一句话：刻画智能软件功能的"可训练性"，并提出在汽车持续集成与部署流水线中用自动化质量门对其进行持续评估。
软件密集型系统越来越多地借助机器学习与运行时优化来增强功能，其中的"智能软件功能"（SSF，如个人驾驶助理、推荐引擎）会依据用户的隐式与显式反馈数据逐步学习并适应用户偏好，这种从交互数据中学习的能力被作者称为"可训练性"（trainability）。新开发或改进的 SSF 特性必须评估其对系统可训练性的影响，然而尽管已有机器学习的持续部署方法，可训练性评估尚未进入持续集成与部署（CID）流水线。本文阐述了 SSF 开发中可训练性的多个侧面，并提出一种在汽车 CID 框架内自动开展可训练性评估的方法，主张用自动化质量门对机器学习模型进行持续评估。基于八辆真实 BMW 车辆数据的指示性评估结果表明，在 SSF 开发中持续、严格地进行可训练性评估十分重要。
More and more software-intensive systems employ machine learning and runtime optimization to improve their functionality by providing advanced features (e. g. personal driving assistants or recommendation engines). Such systems incorporate a number of smart software functions (SSFs) which gradually learn and adapt to the users' preferences. A key property of SSFs is their ability to learn based on data resulting from the interaction with the user (implicit and explicit feedback)-which we call trainability. Newly developed and enhanced features in a SSF must be evaluated based on their effect on the trainability of the system. Despite recent approaches for continuous deployment of machine learning systems, trainability evaluation is not yet part of continuous integration and deployment (CID) pipelines. In this paper, we describe the different facets of trainability for the development of SSFs. We also present our approach for automated trainability evaluation within an automotive CID framework which proposes to use automated quality gates for the continuous evaluation of machine learning models. The results from our indicative evaluation based on real data from eight BMW cars highlight the importance of continuous and rigorous trainability evaluation in the development of SSFs.
@inproceedings{paperbot5183,
title = {Automated Trainability Evaluation for Smart Software Functions},
author = {Ilias Gerostathopoulos and Stefan Kugele and Christoph Segler and Tomas Bures and Alois Knoll},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00096}
}