Software Engineering Practice in the Development of Deep Learning Applications
arXiv:1910.03156
Abstract
Deep-Learning(DL) applications have been widely employed to assist in various tasks. They are constructed based on a data-driven programming paradigm that is different from conventional software applications. Given the increasing popularity and importance of DL applications, software engineering practitioners have some techniques specifically for them. However, little research is conducted to identify the challenges and lacks in practice. To fill this gap, in this paper, we surveyed 195 practitioners to understand their insight and experience in the software engineering practice of DL applications. Specifically, we asked the respondents to identify lacks and challenges in the practice of the development life cycle of DL applications. The results present 13 findings that provide us with a better understanding of software engineering practice of DL applications. Further, we distil these findings into 7 actionable recommendations for software engineering researchers and practitioners to improve the development of DL applications.
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- End to End Learning for Self-Driving Cars
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Cited by in corpus (5)
- Software Engineering for AI-Based Systems: A Survey
- Making sense of AI systems development
- Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects
- A Comprehensive Study on Challenges in Deploying Deep Learning Based Software
- An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors