Z. Rasheed

AI based Multiagent Approach for Requirements Elicitation and Analysis

Results corroborate the effectiveness of LLMs in improving and streamlining RE phases by analyzing the semantic similarity and API performance of different models, as well as their effectiveness and efficiency in requirements analysis.

Large Language Model Evaluation Via Multi AI Agents: Preliminary results

A novel multi-agent AI model is introduced that aims to assess and compare the performance of various LLMs, and initial results indicate that the GPT-3.5 Turbo model's performance is comparatively better than the other models.

CodePori: Large Scale Model for Autonomous Software Development by Using Multi-Agents

It is shown in the paper that CodePori is able to generate running code for large-scale projects, completing the entire software development process in minutes rather than hours, and at a cost of a few dollars.

From Specification to Service: Accelerating API-First Development Using Multi-Agent Systems

The results indicate that if the OpenAPI specification is kept small and focused, LLM-based multi-agent systems are capable of generating complete functional code with business logic that aligns to the specification.

Large Language Models for Code Generation: The Practitioners Perspective

This work proposes and develops a multi-model unified platform to generate and execute code based on natural language prompts and presents practitioners feedback and insights into the use of LLMs in software development, including their strengths and weaknesses, key aspects overlooked by benchmarks and metrics.

LLM-Generated Microservice Implementations from RESTful API Definitions

A system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices and assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages is presented.

VAPU: System for Autonomous Legacy Code Modernization

An LLM-based multi-agent system is indicated that an LLM-based multi-agent system is a capable solution to update components of a legacy application autonomously.

Distributed Approach to Haskell Based Applications Refactoring with LLMs Based Multi-Agent Systems

A large language models based multi-agent system enables precise task execution and inter-agent collaboration, addressing the challenges of refactoring in functional programming.

Autonomous Legacy Web Application Upgrades Using a Multi-Agent System

An LLM-based multi-agent system that autonomously upgrades legacy web applications to the latest versions and maintains context across tasks and agents, improving solution quality over the base model in some cases is proposed.

A Multi-agent LLM System for Automated Requirements Analysis: A Study on User Story Generation and Prioritization

Generative AI Adoption in an Energy Company: Exploring Challenges and Use Cases

This study investigates how employees in a energy company understand AI adoption and identifies areas where AI and LLMs-based agentic workflows could assist daily activities, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection.

Identifying and Prioritizing Generative AI Use Cases in an Organization: An Industrial Case Study

This study investigates how employees in a energy company understand AI adoption and identifies areas where AI and LLMs-based agentic workflows could assist daily activities, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection.