Threshy: Supporting Safe Usage of Intelligent Web Services
This addresses the challenge for software developers in safely deploying intelligent web services, though it is incremental as it builds on existing evaluation tools by extending them to new workflows.
The paper tackles the problem of software developers lacking a systematic approach to set decision thresholds for intelligent web services, presenting Threshy, a tool that helps select thresholds suited to specific problem domains and integrates into multiple workflows, including pre-development and support.
Increased popularity of `intelligent' web services provides end-users with machine-learnt functionality at little effort to developers. However, these services require a decision threshold to be set which is dependent on problem-specific data. Developers lack a systematic approach for evaluating intelligent services and existing evaluation tools are predominantly targeted at data scientists for pre-development evaluation. This paper presents a workflow and supporting tool, Threshy, to help software developers select a decision threshold suited to their problem domain. Unlike existing tools, Threshy is designed to operate in multiple workflows including pre-development, pre-release, and support. Threshy is designed for tuning the confidence scores returned by intelligent web services and does not deal with hyper-parameter optimisation used in ML models. Additionally, it considers the financial impacts of false positives. Threshold configuration files exported by Threshy can be integrated into client applications and monitoring infrastructure. Demo: https://bit.ly/2YKeYhE.