9 citations · 52 across the 29 of their papers we have counts for
32 papers
Where Did the Repair First Go Wrong? Localizing the Origins of Silent Failures in Agentic Vulnerability Repair
Wenji Bai, Muhammad Waseem, Zeeshan Rasheed +2
Localizing where an LLM-based agent first fails to uphold security during a repair can show which stage of its workflow needs an additional safeguard. This is difficult for silent…
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…
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…
REFINE: A Multi-Agent LLM Approach for Evidence-Guided Code Refactoring
Muhammad Waseem, Aakash Ahmad, Pekka Abrahamsson
Large Language Models (LLMs) offer new opportunities for automated code refactoring. However, generated changes must reduce targeted quality problems without introducing new issues…
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…
CodeAssay: A Multi-Metric Benchmark with Audited Ground Truth for LLM Code Generation
Shahbaz Siddeeq, Muhammad Waseem, Umar Subhan Malhi +1
Large Language Models are increasingly evaluated for code generation using test-based benchmarks. The validity of such evaluations depends on the reliability of their references an…
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…
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…
Context Before Code: An Experience Report on Vibe Coding in Practice
Md Nasir Uddin Shuvo, Md Aidul Islam, Md Mahade Hasan +2
Code-generating tools are increasingly used in software development, yet experience reports on conversational "vibe coding" under production constraints remain limited. This paper…
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…
Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development
Mateen A. Abbasi, Tommi J. Mikkonen, Petri J. Ihantola +3
The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the carbon footprint of development activi…
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…
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…
AI based Multiagent Approach for Requirements Elicitation and Analysis
Malik Abdul Sami, Muhammad Waseem, Zheying Zhang +3
Requirements Engineering (RE) plays a pivotal role in software development, encompassing tasks such as requirements elicitation, analysis, specification, and change management. Des…
A Tool for Test Case Scenarios Generation Using Large Language Models
Abdul Malik Sami, Zeeshan Rasheed, Muhammad Waseem +3
Large Language Models (LLMs) are widely used in Software Engineering (SE) for various tasks, including generating code, designing and documenting software, adding code comments, re…