Abstraction Engineering
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909295828795392 |
|---|---|
| author | Bencomo, Nelly Cabot, Jordi Chechik, Marsha Cheng, Betty H. C. Combemale, Benoit Wąsowski, Andrzej Zschaler, Steffen |
| author_facet | Bencomo, Nelly Cabot, Jordi Chechik, Marsha Cheng, Betty H. C. Combemale, Benoit Wąsowski, Andrzej Zschaler, Steffen |
| contents | Modern software-based systems operate under rapidly changing conditions and face ever-increasing uncertainty. In response, systems are increasingly adaptive and reliant on artificial-intelligence methods. In addition to the ubiquity of software with respect to users and application areas (e.g., transportation, smart grids, medicine, etc.), these high-impact software systems necessarily draw from many disciplines for foundational principles, domain expertise, and workflows. Recent progress with lowering the barrier to entry for coding has led to a broader community of developers, who are not necessarily software engineers. As such, the field of software engineering needs to adapt accordingly and offer new methods to systematically develop high-quality software systems by a broad range of experts and non-experts. This paper looks at these new challenges and proposes to address them through the lens of Abstraction. Abstraction is already used across many disciplines involved in software development -- from the time-honored classical deductive reasoning and formal modeling to the inductive reasoning employed by modern data science. The software engineering of the future requires Abstraction Engineering -- a systematic approach to abstraction across the inductive and deductive spaces. We discuss the foundations of Abstraction Engineering, identify key challenges, highlight the research questions that help address these challenges, and create a roadmap for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14074 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Abstraction Engineering Bencomo, Nelly Cabot, Jordi Chechik, Marsha Cheng, Betty H. C. Combemale, Benoit Wąsowski, Andrzej Zschaler, Steffen Software Engineering Modern software-based systems operate under rapidly changing conditions and face ever-increasing uncertainty. In response, systems are increasingly adaptive and reliant on artificial-intelligence methods. In addition to the ubiquity of software with respect to users and application areas (e.g., transportation, smart grids, medicine, etc.), these high-impact software systems necessarily draw from many disciplines for foundational principles, domain expertise, and workflows. Recent progress with lowering the barrier to entry for coding has led to a broader community of developers, who are not necessarily software engineers. As such, the field of software engineering needs to adapt accordingly and offer new methods to systematically develop high-quality software systems by a broad range of experts and non-experts. This paper looks at these new challenges and proposes to address them through the lens of Abstraction. Abstraction is already used across many disciplines involved in software development -- from the time-honored classical deductive reasoning and formal modeling to the inductive reasoning employed by modern data science. The software engineering of the future requires Abstraction Engineering -- a systematic approach to abstraction across the inductive and deductive spaces. We discuss the foundations of Abstraction Engineering, identify key challenges, highlight the research questions that help address these challenges, and create a roadmap for future research. |
| title | Abstraction Engineering |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2408.14074 |