ResearchBot: Bridging the Gap between Academic Research and Practical Programming Communities

• URL: http://arxiv.org/abs/2407.02643v1
• Date: Tue, 2 Jul 2024 20:17:13 GMT
• Title: ResearchBot: Bridging the Gap between Academic Research and Practical Programming Communities
• Authors: Sahar Farzanehpour, Swetha Rajeev, Huayu Liang, Ritvik Prabhu, Chris Brown,
• Abstract summary: This project introduces ResearchBot, a tool designed to bridge the academia-industry gap. ResearchBot employs a modular approach, encompassing understanding questions, curating queries to obtain relevant papers and summarizing paper content. The core objective of ResearchBot is to democratize access to academic knowledge for industry professionals.
• Score: 1.7825757481227438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software developers commonly rely on platforms like Stack Overflow for problem-solving and learning. However, academic research is an untapped resource that could greatly benefit industry practitioners. The challenge lies in connecting the innovative insights from academia to real-world problems faced by developers. This project introduces ResearchBot, a tool designed to bridge this academia-industry gap. ResearchBot employs a modular approach, encompassing understanding questions, curating queries to obtain relevant papers in the CrossRef repository, summarizing paper content and finally answering user questions based on paper summaries. The core objective of ResearchBot is to democratize access to academic knowledge for industry professionals. By providing concise summaries of cutting-edge research directly in response to SE-related questions, ResearchBot facilitates the application of academic insights to practical contexts. Ultimately, it aims to bridge the gap between academia and industry, using research evidence to support learning and decision-making in software development.

Related papers

• What Can Natural Language Processing Do for Peer Review? [173.8912784451817]
  In modern science, peer review is widely used, yet it is hard, time-consuming, and prone to error. Since the artifacts involved in peer review are largely text-based, Natural Language Processing has great potential to improve reviewing. We detail each step of the process from manuscript submission to camera-ready revision, and discuss the associated challenges and opportunities for NLP assistance.
  arXiv  Detail & Related papers  (2024-05-10T16:06:43Z)
• General Place Recognition Survey: Towards Real-World Autonomy [26.794603315981675]
  We highlight the crucial role of PR within the framework of Simultaneous Localization and Mapping (SLAM) 2.0. This new phase in robotic navigation calls for scalable, adaptable, and efficient PR solutions by integrating advanced artificial intelligence (AI) technologies. We provide a comprehensive review of the current state-of-the-art (SOTA) in PR, alongside the remaining challenges, and underscore its broad applications in robotics.
  arXiv  Detail & Related papers  (2024-05-08T04:54:48Z)
• Fish-bone diagram of research issue: Gain a bird's-eye view on a specific research topic [11.556954590485319]
  This study aims to help novice researchers by providing a fish-bone diagram that includes causal relationships. It offers a broad, highly generalized perspective of the research field, based on relevance and logical factors.
  arXiv  Detail & Related papers  (2024-04-30T05:43:41Z)
• A Tale of Two Communities: Exploring Academic References on Stack Overflow [1.2914230269240388]
  We find that Stack Overflow communities with different domains of interest engage with academic literature at varying frequencies and speeds. The contradicting patterns suggest that some disciplines may have diverged in their interests and development trajectories from the corresponding practitioner community.
  arXiv  Detail & Related papers  (2024-03-14T20:33:55Z)
• Machine Unlearning: A Survey [56.79152190680552]
  A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
  arXiv  Detail & Related papers  (2023-06-06T10:18:36Z)
• The Quantum Frontier of Software Engineering: A Systematic Mapping Study [16.93115872272979]
  Quantum software engineering (QSE) is emerging as a new discipline to enable developers to design and develop quantum programs. This paper presents a systematic mapping study of the current state of QSE research.
  arXiv  Detail & Related papers  (2023-05-31T09:26:10Z)
• 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)
• Reasoning with Language Model Prompting: A Survey [86.96133788869092]
  Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting.
  arXiv  Detail & Related papers  (2022-12-19T16:32:42Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• OpenHoldem: An Open Toolkit for Large-Scale Imperfect-Information Game Research [82.09426894653237]
  OpenHoldem is an integrated toolkit for large-scale imperfect-information game research using NLTH. OpenHoldem makes three main contributions to this research direction: 1) a standardized evaluation protocol for thoroughly evaluating different NLTH AIs, 2) three publicly available strong baselines for NLTH AI, and 3) an online testing platform with easy-to-use APIs for public NLTH AI evaluation.
  arXiv  Detail & Related papers  (2020-12-11T07:24:08Z)
• Talk to Papers: Bringing Neural Question Answering to Academic Search [8.883733362171034]
  Talk to Papers exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. It's designed to enable researchers to use natural language queries to find precise answers and extract insights from a massive amount of academic papers.
  arXiv  Detail & Related papers  (2020-04-04T19:19:55Z)

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.