The Present and Future of Bots in Software Engineering

• URL: http://arxiv.org/abs/2207.01254v1
• Date: Mon, 4 Jul 2022 08:26:56 GMT
• Title: The Present and Future of Bots in Software Engineering
• Authors: Emad Shihab and Stefan Wagner and Marco A. Gerosa and Mairieli Wessel and Jordi Cabot
• Abstract summary: We are witnessing a massive adoption of software engineering bots, applications that react to events triggered by tools and messages posted by users and run automated tasks in response. This thematic issue describes experiences and challenges with these bots.
• Score: 8.885976491708375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We are witnessing a massive adoption of software engineering bots, applications that react to events triggered by tools and messages posted by users and run automated tasks in response, in a variety of domains. This thematic issues describes experiences and challenges with these bots.

Related papers

• The Perceptions of Software Engineers Concerning the Utilization of Bots in the OSS Development Process: An Exploratory Survey [1.663160284499972]
  Bots provide daily support to professionals by enhancing productivity and facilitating task automation. Current bots are not sufficiently intelligent and raised new challenges and enhancements to aid bot designers in developing additional functionalities and integrations.
  arXiv  Detail & Related papers  (2024-11-14T14:16:03Z)
• Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review [14.84837870899906]
  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.
  arXiv  Detail & Related papers  (2024-09-18T10:37:06Z)
• Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
  Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision. We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
  arXiv  Detail & Related papers  (2024-07-02T21:00:30Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Building Your Own Product Copilot: Challenges, Opportunities, and Needs [16.710056957807353]
  We interviewed 26 professional software engineers responsible for building product copilots at various companies. We found pain points at every step of the engineering process and the challenges that strained existing development practices.
  arXiv  Detail & Related papers  (2023-12-21T18:37:43Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Suggestion Bot: Analyzing the Impact of Automated Suggested Changes on Code Reviews [2.773900417167691]
  We created a bot called SUGGESTION BOT to automatically review the code base using GitHub's suggested changes functionality. We evaluate SUGGESTION BOT concerning its impact on review time and also analyze whether the comments given by the bot are clear and useful for users.
  arXiv  Detail & Related papers  (2023-05-10T17:33:43Z)
• AutoML-GPT: Automatic Machine Learning with GPT [74.30699827690596]
  We propose developing task-oriented prompts and automatically utilizing large language models (LLMs) to automate the training pipeline. We present the AutoML-GPT, which employs GPT as the bridge to diverse AI models and dynamically trains models with optimized hyper parameters. This approach achieves remarkable results in computer vision, natural language processing, and other challenging areas.
  arXiv  Detail & Related papers  (2023-05-04T02:09:43Z)
• Challenges in Visual Anomaly Detection for Mobile Robots [65.53820325712455]
  We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods.
  arXiv  Detail & Related papers  (2022-09-22T13:26:46Z)
• Instruction-driven history-aware policies for robotic manipulations [82.25511767738224]
  We propose a unified transformer-based approach that takes into account multiple inputs. In particular, our transformer architecture integrates (i) natural language instructions and (ii) multi-view scene observations. We evaluate our method on the challenging RLBench benchmark and on a real-world robot.
  arXiv  Detail & Related papers  (2022-09-11T16:28:25Z)
• 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.