MetricSynth: Framework for Aggregating DORA and KPI Metrics Across Multi-Platform Engineering
- URL: http://arxiv.org/abs/2511.06864v1
- Date: Mon, 10 Nov 2025 09:08:32 GMT
- Title: MetricSynth: Framework for Aggregating DORA and KPI Metrics Across Multi-Platform Engineering
- Authors: Pallav Jain, Yuvraj Agrawal, Ashutosh Nigam, Pushpak Patil,
- Abstract summary: This paper presents the architecture and implementation of a framework designed to provide near-real-time visibility into developer experience (DevEx) and Key Performance Indicator (KPI) metrics for a software ecosystem.<n>By aggregating data from various internal tools and platforms, the system computes and visualizes metrics across key areas such as Developer Productivity, Quality, and Operational Efficiency.<n>The implementation resulted in a significant reduction in manual reporting efforts, saving an estimated 20 person-hours per week, and enabled faster, data-driven bottleneck identification.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In modern, large-scale software development, engineering leaders face the significant challenge of gaining a holistic and data-driven view of team performance and system health. Data is often siloed across numerous disparate tools, making manual report generation time-consuming and prone to inconsistencies. This paper presents the architecture and implementation of a centralized framework designed to provide near-real-time visibility into developer experience (DevEx) and Key Performance Indicator (KPI) metrics for a software ecosystem. By aggregating data from various internal tools and platforms, the system computes and visualizes metrics across key areas such as Developer Productivity, Quality, and Operational Efficiency. The architecture features a cron-based data ingestion layer, a dual-schema data storage approach, a processing engine for metric pre-computation, a proactive alerting system, and utilizes the open-source BI tool Metabase for visualization, all secured with role-based access control (RBAC). The implementation resulted in a significant reduction in manual reporting efforts, saving an estimated 20 person-hours per week, and enabled faster, data-driven bottleneck identification. Finally, we evaluate the system's scalability and discuss its trade-offs, positioning it as a valuable contribution to engineering intelligence platforms.
Related papers
- OpenSage: Self-programming Agent Generation Engine [56.399761469404496]
We propose OpenSage, the first agent development kit (ADK) to automatically create agents with self-generated topology and toolsets.<n>OpenSage offers effective functionality for agents to create and manage their own sub-agents and toolkits.<n>We believe OpenSage can pave the way for the next generation of agent development, shifting the focus from human-centered to AI-centered paradigms.
arXiv Detail & Related papers (2026-02-18T21:16:29Z) - LoCoBench-Agent: An Interactive Benchmark for LLM Agents in Long-Context Software Engineering [90.84806758077536]
We introduce textbfLoCoBench-Agent, a comprehensive evaluation framework specifically designed to assess large language models (LLMs) agents in realistic, long-context software engineering.<n>Our framework extends LoCoBench's 8,000 scenarios into interactive agent environments, enabling systematic evaluation of multi-turn conversations.<n>Our framework provides agents with 8 specialized tools (file operations, search, code analysis) and evaluates them across context lengths ranging from 10K to 1M tokens.
arXiv Detail & Related papers (2025-11-17T23:57:24Z) - CoDA: Agentic Systems for Collaborative Data Visualization [57.270599188947294]
Deep research has revolutionized data analysis, yet data scientists still devote substantial time to manually crafting visualizations.<n>Existing approaches, including simple single- or multi-agent systems, often oversimplify the task.<n>We introduce CoDA, a multi-agent system that employs specialized LLM agents for metadata analysis, task planning, code generation, and self-reflection.
arXiv Detail & Related papers (2025-10-03T17:30:16Z) - Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures [27.473508984130728]
Large-scale scientific collaborations generate petabytes of data, with volumes soon expected to reach exabytes.<n>To manage these computational and storage demands, centralized workflow and data management systems are implemented.<n>A significant obstacle in adopting more effective or AI-driven solutions is the absence of a quick and reliable introspective dynamic model.
arXiv Detail & Related papers (2025-06-24T12:42:36Z) - Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models [83.65386456026441]
Data-Juicer 2.0 is a data processing system backed by 100+ data processing operators spanning text, image, video, and audio modalities.<n>It supports more critical tasks including data analysis, synthesis, annotation, and foundation model post-training.<n>The system is publicly available and has been widely adopted in diverse research fields and real-world products such as Alibaba Cloud PAI.
arXiv Detail & Related papers (2024-12-23T08:29:57Z) - Intelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows [1.4582633500696451]
LangGraph framework is designed to enhance machine learning through scalability, visualization, and intelligent process optimization.<n>At its core, the framework introduces Agent AI, a pivotal innovation that leverages Spark's distributed computing capabilities.<n>The framework also incorporates large language models through the LangChain ecosystem, enhancing interaction with unstructured data.
arXiv Detail & Related papers (2024-12-02T13:41:38Z) - Integration of Domain Expert-Centric Ontology Design into the CRISP-DM for Cyber-Physical Production Systems [45.05372822216111]
Methods from Machine Learning (ML) and Data Mining (DM) have proven to be promising in extracting complex and hidden patterns from the data collected.
However, such data-driven projects, usually performed with the Cross-Industry Standard Process for Data Mining (CRISPDM), often fail due to the disproportionate amount of time needed for understanding and preparing the data.
This contribution intends present an integrated approach so that data scientists are able to more quickly and reliably gain insights into the CPPS challenges.
arXiv Detail & Related papers (2023-07-21T15:04:00Z) - Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data
Programming [77.38174112525168]
We present Nemo, an end-to-end interactive Supervision system that improves overall productivity of WS learning pipeline by an average 20% (and up to 47% in one task) compared to the prevailing WS supervision approach.
arXiv Detail & Related papers (2022-03-02T19:57:32Z) - Exploring the potential of flow-based programming for machine learning
deployment in comparison with service-oriented architectures [8.677012233188968]
We argue that part of the reason is infrastructure that was not designed for activities around data collection and analysis.
We propose to consider flow-based programming with data streams as an alternative to commonly used service-oriented architectures for building software applications.
arXiv Detail & Related papers (2021-08-09T15:06:02Z) - Automated Machine Learning Techniques for Data Streams [91.3755431537592]
This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time.
The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
arXiv Detail & Related papers (2021-06-14T11:42:46Z) - A Flexible and Intelligent Framework for Remote Health Monitoring
Dashboards [4.561162529091813]
ViSierra is a framework for designing data monitoring dashboards in RPM projects.
These platforms will cover all the necessary aspects in a traditional RPM project and combine them with novel functionalities such as machine learning solutions.
arXiv Detail & Related papers (2020-06-09T14:07:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.