Interpreting Cloud Computer Vision Pain-Points: A Mining Study of Stack Overflow

• URL: http://arxiv.org/abs/2001.10130v1
• Date: Tue, 28 Jan 2020 00:56:51 GMT
• Title: Interpreting Cloud Computer Vision Pain-Points: A Mining Study of Stack Overflow
• Authors: Alex Cummaudo, Rajesh Vasa, Scott Barnett, John Grundy, Mohamed Abdelrazek
• Abstract summary: This study investigates developers' frustrations with computer vision services. We find that unlike mature fields like mobile development, there is a contrast in the types of questions asked by developers. These indicate a shallow understanding of the technology that empower such systems.
• Score: 5.975695375814528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services and products to users, and large technology firms enable this via RESTful APIs. While such APIs promise an easy-to-integrate on-demand machine intelligence, their current design, documentation and developer interface hides much of the underlying machine learning techniques that power them. Such APIs look and feel like conventional APIs but abstract away data-driven probabilistic behaviour - the implications of a developer treating these APIs in the same way as other, traditional cloud services, such as cloud storage, is of concern. The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision. We use Stack Overflow to mine indications of the frustrations that developers appear to face when using computer vision services, classifying their questions against two recent classification taxonomies (documentation-related and general questions). We find that, unlike mature fields like mobile development, there is a contrast in the types of questions asked by developers. These indicate a shallow understanding of the underlying technology that empower such systems. We discuss several implications of these findings via the lens of learning taxonomies to suggest how the software engineering community can improve these services and comment on the nature by which developers use them.

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