DevOps Handbook: Experiments in Accelerating Delivery

Nov 15, 20172 likes1,519 views

The document summarizes a presentation given at the DevOps Enterprise Summit 2017 by Cindy Payne and Jim Grafmeyer of Nationwide. It discusses Nationwide's 10 year journey to build an in-house software development capability using agile, DevOps, lean, and CMMI principles. It describes experiments they conducted using the DevOps Handbook to reduce lead times, implement continuous delivery practices, and improve developer experience. Nationwide now has over 200 agile teams developing over 2,500 applications using these principles to accelerate software delivery.

DEVOPS ENTERPRISE SUMMIT 2017
DEVOPS HANDBOOK:
EXPERIMENTS IN
ACCELERATING DELIVERY
NOVEMBER 13, 2017
CINDY PAYNE
LEADER OF TECH CONSULTING GROUP,
NATIONWIDE
JIM GRAFMEYER
DIGITAL SOLUTIONS ARCHITECT,
NATIONWIDE FINANCIAL

16+
MILLION
POLICIES
$400MILLION
CONTRIBUTED
TO NONPROFITS
AND COMMUNITIES
SINCE 2000
$209.8
BILLION
IN ASSETS
7th
LARGEST
COMMERCIAL
INSURER
34,000 Nationwide associates
serve customers in every state
$17
BILLION
#68ON THE
FORTUNE
500 LIST
2
NATIONWIDE: PROTECTING WHAT MATTERS MOST
PAID IN CLAIMS
AND BENEFITS
IN 2016

NATIONWIDE IT
#51: COMPUTERWORLD BEST
PLACES TO WORK IN IT
200 AGILE / LEAN
STANDING TEAMS >2500 APPLICATIONS
23
BUSINESS UNITS
5100 IT ASSOCIATES
OF
AND
BALANCE
INNOVATION
DISCIPLINE DEVELOPERS & TESTERS
>2600
50PRIMARY
TECHNOLOGIES

4
ORGANIZATIONAL STRUCTURE
I & O
Cindy
Nationwide IT
Jim
Tools
Run
Platform
Enterprise Applications
Nationwide Financial
Business UnitsShared Services

5
10 YEARS AGO, WE SET OUT TO BUILD A GLOBALLY COMPETITIVE
IN-HOUSE SOFTWARE DEVELOPMENT CAPABILITY
AGILE DEVOPS LEAN CMMI
How we deliver
software with
high quality
How we deliver
with speed,
efficiency, and
low risk
How we
manage IT at
enterprise
scale
How we provide
industry
validation of our
progress
WHERE WE STARTED

6
WHERE WE STARTED
AGILE DEVOPS LEAN CMMI
DevOps house
Methodology
Deployment tooling
Practices
Simplify
Organize
Experiment
Motivate
Tool adoption
Continuous improvement
Self-service
Immersive pairing
Availability (SRE)
Pipeline as a product
• Architecture
• Ownership
• Prioritization
• Funding model
Model lines: speed,
practitioner led
Business planning: Minimum
Marketable Product (MMP),
single embedded owner
Infrastructure engineering
Developer experience
DevOps Handbook
Senior leadership
sponsorship
IT Delivery Model
transformation
• Software development
model
• Lean management
• Suppliers
2015 2016 2017 2018

INTERACTION MODEL
7
Issues/
Blockers
DevOps Leadership
Team
Goals, Scope,
Systems Thinking
DevOps
Platform Team
Team 1
Backlog
Team 1 Team 2
A/B Tests
DevOps
Product
Backlog
Team 2
Backlog
Platform
Backlog
Governance Team

8
HANDBOOK EXPERIMENTS
EPICS
Version Control Everything
Automated Testing (unit, integration, performance)
Continuous Integration to the Trunk
Automated Infrastructure
Continuous Delivery into Production-like Environments
Automate Low-Risk Releases
Architect for Low-Risk Releases
Monitor Everything

DEVELOPER EXPERIENCE
GREAT TOOLS, LOTS OF CONTEXT SWITCHING
9

DEVELOPER EXPERIENCE
EMBRACING CHAT BOTS TO REDUCE CONTEXT SWITCHING
10

DevOps Handbook: Experiments in Accelerating Delivery

Introduced lead time metrics for
all 200 development teams
Empower teams to pick their
course guided by lead time
Drive towards daily, zero
downtime deployments
THE NORTH STAR –
LEAD TIME

Tools enable practices
Tools alone have benefits
Handbook is your map
THE MAP AND THE
EQUIPMENT

Development teams must make
their own journey
Tech Consulting and DevOps
leaders are expert climbers
Cross-pollinate
Support the Delivery Pipeline as
a single product
SUPPORT MODEL
SHERPAs
• Tech Consulting
• Build Capability SMEs
COMING SOON
• Immersive Pairing
• Monthly DevOps Dojo
• Delivery Pipeline Support

High volume of flooded cars
Business ask: optimize online
claims process
IT response: collaborative
design and implementation in 8
hours
Results: streamlined online
claims process by 40%
HURRICANE HARVEY

WE STILL NEED HELP
Leader Experience
Matrixed Org Model
Value Stream
Architecture

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🌍📱👉COPY LINK & PASTE ON GOOGLE http://drfiles.net/ 👈🌍 Adobe After Effects is a software application used for creating motion graphics, special effects, and video compositing. It's widely used in TV and film post-production, as well as for creating visuals for online content, presentations, and more. While it can be used to create basic animations and designs, its primary strength lies in adding visual effects and motion to videos and graphics after they have been edited. Here's a more detailed breakdown: Motion Graphics: . After Effects is powerful for creating animated titles, transitions, and other visual elements to enhance the look of videos and presentations. Visual Effects: . It's used extensively in film and television for creating special effects like green screen compositing, object manipulation, and other visual enhancements. Video Compositing: . After Effects allows users to combine multiple video clips, images, and graphics to create a final, cohesive visual. Animation: . It uses keyframes to create smooth, animated sequences, allowing for precise control over the movement and appearance of objects. Integration with Adobe Creative Cloud: . After Effects is part of the Adobe Creative Cloud, a suite of software that includes other popular applications like Photoshop and Premiere Pro. Post-Production Tool: . After Effects is primarily used in the post-production phase, meaning it's used to enhance the visuals after the initial editing of footage has been completed.

Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak

As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.

Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.Dele Amefo

How Valletta helped healthcare SaaS to transform QA and compliance to grow wi...Egor Kaleynik

This case study explores how we partnered with a mid-sized U.S. healthcare SaaS provider to help them scale from a successful pilot phase to supporting over 10,000 users—while meeting strict HIPAA compliance requirements. Faced with slow, manual testing cycles, frequent regression bugs, and looming audit risks, their growth was at risk. Their existing QA processes couldn’t keep up with the complexity of real-time biometric data handling, and earlier automation attempts had failed due to unreliable tools and fragmented workflows. We stepped in to deliver a full QA and DevOps transformation. Our team replaced their fragile legacy tests with Testim’s self-healing automation, integrated Postman and OWASP ZAP into Jenkins pipelines for continuous API and security validation, and leveraged AWS Device Farm for real-device, region-specific compliance testing. Custom deployment scripts gave them control over rollouts without relying on heavy CI/CD infrastructure. The result? Test cycle times were reduced from 3 days to just 8 hours, regression bugs dropped by 40%, and they passed their first HIPAA audit without issue—unlocking faster contract signings and enabling them to expand confidently. More than just a technical upgrade, this project embedded compliance into every phase of development, proving that SaaS providers in regulated industries can scale fast and stay secure.

Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora

Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.

How to Optimize Your AWS Environment for Improved Cloud PerformanceThousandEyes

Requirements in Engineering AI- Enabled Systems: Open Problems and Safe AI Sy...Lionel Briand

What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora

Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.

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Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AIdanshalev

If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic? Trick question, b/c most can’t. They treat retrieval as nearest-neighbor search. Today, we discussed scaling #GraphRAG at AWS DevOps Day, and the takeaway is clear: VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval. GraphRAG builds a knowledge graph from source documents, allowing for a deeper understanding of the data + higher accuracy.

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TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora

Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.

Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi

Exploring Code Comprehension in Scientific Programming: Preliminary Insight...University of Hawai‘i at Mānoa

This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software. Presented at: The 33rd International Conference on Program Comprehension (ICPC '25) Date of Conference: April 2025 Conference Location: Ottawa, Ontario, Canada Preprint: https://arxiv.org/abs/2501.10037

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Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell

It's time you stopped letting your telemetry data pressure your budgets and get in the way of solving issues with agility! No more I say! Take back control of your telemetry data as we guide you through the open source project Fluent Bit. Learn how to manage your telemetry data from source to destination using the pipeline phases covering collection, parsing, aggregation, transformation, and forwarding from any source to any destination. Buckle up for a fun ride as you learn by exploring how telemetry pipelines work, how to set up your first pipeline, and exploring several common use cases that Fluent Bit helps solve. All this backed by a self-paced, hands-on workshop that attendees can pursue at home after this session (https://o11y-workshops.gitlab.io/workshop-fluentbit).

Who Watches the Watchmen (SciFiDevCon 2025)Allon Mureinik

Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe. We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved? Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security. Talk from SciFiDevCon 2025 https://www.scifidevcon.com/courses/2025-scifidevcon/contents/680efa43ae4f5