一句话：提出 BayesInsights，用贝叶斯网络可视化软件工程过程因果依赖，辅助识别交付挑战与开发者体验问题。
论文报告 Bloomberg 开发 BayesInsights 的经验。面对工程数据规模增长但缺乏结构化表示、难以定位研发缺陷根因或预测变更影响的问题，作者基于文献、专家洞见和结构学习算法定义贝叶斯网络结构，构建可交互可视化工具，展示软件工程过程各环节的因果依赖，并集成到已有数据分析方案。评估采用性能基准与 24 名资深从业者问卷的混合方法；95.8% 参与者认为工具有助于识别团队和组织层面的软件交付挑战，证明其作为软件交付与开发者体验建模概念验证的价值。工具已进入预览，向七个工程团队开放，并规划更广泛部署。
As software in industry grows in size and complexity, so does the volume of engineering data that companies generate and use. Ideally, this data could be used for many purposes, including informing decisions on engineering priorities. However, without a structured representation of the links between different aspects of software development, companies can struggle to identify the root causes of deficiencies or anticipate the effects of changes. In this paper, we report on our experience at Bloomberg in developing a novel tool, dubbed BayesInsights, which provides an interactive interface for visualising causal dependencies across various aspects of the software engineering (SE) process using Bayesian Networks (BNs). We describe our journey from defining network structures using a combination of established literature, expert insight, and structure learning algorithms, to integrating BayesInsights into existing data analytics solutions, and conclude with a mixed-methods evaluation of performance benchmarking and survey responses from 24 senior practitioners at Bloomberg. Our results revealed 95.8% of participants found the tool useful for identifying software delivery challenges at the team and organisational levels, cementing its value as a proof of concept for modelling software delivery and developer experience. BayesInsights is currently in preview, with access granted to seven engineering teams and a wider deployment roadmap in place for the future.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2603-29929,
title = {BayesInsights: Modelling Software Delivery and Developer Experience with Bayesian Networks at Bloomberg},
author = {Serkan Kirbas and Federica Sarro and David Williams},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805242}
}