Software-Defined Software: A Perspective of Machine Learning-Based Software Production

As the Moore's Law is ending, and increasingly high demand of software development continues in the human society, we are facing two serious challenges in the computing field. First, the general-purpose computing ecosystem that has been developed for more than 50 years will have to be changed b...

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Bibliographic Details
Published in2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) pp. 1270 - 1275
Main Authors Lee, Rubao, Wang, Hao, Zhang, Xiaodong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Automatic programming
Deep Learning
Hardware
Libraries
Machine Learning
Optimization
Production
Programming
Software
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Summary:As the Moore's Law is ending, and increasingly high demand of software development continues in the human society, we are facing two serious challenges in the computing field. First, the general-purpose computing ecosystem that has been developed for more than 50 years will have to be changed by including many diverse devices for various specialties in high performance. Second, human-based software development is not sustainable to respond the requests from all the fields in the society. We envision that we will enter a time of developing high quality software by machines, and we name this as Software-defined Software (SDS). In this paper, we will elaborate our vision, the goals and its roadmap.
ISSN:2575-8411
DOI:10.1109/ICDCS.2018.00126