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390 papers

#machine learning Review Open access Apr 2023

StartCards - A method for early-stage software startups

The first published version of StartCards is presented, which is considered useful for early-stage startups and can also be used as a pedagogical tool in startup education.

Kai-Kristian Kemell, Anh Nguyen-Duc, Mari Suoranta et al. · 25 citations · ⚡1
#artificial intelligence Conference Open access Sep 2023

Preface of RESET 2023: 2nd International Workshop on Requirement Engineering for Software Startups and Emerging Technologies

The Second International Workshop on Requirement Engineering for Software startups and Emerging Technologies (RESET) brought together requirements engineering researchers and practitioners to discuss the need for adapting conventional requirement engineering artifacts in developing and operating emerging technologies.

Anh Nguyen-Duc, Chetan Arora, P. Abrahamsson · 1 citation
#computer vision Review Open access 2023

Impact in Software Engineering Activities After One Year of COVID-19 Restrictions for Startups and Established Companies

Investigating the impacts of COVID-19 on software development activities after one year of the pandemic restrictions found that most respondents did not observe a significant impact, and software startups and established companies were affected differently.

Hosna Hooshyar, E. Guerra, Jorge Melegati et al. · 4 citations
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3

Experimenting with Multi-Agent Software Development: Towards a Unified Platform

A unified platform that utilizes multiple artificial intelligence agents to automate the process of transforming user requirements into well-organized deliverables, including user stories, prioritization, and UML sequence diagrams, along with the modular approach to APIs, unit tests, and end-to-end tests.

Malik Abdul Sami, Muhammad Waseem, Z. Rasheed et al. · 14 citations · ⚡1
#computer vision Review Jun 2024

A Tool for Test Case Scenarios Generation Using Large Language Models

A web-based software tool is introduced that employs an LLM-based agent and prompt engineering to automate the generation of test case scenarios against user requirements and crafting test case scenarios based on these stories.

Malik Abdul Sami, Z. Rasheed, Muhammad Waseem et al. · 13 citations
#computer vision Apr 2024

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.

Malik Abdul Sami, Z. Rasheed, Muhammad Waseem et al. · 15 citations
#computer vision Review Mar 2024

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.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Aug 2024

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.

Malik Abdul Sami, Muhammad Waseem, Zheying Zhang et al. · 17 citations · ⚡2
#computer vision Apr 2024

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.

Z. Rasheed, Muhammad Waseem, Kari Systä et al. · 23 citations

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