Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review

• URL: http://arxiv.org/abs/2409.11864v1
• Date: Wed, 18 Sep 2024 10:37:06 GMT
• Title: Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review
• Authors: Stefano Lambiase, Gemma Catolino, Fabio Palomba, Filomena Ferrucci,
• Abstract summary: We aim to provide a taxonomy for characterizing bots, as well as a series of challenges for their adoption for Software Engineering. To reach our objectives, we conducted a multivocal literature review, reviewing both research and practitioner's literature.
• Score: 14.84837870899906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bots are software systems designed to support users by automating a specific process, task, or activity. When such systems implement a conversational component to interact with the users, they are also known as conversational agents. Bots, particularly in their conversation-oriented version and AI-powered, have seen their adoption increase over time for software development and engineering purposes. Despite their exciting potential, ulteriorly enhanced by the advent of Generative AI and Large Language Models, bots still need to be improved to develop and integrate into the development cycle since practitioners report that bots add additional challenges that may worsen rather than improve. In this work, we aim to provide a taxonomy for characterizing bots, as well as a series of challenges for their adoption for Software Engineering associated with potential mitigation strategies. To reach our objectives, we conducted a multivocal literature review, reviewing both research and practitioner's literature. Through such an approach, we hope to contribute to both researchers and practitioners by providing first, a series of future research routes to follow, second, a list of strategies to adopt for improving the use of bots for software engineering purposes, and third, enforce a technology and knowledge transfer from the research field to the practitioners one, that is one of the primary goal of multivocal literature reviews.

Related papers

• Investigating Developers' Preferences for Learning and Issue Resolution Resources in the ChatGPT Era [1.3124513975412255]
  The landscape of software developer learning resources has continuously evolved, with recent trends favoring engaging formats like video tutorials. The emergence of Large Language Models (LLMs) like ChatGPT presents a new learning paradigm. We conducted a survey targeting software developers and computer science students, gathering 341 responses, of which 268 were completed and analyzed.
  arXiv  Detail & Related papers  (2024-10-10T22:57:29Z)
• Charting a Path to Efficient Onboarding: The Role of Software Visualization [49.1574468325115]
  The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools. This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
  arXiv  Detail & Related papers  (2024-01-17T21:30:45Z)
• DevBots can co-design APIs [0.0]
  DevBots are automated tools that perform various tasks in order to support software development. We analyzed 24 articles to investigate the state of the art of using DevBots in software development.
  arXiv  Detail & Related papers  (2023-12-10T02:29:05Z)
• Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements [55.2480439325792]
  This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques. We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models. A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement.
  arXiv  Detail & Related papers  (2023-11-22T02:45:01Z)
• ChatDev: Communicative Agents for Software Development [84.90400377131962]
  ChatDev is a chat-powered software development framework in which specialized agents are guided in what to communicate. These agents actively contribute to the design, coding, and testing phases through unified language-based communication.
  arXiv  Detail & Related papers  (2023-07-16T02:11:34Z)
• Comparing Software Developers with ChatGPT: An Empirical Investigation [0.0]
  This paper conducts an empirical investigation, contrasting the performance of software engineers and AI systems, like ChatGPT, across different evaluation metrics. The paper posits that a comprehensive comparison of software engineers and AI-based solutions, considering various evaluation criteria, is pivotal in fostering human-machine collaboration.
  arXiv  Detail & Related papers  (2023-05-19T17:25:54Z)
• ChatGPT for Robotics: Design Principles and Model Abilities [25.032064314822243]
  We outline a strategy that combines design principles for prompt engineering and the creation of a high-level function library. We focus our evaluations on the effectiveness of different prompt engineering techniques and dialog strategies towards the execution of various types of robotics tasks. Our study encompasses a range of tasks within the robotics domain, from basic logical, geometrical, and mathematical reasoning all the way to complex domains such as aerial navigation, manipulation, and embodied agents.
  arXiv  Detail & Related papers  (2023-02-20T06:39:06Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• A Literature Survey of Recent Advances in Chatbots [0.0]
  We review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation.
  arXiv  Detail & Related papers  (2022-01-17T23:08:58Z)

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.