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Prioritizing Software Requirements Using Large Language Models

A web-based software tool utilizing AI agents and prompt engineering to automate task prioritization and apply diverse prioritization techniques, aimed at enhancing project management within the agile framework is introduced.

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

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.

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.

Assessing Small Language Models for Code Generation: An Empirical Study with Benchmarks

This study presents a comprehensive empirical evaluation of 20 open-source Small Language Models and reveals that several compact SLMs achieve competitive results while maintaining a balance between performance and efficiency, making them viable for deployment in resource-constrained environments.

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.

Vibe Coding in Practice: Flow, Technical Debt, and Guidelines for Sustainable Use

This article analyzes the flow-debt tradeoffs associated with VC and identifies and explains how current model, platform, and hardware limitations contribute to these issues, and proposes countermeasures to address them, informing research and practice towards more sustainable VC approaches.

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