Related papers: BayesInsights: Modelling Software Delivery and Developer Experience with Bayesian Networks at Bloomberg

BayesInsights: Modelling Software Delivery and Developer Experience with Bayesian Networks at Bloomberg

URL: http://arxiv.org/abs/2603.29929v1
Date: Tue, 31 Mar 2026 16:02:49 GMT
Title: BayesInsights: Modelling Software Delivery and Developer Experience with Bayesian Networks at Bloomberg
Abstract summary: BayesInsights provides an interactive interface for visualising causal dependencies across various aspects of the software engineering (SE) process using Bayesian Networks (BNs)<n>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.<n>BayesInsights is currently in preview, with access granted to seven engineering teams and a wider deployment roadmap in place for the future.
Score: 5.215605108929471
License: http://creativecommons.org/licenses/by/4.0/
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.

Related papers

The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration [58.062622978943885]
Tool use enables large language models to access external information, invoke software systems, and act in digital environments beyond what can be solved from model parameters alone.<n>As agent systems evolve, the central problem has shifted from isolated invocation to multi-tool orchestration over long trajectories with intermediate state, execution feedback, changing environments, and practical constraints such as safety, cost, and verifiability.<n>We organize the literature around six core dimensions: inference-time planning and execution, training and trajectory construction, safety and control, efficiency under resource constraints, capability completeness in open environments, and benchmark design and evaluation.
arXiv Detail & Related papers (2026-03-24T07:05:05Z)
Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
arXiv Detail & Related papers (2026-01-15T18:55:03Z)
A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
arXiv Detail & Related papers (2025-10-10T06:56:50Z)
A Serverless Architecture for Real-Time Stock Analysis using Large Language Models: An Iterative Development and Debugging Case Study [0.0]
This paper documents the design, implementation, and iterative debug of a novel, serverless system for real-time stock analysis.<n>We detail the architectural evolution of the system, from initial concepts to a robust, event-driven pipeline.<n>The final architecture operates at a near-zero cost, demonstrating a viable model for individuals to build sophisticated AI-powered financial tools.
arXiv Detail & Related papers (2025-07-13T11:29:51Z)
Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z)
Charting a Path to Efficient Onboarding: The Role of Software Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools. This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z)
Collaborative business intelligence virtual assistant [1.9953434933575993]
This study focuses on the applications of data mining within distributed virtual teams through the interaction of users and a CBI Virtual Assistant. The proposed virtual assistant for CBI endeavors to enhance data exploration accessibility for a wider range of users and streamline the time and effort required for data analysis.
arXiv Detail & Related papers (2023-12-20T05:34:12Z)
Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models [11.388023221294686]
This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions.
arXiv Detail & Related papers (2023-10-17T09:53:03Z)
Dataflow graphs as complete causal graphs [17.15640410609126]
We consider an alternative approach to software design, flow-based programming (FBP) We show how this connection can be leveraged to improve day-to-day tasks in software projects.
arXiv Detail & Related papers (2023-03-16T17:59:13Z)
Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
arXiv Detail & Related papers (2022-04-29T08:06:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.