Designing the software systems of the future
Abstract
We report here on the Future of Software DesignWorkshop that was held on Jan 12-14, 2018 in Pittsburgh, PA under the sponsorship of the Carnegie Mellon University Software Engineering Institute. The software industry is awash in modern trends that involve artificial intelligence (AI), autonomy, data everywhere, etc. These trends affect the structure of software-intensive systems and their designs. The goal of the workshop was to bring together participants from diverse backgrounds to formulate ideas for software design of future systems and related research opportunities and challenges. In this report we summarize the outcomes of the workshop.
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A proliferation of roles, including specialists, in particular SE phases, such as design or specific types of algorithms such as natural language processing.
“A proliferation of roles, including specialists, in particular, SE phases, such as design (Northrop et al., 2019) or specific types of algorithms such as natural language processing (Byrne, 2017).”
From A software engineering perspective on engineering machine learning systems: State of the art and challenges (2021) · cites this paper as Northrop et al., 2019
Cited by
Sorted by citations
Giray, G. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.111031 · Cited by 187 · see citing works
Adapting Software Architectures to Machine Learning Challenges
Serban, A., & Visser, J. (2022) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner53432.2022.00029 · Cited by 43 · see citing works
“[57], [51], [52], [47] 12 Design Avoid unstructured components which link frameworks or APIs (e.g., glue code).”
InfluentialSafety of Perception Systems for Automated Driving: A Case Study on Apollo
Kochanthara, S., Singh, T., Forrai, A., & Cleophas, L. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3631969 · Cited by 22 · see citing works
- Authors
- Northrop, L., Ozkaya, I., Fairbanks, G., & Keeling, M.
- Affiliations
- Software Engineering Institute (US)
- Software Engineering Institute, Pittsburgh, PA, USA
- Google (United States) (US)
- IBM (United States) (US)
- Venue
- ACM SIGSOFT Software Engineering Notes
- Published
- 2018-12-28
- Volume
- 43
- Issue
- 4
- Pages
- 28-30
- ISSN
- 0163-5948 (ISSN-L)
- OpenAlex
- W3129128093
- Cited by
- 3
- References
- 0
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 1
- FWCI
- 0.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM SIGSOFT Software Engineering Notes (publishedVersion)
- Funders
- U.S. Department of Defense
- U.S. Air Force
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- software design, software-intensive systems, artificial intelligence, autonomy, data-driven systems, software architecture, research challenges
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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