From webform to API using microframeworks

Sep 17, 2020Download as PPTX, PDF1 like65 views

This document discusses converting a webform into an API using microframeworks like Quarkus and libraries such as Jsoup and WireMock. It notes that while microframeworks allow rapid prototyping with few lines of code and fast development, the resulting API can be brittle and break if the underlying form or responses change. The document provides links to the Quarkus, Jsoup, Jib, and WireMock open source projects discussed.

BOSA.be
using microframeworks
17 September 2020 – Brussels
Bart Hanssens
BOSA DG Digital Transformation
From webform
to API

Java open source components
• Quarkus: cloud-first, REST, OpenAPI
• Alternatives: Micronaut, Helidon, (Spring Boot)
• Jsoup: parsing (X)HTML
• If a full browser would be required => HtmlUnit
• WireMock: mocking HTTP responses
• Jib: maven plugin for building containers
• Docker not required on dev machine

Evaluation
• Rapid prototyping
• Very few lines of code
• A few days from develop to deployment
• Extremely fast to develop…
• No changed in the backend needed
• …but also very brittle
• If the form or response changes, the API will break

Links
• https://quarkus.io
• https://jsoup.org
• https://github.com/GoogleContainerTools/jib
• http://wiremock.org

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Shift Left using Lean for Agile Software DevelopmentSathyaShankar6

Mastering OOP: Understanding the Four Core PillarsMarcel David

Visit for updated note: https://www.notion.so/Four-Pillars-of-Object-Oriented-Programming-OOP-1e2d7d9612808079b7c5f938afd62a7b?pvs=4 Dive into the essential concepts of Object-Oriented Programming (OOP) with a detailed explanation of its four key pillars: Encapsulation, Inheritance, Polymorphism, and Abstraction. Understand how these principles contribute to robust, maintainable, and scalable software development.

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).

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Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185

GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise. https://arxiv.org/abs/2501.16495

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.

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Microsoft AI Nonprofit Use Cases and Live Demo_2025.04.30.pdfTechSoup

In this webinar we will dive into the essentials of generative AI, address key AI concerns, and demonstrate how nonprofits can benefit from using Microsoft’s AI assistant, Copilot, to achieve their goals. This event series to help nonprofits obtain Copilot skills is made possible by generous support from Microsoft. What You’ll Learn in Part 2: Explore real-world nonprofit use cases and success stories. Participate in live demonstrations and a hands-on activity to see how you can use Microsoft 365 Copilot in your own work!

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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.

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Designing AI-Powered APIs on Azure: Best Practices& ConsiderationsDinusha Kumarasiri

AI is transforming APIs, enabling smarter automation, enhanced decision-making, and seamless integrations. This presentation explores key design principles for AI-infused APIs on Azure, covering performance optimization, security best practices, scalability strategies, and responsible AI governance. Learn how to leverage Azure API Management, machine learning models, and cloud-native architectures to build robust, efficient, and intelligent API solutions