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.

@inproceedings{paperbot5810,
  title = {Threshy: supporting safe usage of intelligent web services},
  author = {Alex Cummaudo and Scott Barnett and Rajesh Vasa and John Grundy},
  booktitle = {FSE},
  year = {2020},
  doi = {10.1145/3368089.3417919}
}