Towards Code Generation from BDD Test Case Specifications: A Vision
- URL: http://arxiv.org/abs/2305.11619v1
- Date: Fri, 19 May 2023 11:54:52 GMT
- Title: Towards Code Generation from BDD Test Case Specifications: A Vision
- Authors: Leon Chemnitz, David Reichenbach, Hani Aldebes, Mariam Naveed, Krishna
Narasimhan, Mira Mezini
- Abstract summary: This paper introduces a novel approach to generating specifications as input for the popular Angular framework.
Our approach aims to drastically reduce the development time needed for web applications while potentially increasing software quality.
- Score: 0.9137351242229175
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Automatic code generation has recently attracted large attention and is
becoming more significant to the software development process. Solutions based
on Machine Learning and Artificial Intelligence are being used to increase
human and software efficiency in potent and innovative ways. In this paper, we
aim to leverage these developments and introduce a novel approach to generating
frontend component code for the popular Angular framework. We propose to do
this using behavior-driven development test specifications as input to a
transformer-based machine learning model. Our approach aims to drastically
reduce the development time needed for web applications while potentially
increasing software quality and introducing new research ideas toward automatic
code generation.
Related papers
- Optimizing AI-Assisted Code Generation [0.8901073744693314]
AI-assisted code-generation tools have significantly transformed software development.
The security, reliability, functionality, and quality of the generated code must be guaranteed.
This paper examines the implementation of these goals to date and explores strategies to optimize them.
arXiv Detail & Related papers (2024-12-14T20:14:44Z) - Bot-Driven Development: From Simple Automation to Autonomous Software Development Bots [10.364014177847201]
Bot-driven development (BotDD) represents a transformative shift where bots assume proactive roles in coding, testing, and project management.
This paper explores how bot-driven development impacts traditional development roles, particularly in redefining driver-navigator dynamics.
We propose a research agenda addressing challenges in bot-driven development, including skill development for developers, human-bot trust dynamics, optimal interruption frequency, and ethical considerations.
arXiv Detail & Related papers (2024-11-25T05:21:23Z) - Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplace [2.5280615594444567]
Generative AI coding tools are relatively new, and their impact on developers extends beyond traditional coding metrics.
This study aims to illuminate developers' preexisting beliefs about generative AI tools, their self perceptions, and how regular use of these tools may alter these beliefs.
Our findings reveal that the introduction and sustained use of generative AI coding tools significantly increases developers' perceptions of these tools as both useful and enjoyable.
arXiv Detail & Related papers (2024-10-24T00:07:27Z) - Agent-Driven Automatic Software Improvement [55.2480439325792]
This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs)
The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation.
We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
arXiv Detail & Related papers (2024-06-24T15:45:22Z) - The Future of Scientific Publishing: Automated Article Generation [0.0]
This study introduces a novel software tool leveraging large language model (LLM) prompts, designed to automate the generation of academic articles from Python code.
Python served as a foundational proof of concept; however, the underlying methodology and framework exhibit adaptability across various GitHub repo's.
The development was achieved without reliance on advanced language model agents, ensuring high fidelity in the automated generation of coherent and comprehensive academic content.
arXiv Detail & Related papers (2024-04-11T16:47:02Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - Exploring the intersection of Generative AI and Software Development [0.0]
The synergy between generative AI and Software Engineering emerges as a transformative frontier.
This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development.
It serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering.
arXiv Detail & Related papers (2023-12-21T19:23:23Z) - Active Predicting Coding: Brain-Inspired Reinforcement Learning for
Sparse Reward Robotic Control Problems [79.07468367923619]
We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC)
We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards.
We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
arXiv Detail & Related papers (2022-09-19T16:49:32Z) - CodeRL: Mastering Code Generation through Pretrained Models and Deep
Reinforcement Learning [92.36705236706678]
"CodeRL" is a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning.
During inference, we introduce a new generation procedure with a critical sampling strategy.
For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives.
arXiv Detail & Related papers (2022-07-05T02:42:15Z) - 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) - AI-based Modeling and Data-driven Evaluation for Smart Manufacturing
Processes [56.65379135797867]
We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes.
We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
arXiv Detail & Related papers (2020-08-29T14:57:53Z)
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.