Problem
Research questions and friction points this paper is trying to address.
software delivery
developer experience
engineering data
causal dependencies
root cause analysis
BayesInsights: Modelling Software Delivery and Developer Experience with Bayesian Networks at Bloomberg
🤖 AI Summary
This work addresses the challenge enterprises face in identifying root causes of software delivery issues or predicting the impact of changes from vast engineering data, primarily due to the absence of structured causal modeling of development workflows. We present the first application of Bayesian networks to large-scale industrial settings, introducing an interactive causal modeling tool that integrates domain literature, expert priors, and structure learning algorithms to enable visual, interpretable reasoning about software delivery and developer experience. Integrated into an existing data analytics platform, the tool was evaluated by 24 senior engineers, with 95.8% reporting it effectively uncovered team- and organization-level delivery bottlenecks. It is currently available in preview to seven engineering teams.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian Networks
Software Delivery
Developer Experience
Causal Modeling
Engineering Analytics
🔎 Similar Papers
No similar papers found.
