Muhammad Waseem

Early Results of an AI Multiagent System for Requirements Elicitation and Analysis

Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation

Five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics are presented, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.

LLM-based Multi-Agent System for Intelligent Refactoring of Haskell Code

Results highlight the ability of LLM-based multi-agent in managing refactoring tasks targeted toward functional programming paradigms and hint that LLM-based multi-agent systems integration into the refactoring of functional programming languages can enhance maintainability and support automated development workflows.

From Meetings to Product Vision: An Agentic Approach with MARARE

From Business Meetings to Requirement Artifacts: An Agentic AI Approach with MARARE

Preliminary results indicate performance differences across LLMs, suggesting that model choice influences coverage, consistency, and hallucination rates.

Bridging Humans and LLMs: Investigating Human-AI Collaboration in Multi-agent Requirements Analysis for Organizational AI Adoption

LLM-based multi-agent systems can support strategic AI planning by enabling iterative refinement with human experts by supporting structured and collaborative Requirements Engineering processes for AI adoption planning.

REFINE: A Multi-Agent LLM Approach for Evidence-Guided Code Refactoring

This work introduces REFINE (Refactoring with Evidence-aware Flow for Integrated ageNtic Execution), a tool-agnostic, evidence-aware multi-agent approach for generating Java file-level refactoring candidates that achieves a higher median code-smell reduction with smaller edits and fewer public-method removals.

Trustworthy RAG: An Evaluation Agent for Detecting Misinformation and Knowledge Poisoning in Generative AI Systems

An Evaluation Agent, middleware that combines Natural Language Inference factual verification, a five-signal poison detector with relevance-weighted aggregation, and a Trust Index is proposed, which reliably blocks instruction injection of unsafe advice while contradiction and subtle semantic weakening remain hard.

CodeAssay: A Multi-Metric Benchmark with Audited Ground Truth for LLM Code Generation

These findings show that reliable evaluation of LLM-generated code requires validated ground truth, protected tests, and multiple explicitly interpreted measures, and that CodeAssay provides a reproducible basis for evidence-based model evaluation in AI-augmented software development.