A Metadata-Driven Deployment Risk Scoring Framework for Enterprise Salesforce CI/CD Pipelines

DOI

10.2991/978-94-6239-799-6_11How to use a DOI?

Keywords

Salesforce; Deployment Risk; CI/CD Pipelines; Metadata Analysis; Static Analysis; Risk Prediction

Abstract

When a Salesforce deployment fails, the cleanup is rarely quick. Somebody has to roll it back by hand, the data fixes crawl along, and pinning down which single piece of a 90-component package knocked the org over can swallow the better part of a day. Underneath all of that sits a design choice most enterprise CI/CD pipelines never revisit: they run the same gate logic against every deployment, whether the package is trivial or structurally dangerous. Our answer is a tool that grades a package before it ever executes. It parses the manifest, talks to the target org over the Metadata API, and comes back with a risk score on a 0–100 scale, built from ten features, among them test coverage delta, dependency chain depth, whether the package mutates schema, and cyclomatic complexity. The whole thing finishes inside three seconds. From there the score decides what the pipeline does next: wave the deployment through, insist on a full org-wide Apex test run first, or freeze the package for architect review with a written rollback plan attached. We ran it live in the pipelines of six enterprise Salesforce organizations for half a year, across 847 deployments. The failure rate went from 23.4% down to 8.1%, and rollbacks came down by 66.3%. Because the Copado and Gearset integrations sit on hook interfaces those platforms already expose, none of this asked for new infrastructure.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Murali Mohan Reddy Seelam
AU  - Vyshnavi Thanneeru
AU  - Amrut Pagidipally
PY  - 2026
DA  - 2026/09/30
TI  - A Metadata-Driven Deployment Risk Scoring Framework for Enterprise Salesforce CI/CD Pipelines
BT  - Proceedings of the International Conference on Emerging Trends and Technologies in Applications of Computer Science (ICETTACS 2026)
PB  - Atlantis Press
SP  - 146
EP  - 156
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-799-6_11
DO  - 10.2991/978-94-6239-799-6_11
ID  - Seelam2026
ER  -