LLM-based agents for automated code repair have received significant attention in recent years from both research and software engineering practice perspectives. However, limited a…
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…
Identifying and Prioritizing Generative AI Use Cases in an Organization: An Industrial Case Study
Malik Abdul Sami, Zeeshan Rasheed, Meri Olenius +4
Organisations are examining how generative AI can support their operational work and decision-making processes. This study investigates how employees in a energy company understand…
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…
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…