Generative AI in Evidence-Based Software Engineering: A White Paper
- URL: http://arxiv.org/abs/2407.17440v3
- Date: Thu, 22 Aug 2024 15:11:24 GMT
- Title: Generative AI in Evidence-Based Software Engineering: A White Paper
- Authors: Matteo Esposito, Andrea Janes, Davide Taibi, Valentina Lenarduzzi,
- Abstract summary: In less than a year practitioners and researchers witnessed a rapid and wide implementation of Generative Artificial Intelligence.
Textual GAIs capabilities enable researchers worldwide to explore new generative scenarios simplifying and hastening all timeconsuming text generation and analysis tasks.
Based on our current investigation we will follow up the vision with the creation and empirical validation of a comprehensive suite of models to effectively support EBSE researchers.
- Score: 10.489725182789885
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Context. In less than a year practitioners and researchers witnessed a rapid and wide implementation of Generative Artificial Intelligence. The daily availability of new models proposed by practitioners and researchers has enabled quick adoption. Textual GAIs capabilities enable researchers worldwide to explore new generative scenarios simplifying and hastening all timeconsuming text generation and analysis tasks. Motivation. The exponentially growing number of publications in our field with the increased accessibility to information due to digital libraries makes conducting systematic literature reviews and mapping studies an effort and timeinsensitive task Stemmed from this challenge we investigated and envisioned the role of GAIs in evidencebased software engineering. Future Directions. Based on our current investigation we will follow up the vision with the creation and empirical validation of a comprehensive suite of models to effectively support EBSE researchers
Related papers
- Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.
Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z) - O1 Replication Journey: A Strategic Progress Report -- Part 1 [52.062216849476776]
This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey.
Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects.
We propose the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process.
arXiv Detail & Related papers (2024-10-08T15:13:01Z) - OpenResearcher: Unleashing AI for Accelerated Scientific Research [35.31092912532057]
We introduce OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to accelerate the research process.
OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge.
We develop various tools for OpenResearcher to understand researchers' queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine these answers.
arXiv Detail & Related papers (2024-08-13T14:59:44Z) - Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives [10.16399860867284]
The emergence of Generative Artificial Intelligence (AI) and Large Language Models (LLMs) has marked a new era of Natural Language Processing (NLP)
This paper explores the current state of these cutting-edge technologies, demonstrating their remarkable advancements and wide-ranging applications.
arXiv Detail & Related papers (2024-07-20T18:48:35Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is an AI-based system for ideation and operationalization of novel work.
ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.
We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z) - Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation.
We present an extensive overview by categorizing these works in terms of various IE subtasks and techniques.
We empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs.
arXiv Detail & Related papers (2023-12-29T14:25:22Z) - Detection of Fake Generated Scientific Abstracts [0.9525711971667679]
The academic community has expressed concerns regarding the difficulty of discriminating between what is real and what is artificially generated.
In this study, we utilize the GPT-3 model to generate scientific paper abstracts through Artificial Intelligence.
We explore various text representation methods when combined with Machine Learning models with the aim of identifying machine-written text.
arXiv Detail & Related papers (2023-04-12T20:20:22Z) - The Semantic Reader Project: Augmenting Scholarly Documents through
AI-Powered Interactive Reading Interfaces [54.2590226904332]
We describe the Semantic Reader Project, a effort across multiple institutions to explore automatic creation of dynamic reading interfaces for research papers.
Ten prototype interfaces have been developed and more than 300 participants and real-world users have shown improved reading experiences.
We structure this paper around challenges scholars and the public face when reading research papers.
arXiv Detail & Related papers (2023-03-25T02:47:09Z) - Automated Mining of Leaderboards for Empirical AI Research [0.0]
This study presents a comprehensive approach for generating Leaderboards for knowledge-graph-based scholarly information organization.
Specifically, we investigate the problem of automated Leaderboard construction using state-of-the-art transformer models, viz. Bert, SciBert, and XLNet.
As a result, a vast share of empirical AI research can be organized in the next-generation digital libraries as knowledge graphs.
arXiv Detail & Related papers (2021-08-31T10:00:52Z) - A Survey of Knowledge Tracing: Models, Variants, and Applications [70.69281873057619]
Knowledge Tracing is one of the fundamental tasks for student behavioral data analysis.
We present three types of fundamental KT models with distinct technical routes.
We discuss potential directions for future research in this rapidly growing field.
arXiv Detail & Related papers (2021-05-06T13:05:55Z) - Generating Knowledge Graphs by Employing Natural Language Processing and
Machine Learning Techniques within the Scholarly Domain [1.9004296236396943]
We present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications.
Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools.
We generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain.
arXiv Detail & Related papers (2020-10-28T08:31:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.