DAnTE: a taxonomy for the automation degree of software engineering tasks

• URL: http://arxiv.org/abs/2309.14903v1
• Date: Tue, 26 Sep 2023 13:04:58 GMT
• Title: DAnTE: a taxonomy for the automation degree of software engineering tasks
• Authors: Jorge Melegati and Eduardo Guerra
• Abstract summary: We propose DAnTE, a Degree of Automation taxonomy for software engineering. We evaluate several tools used in the past and in the present for software engineering practices. We discuss what novel tools could emerge in the middle and long term.
• Score: 2.356908851188234
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Software engineering researchers and practitioners have pursued manners to reduce the amount of time and effort required to develop code and increase productivity since the emergence of the discipline. Generative language models are just another step in this journey, but it will probably not be the last one. In this chapter, we propose DAnTE, a Degree of Automation Taxonomy for software Engineering, describing several levels of automation based on the idiosyncrasies of the field. Based on the taxonomy, we evaluated several tools used in the past and in the present for software engineering practices. Then, we give particular attention to AI-based tools, including generative language models, discussing how they are located within the proposed taxonomy, and reasoning about possible limitations they currently have. Based on this analysis, we discuss what novel tools could emerge in the middle and long term.

Related papers

• Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows? [73.81908518992161]
  We introduce Spider2-V, the first multimodal agent benchmark focusing on professional data science and engineering. Spider2-V features real-world tasks in authentic computer environments and incorporating 20 enterprise-level professional applications. These tasks evaluate the ability of a multimodal agent to perform data-related tasks by writing code and managing the GUI in enterprise data software systems.
  arXiv  Detail & Related papers  (2024-07-15T17:54:37Z)
• A Systematic Literature Review on the Use of Machine Learning in Software Engineering [0.0]
  The study was carried out following the objective and the research questions to explore the current state of the art in applying machine learning techniques in software engineering processes. The review identifies the key areas within software engineering where ML has been applied, including software quality assurance, software maintenance, software comprehension, and software documentation.
  arXiv  Detail & Related papers  (2024-06-19T23:04:27Z)
• Morescient GAI for Software Engineering [2.4861619769660637]
  Using Generative AI (GAI) for software engineering tasks is one of the most rapidly expanding fields of software engineering research. We present a vision for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.
  arXiv  Detail & Related papers  (2024-06-07T07:38:33Z)
• Automatic Programming: Large Language Models and Beyond [48.34544922560503]
  We study concerns around code quality, security and related issues of programmer responsibility. We discuss how advances in software engineering can enable automatic programming. We conclude with a forward looking view, focusing on the programming environment of the near future.
  arXiv  Detail & Related papers  (2024-05-03T16:19:24Z)
• Automated User Story Generation with Test Case Specification Using Large Language Model [0.0]
  We developed a tool "GeneUS" to automatically create user stories from requirements documents. The output is provided in format leaving the possibilities open for downstream integration to the popular project management tools.
  arXiv  Detail & Related papers  (2024-04-02T01:45:57Z)
• Generative Artificial Intelligence for Software Engineering -- A Research Agenda [8.685607624226037]
  We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for Software Engineering. Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision-making in all software development activities. Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology.
  arXiv  Detail & Related papers  (2023-10-28T09:14:39Z)
• AI in Software Engineering: A Survey on Project Management Applications [3.156791351998142]
  Machine Learning (ML) employs algorithms that undergo training on data sets, enabling them to carry out specific tasks autonomously. AI holds immense potential in the field of software engineering, particularly in project management and planning.
  arXiv  Detail & Related papers  (2023-07-27T23:02:24Z)
• 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)
• Using Document Similarity Methods to create Parallel Datasets for Code Translation [60.36392618065203]
  Translating source code from one programming language to another is a critical, time-consuming task. We propose to use document similarity methods to create noisy parallel datasets of code. We show that these models perform comparably to models trained on ground truth for reasonable levels of noise.
  arXiv  Detail & Related papers  (2021-10-11T17:07:58Z)
• Induction and Exploitation of Subgoal Automata for Reinforcement Learning [75.55324974788475]
  We present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induction of a subgoal automaton, an automaton whose edges are labeled by the task's subgoals. A subgoal automaton also consists of two special states: a state indicating the successful completion of the task, and a state indicating that the task has finished without succeeding.
  arXiv  Detail & Related papers  (2020-09-08T16:42:55Z)
• 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.