Prompted Software Engineering in the Era of AI Models
- URL: http://arxiv.org/abs/2311.03359v1
- Date: Thu, 7 Sep 2023 20:40:04 GMT
- Title: Prompted Software Engineering in the Era of AI Models
- Authors: Dae-Kyoo Kim
- Abstract summary: This paper introduces prompted software engineering (PSE), which integrates prompt engineering to build effective prompts for language-based AI models.
PSE enables the use of AI models in software development to produce high-quality software with fewer resources, automating tedious tasks and allowing developers to focus on more innovative aspects.
- Score: 1.450405446885067
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This paper introduces prompted software engineering (PSE), which integrates
prompt engineering to build effective prompts for language-based AI models, to
enhance the software development process. PSE enables the use of AI models in
software development to produce high-quality software with fewer resources,
automating tedious tasks and allowing developers to focus on more innovative
aspects. However, effective prompts are necessary to guide software development
in generating accurate, relevant, and useful responses, while mitigating risks
of misleading outputs. This paper describes how productive prompts should be
built throughout the software development cycle.
Related papers
- Next-Gen Software Engineering: AI-Assisted Big Models [0.0]
This paper aims to facilitate a synthesis between models and AI in software engineering.
The paper provides an overview of the current status of AI-assisted software engineering.
A vision of AI-assisted Big Models in SE is put forth, with the aim of capitalising on the advantages inherent to both approaches.
arXiv Detail & Related papers (2024-09-26T16:49:57Z) - Future of Artificial Intelligence in Agile Software Development [0.0]
AI can assist software development managers, software testers, and other team members by leveraging LLMs, GenAI models, and AI agents.
AI has the potential to increase efficiency and reduce the risks encountered by the project management team.
arXiv Detail & Related papers (2024-08-01T16:49:50Z) - 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) - Natural Language-Oriented Programming (NLOP): Towards Democratizing Software Creation [4.5318695190841884]
Natural Language-Oriented Programming (NLOP) is a vision introduced in this paper.
It allows developers to articulate software requirements and logic in their natural language, thereby democratizing software creation.
This paper reviews various programming models, assesses their contributions and limitations, and highlights that natural language will be the new programming language.
arXiv Detail & Related papers (2024-06-08T09:13:54Z) - Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE.
We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - 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) - 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) - 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) - Empowered and Embedded: Ethics and Agile Processes [60.63670249088117]
We argue that ethical considerations need to be embedded into the (agile) software development process.
We put emphasis on the possibility to implement ethical deliberations in already existing and well established agile software development processes.
arXiv Detail & Related papers (2021-07-15T11:14:03Z) - 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) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
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.