| Datum
|
2019/11/06 11:30:00 – 2019/11/06 12:30:00
|
| Ort
|
Gebäude 50.34, Raum 333
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| Vortragende(r)
|
Snigdha Singh
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| Forschungsgruppe
|
ARE
|
| Titel
|
A Survey of Software Quality for MachineLearning Applications
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| Autoren
|
Satoshi Masuda, Kohichi Ono, Toshiaki Yasue, Nobuhiro HosokawaIBM Research, TokyoChuou-ku, Tokyo, Japan
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| PDF
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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8411764&isnumber=8411704
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| URL
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https://ieeexplore.ieee.org/document/8411764/references#references
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| BibTeX
|
https://sdqweb.ipd.kit.edu/wiki/BibTeX-Eintrag/Masuda2018
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| Abstract
|
Machine learning (ML) is now widespread. Traditional software engineering can be applied to the development of ML applications. However, we have to consider specific problems with ML applications in terms of their quality. In this paper, we present a survey of software quality for ML applications to consider the quality of ML applications as an emerging discussion. From this survey, we raised problems with ML applications and discovered software engineering approaches and software testing research areas to solve these problems. We classified survey targets into Academic Conferences, Magazines, and Communities. We targeted 16 academic conferences on artificial intelligence and software engineering, including 78papers. We targeted 5 Magazines, including 22 papers. The results indicated key areas, such as deep learning, fault localization,and prediction, to be researched with software engineering and testing.
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