一句话：总结在诺基亚落地 ML 软件缺陷预测的十三项经验，帮助学界与工业界弥合采用差距。
本文是一篇经验论文，报告将机器学习软件缺陷预测（ML SDP）引入实际系统级测试质量保证流程中的十三项考量。作者基于在全球领先电信厂商 Nokia 的大规模研究和落地实践，沿业务分析知识体系的逻辑展开：从识别需求与设定要求，到设计实现方案，再到盈利性分析、利益相关者管理与移交。论文指出，尽管 ML SDP 在改善软件质量、降低成本方面具有潜力，工业界采用长期落后于学术成果；这些经验教训旨在为研究者和实践者提供参考，弥合学术界与工业界之间的鸿沟。
This experience paper describes thirteen considerations for implementing machine learning software defect prediction (ML SDP) in vivo. Specifically, we provide the following report on the ground of the most important observations and lessons learned gathered during a large-scale research effort and introduction of ML SDP to the system-level testing quality assurance process of one of the leading telecommunication vendors in the world — Nokia. We adhere to a holistic and logical progression based on the principles of the business analysis body of knowledge: from identifying the need and setting requirements, through designing and implementing the solution, to profitability analysis, stakeholder management, and handover. Conversely, for many years, industry adoption has not kept up the pace of academic achievements in the field, despite promising potential to improve quality and decrease the cost of software products for many companies worldwide. Therefore, discussed considerations hopefully help researchers and practitioners bridge the gaps between academia and industry.
@inproceedings{StradowskiM23,
title = {Bridging the Gap Between Academia and Industry in Machine Learning Software Defect Prediction: Thirteen Considerations},
author = {Szymon Stradowski and Lech Madeyski},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00026}
}