# quality assurance Published articles for quality assurance. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## There's more to software development than coding DevFeed: [There's more to software development than coding]() Original publisher: [Read original article]() Author: Dan Lew Published: 2026-08-17T16:38:20Z Content type: opinion Language: en Sources: [Dan Lew Blog]() Topics: [Development](), [coding](), [Artificial Intelligence](), [AI Development](), [Software Engineering]() Tags: [ai](), [career](), [developer](), [developers](), [development](), [engineering](), [engineering-manager](), [like](), [manager](), [product](), [quality-assurance](), [software-development]() ### AI overview This opinion article argues that software development involves much more than writing code. It describes requirements refinement, design and logic review, communication, tradeoff discussions, architecture, project coordination, mentoring, and cross-functional work as important parts of the role. It concludes that these non-coding skills may remain valuable as AI improves at coding. ### Source excerpt Like many software developers, I've been considering the future of my profession in the face of AI. AI can write code. Our job is to write code. It seems natural that we'd be replaced by AI, no? But recently I've been considering the warning ## Agents, Randomness, and Receipts: Notes from trivago's QA Meetup DevFeed: [Agents, Randomness, and Receipts: Notes from trivago's QA Meetup]() Original publisher: [Read original article]() Author: Aasma Jabin Quality Assurance Engineer Linkedin profile Published: 2026-08-12T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Testing](), [Artificial Intelligence](), [AI Agent](), [dora metrics](), [Automation](), [Git](), [Open Source]() Tags: [ai](), [ai-agent](), [automation](), [dora-metrics](), [git](), [open-source](), [pull-requests](), [quality-assurance](), [testing]() ### AI overview The article reports on trivago's QA Meetup, covering talks about AI adoption metrics in software delivery, an AI agent for testing API integrations, and randomized automated tests for finding defects missed by deterministic testing. It also describes Qase.io's git-ai attribution tool and reported changes in DORA metrics after AI adoption. ### Source excerpt In July, we opened the doors of our Düsseldorf campus for this year's QA Meetup, bringing together around 90 people from testing, engineering, and beyond. Hosting this meetup has become a bi... ## How Zapier transformed core marketing processes with ChatGPT Work DevFeed: [How Zapier transformed core marketing processes with ChatGPT Work]() Original publisher: [Read original article]() Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Processes](), [dashboards](), [Optimization]() Tags: [building](), [chatgpt](), [executive](), [optimization](), [processes](), [qa](), [qc](), [quality-assurance](), [quality-control](), [sales](), [work]() ### AI overview Zapier's enterprise marketing team uses ChatGPT Work to automate lead-funnel QA/QC, identify and fix lead problems, build campaign assets, and report recurring pipeline issues. The system reduces manual effort, accelerates campaign execution, and helps generate pipeline value for sales. ### Source excerpt The enterprise marketing team at Zapier uses ChatGPT Work to reduce the number of drop-offs in its lead funnel, build campaign assets, and automate reporting. ## Dogfooding vs. QA vs. User Research DevFeed: [Dogfooding vs. QA vs. User Research]() Original publisher: [Read original article]() Author: Therese Fessenden Published: 2026-08-07T17:00:00Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [dogfooding](), [FIRST]() Tags: [article](), [bugs](), [development](), [dogfooding](), [qa](), [qc](), [quality-assurance](), [quality-control](), [user-research](), [user-testing]() ### AI overview Dogfooding--using a company's own products internally--can reveal bugs, broken flows, crashes, and missing features while building empathy and shared ownership. The article explains that it complements but cannot replace user research, because insiders understand the product too well to represent real users. It also distinguishes genuine dogfooding from performative displays of loyalty. ### Source excerpt Dogfooding, or using your own products internally, helps catch bugs, but it can't replace user research: your team knows too much to represent real users. ## What it means to be a truly AI-native software company DevFeed: [What it means to be a truly AI-native software company]() Original publisher: [Read original article]() Author: Kaushik Gopal Published: 2026-07-01T20:00:00Z Content type: opinion Language: en Sources: [Kaushik Gopal's Site]() Topics: [Artificial Intelligence](), [Testing](), [bug](), [Pull Request](), [User experience (UX)](), [Code](), [Figma]() Tags: [agent](), [ai](), [bug](), [code](), [figma](), [quality-assurance](), [test](), [ux]() ### AI overview An opinion essay sketches how an AI-native software company might redesign quality assurance, product management, and design around agents, shared context, prototypes, automated fixes, and verification loops. ### Source excerpt Everyone says they're rebuilding their company in an AI-native way. But what does that mean? Most companies look at what their existing employees do and try to automate it with AI. That's not it. That's dabbling. A truly AI-native company rethinks every role and tears down the walls between them. None of us can 8-ball this but here's my sketch. It's not exhaustive but hopefully it sparks something: Role changes # Quality Assurance ## The QA team typically finds bugs and dutifully files them in Jira. Engineering has finite capacity, so they fix the P1s and freeze the rest. P2 and P3 bugs die in the backlog -- the UX nits, the copy, the small fixes. An AI-native QA team files the bug and immediately points an agent at it. The agent takes a first pass, finds a root cause, proposes a fix, and opens a PR -- then sends a test build right back to QA to verify. Along the way the bug is updated in detail, so if it needs escalation to the engineer who built it, all the context is right there. A more advanced team has a loop wired to trigger the minute a bug is filed. Product managers ## A PM understands the business and goals well. They write a thoughtful PRD -- but it's often 70% done. How are they to keep the full codebase and every edge case in their head? The engineer starts building, hits those edge cases mid-feature, and bounces it back to the PM who has better intuition. Tweak the PRD, back to the engineer. This can happen for every slice of that remaining 30%, and it's frustrating for everyone. An AI-native PM sends an agent to walk the real code, surface those forks up front, and spin up throwaway prototypes to explore each one. They keep a knowledge base of past decision briefs so the team doesn't rebuild what was already ruled out. With that in hand, they produce a fully specced PRD -- and maybe pushing further, include end-to-end tests defining what done looks like. Designers ## Designers mock up screens in Figma and ship them over, hoping what shows up in production ## LLM-судья для нейроразбора резюме на hh DevFeed: [LLM-судья для нейроразбора резюме на hh]() Original publisher: [Read original article]() Author: Mentalitet (hh.ru) Published: 2026-06-22T05:31:17Z Content type: article Language: ru Sources: [HeadHunter RU]() Topics: [Large Language Model](), [Machine learning]() Tags: [ai](), [ai-quality](), [eval](), [hh-ru](), [hr](), [llm](), [llm-10b5a83db3c5](), [llm-as-a-judge](), [llm-as-judge](), [quality-assurance](), [tag-61cd5a476b1d](), [tag-a939ae104bb0](), [tag-b6914c0b0244](), [tag-d96e238f1419](), [tag-e7e38deac50c]() ### AI overview This article describes how hh.ru designed an LLM judge for its resume-analysis feature in an AI assistant. It explains why reliable evaluation requires its own rubrics, datasets, quality metrics, operating-cost considerations, and controls for hallucinations and interpretation errors. The system assesses candidates against criteria relevant to a specific position, which may be extracted from a vacancy or defined by an employer. ### Source excerpt Создать LLM-судью легко. Гораздо сложнее сделать так, чтобы его оценкам можно было доверять. Мы убедились в этом на практике при разработке нейроразбора резюме для ИИ-помощника hh.ru. Быстро выяснилось, что хороший LLM-судья -- это отдельный продукт со своими рубриками, датасетами, метриками качества и стоимостью эксплуатации. Меня зовут Женя Орлов, я LLM Eval Lead. В этой статье расскажу, как мы проектировали систему оценки для нейроразбора резюме, почему отказались от наивных подходов и какие выводы сделали по ходу разработки. Читать далее ## Inside the Geofence: Faster Pickups Start Here DevFeed: [Inside the Geofence: Faster Pickups Start Here]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-05-12T13:14:04Z Content type: article Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [Testing](), [Mobile](), [real-time](), [Wi-Fi](), [data](), [App]() Tags: [app](), [automation](), [geofencing](), [gps](), [location](), [location-based](), [mobile](), [notifications](), [push-notifications](), [quality-assurance](), [real-time](), [technology](), [testing](), [wi-fi](), [workflows]() ### AI overview The article explains how McDonald's integrates geofencing into mobile field testing to evaluate digital features under real-world conditions such as drive-thru lanes, parking areas, and restaurant environments. It reports that this approach improved accuracy, timing, and reliability for experiences including Ready on Arrival, curbside pickup, loyalty, notifications, and order-preparation workflows. ### Source excerpt Geofencing field testing turns noisy GPS signals into reliable arrivals for drive-thru, curbside, and beyond. By Chandrani Gupta Chowdhury, Sr. Manager, Quality Assurance Engineering Quick Bytes: Lab testing alone can't fully capture the real-world conditions that shape mobile experiences inside McDonald's restaurants As customers drive toward a restaurant, pull into a parking spot, or move through a drive-thru lane, geofencing-enabled field testing shows how digital features respond in real time This approach improved accuracy, timing, and reliability -- strengthening customer experiences like Ready on Arrival (ROA), curbside, and loyalty Mobile experiences don't happen in perfect conditions -- they happen in cars, parking lots, drive-thru lanes, and busy restaurants where signals fluctuate and customer behavior is unpredictable. For McDonald's, ensuring digital features work reliably in these real-world environments is critical to delivering fast, seamless experiences at scale. To meet that challenge, teams began integrating geofencing into field testing, allowing them to observe how mobile features perform as customers approach, arrive, and move through restaurant spaces. Inside the digital boundary of the restaurant Geofencing is a location-based technology that leverages GPS, Wi-Fi, and cellular data to create virtual boundaries around physical locations, enabling applications to trigger predefined actions when users enter or exit those zones. The benefits of incorporating geofencing into field testing are significant. From an operational standpoint, it helps ensure restaurants are prepared to receive and fulfill digital orders efficiently, reducing customer wait times and improving order accuracy. From a customer experience perspective, geofencing enables timely, relevant, and seamless interactions, reinforcing trust in the digital journey. Within the McDonald's ecosystem, geofencing can be applied across restaurant footprints -- including parking areas and drive ## Reducing Failure-Analysis Work for Flaky End-to-End Test Suites DevFeed: [Reducing Failure-Analysis Work for Flaky End-to-End Test Suites]() Original publisher: [Read original article]() Author: Giuseppe Donati Web Test Automation Engineer Not a stereotypical Italian guy; Except Published: 2026-04-15T00:00:00Z Content type: opinion Language: en Sources: [Trivago]() Topics: [Testing](), [Test automation](), [ci](), [Selenium](), [GitHub](), [Latency](), [Server-side rendering](), [Microservice]() Tags: [artificial-intelligence](), [ci](), [github](), [integration](), [latency](), [quality-assurance](), [selenium](), [server](), [server-side-rendering](), [test-automation](), [tests]() ### AI overview The article describes the investigation burden caused by flaky end-to-end tests in trivago's Continuous Integration pipeline. Its extended suite has scenario-level success above 99% but workflow-level success of 20%, requiring frequent analysis of failures caused by flakiness, changing live data, preview-environment latency, and feature churn. ### Source excerpt Does 99.8% represent a good success rate in end-to-end test automation of a web application? What about 99.9%? Maybe yes, but when you have a test suite that has up to 1,000 scenarios, it means ... ## From a long list to a clear signal: baseline-driven accessibility reporting DevFeed: [From a long list to a clear signal: baseline-driven accessibility reporting]() Original publisher: [Read original article]() Author: Benjamin Bischoff Test Automation Engineer; Author; Speaker; Magician Github Profile Linkedin Profile Published: 2026-03-13T00:00:00Z Content type: tutorial Language: en Sources: [Trivago]() Topics: [digital accessibility](), [Accessibility](), [axe-core](), [Test automation](), [Automation](), [Selenium](), [Testing](), [Extension]() Tags: [accessibility](), [accessibility-testing](), [automation](), [axe-core](), [eaa](), [european-accessibility-act](), [frontend](), [quality-assurance](), [selenium](), [test-automation](), [testing]() ### AI overview The article describes a baseline-driven approach to accessibility reporting. It extends an in-house test automation framework so new test runs can be compared with a snapshot of known issues on the main branch, helping teams distinguish regressions from existing technical debt and track changes more clearly. ### Source excerpt The output of most automated accessibility tests is a long list of violations. This format, while comprehensive, makes it difficult to distinguish new issues introduced in a feature branch from ... ## How a Learning Project Became Our Modern Mobile Test Framework DevFeed: [How a Learning Project Became Our Modern Mobile Test Framework]() Original publisher: [Read original article]() Author: Raymond Saba I'm an SDET who enjoys building test automation; Finding Ways Published: 2026-02-18T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Mobile](), [Automation](), [webDriver](), [Concurrency](), [Java]() Tags: [appium](), [automation](), [engineering](), [java](), [migration](), [mobile](), [modularity](), [overhead](), [quality-assurance](), [standards](), [threads](), [webdriver]() ### AI overview This engineering article describes how trivago's aging homegrown mobile automation framework evolved into a shared iOS and Android test framework. It covers the transition from Appium 1 to Appium 2 and later Appium 3, including a rewrite based on W3C WebDriver standards, platform-specific drivers, modularity, and parallel execution with threads in one JVM. ### Source excerpt About six years ago, our mobile automation setup was showing its age. It was a small, homegrown framework that had worked "well enough" for a long time, until we tried to upgrade Java and a few ... ## 📚 3LM: A Benchmark for Arabic LLMs in STEM and Code DevFeed: [📚 3LM: A Benchmark for Arabic LLMs in STEM and Code]() Original publisher: [Read original article]() Author: Basma Boussaha; Leen AlQadi; Mughaira; Shaikha Alsuwaidi; Giulia Campesan; Ahmed Alzubaidi; Mohammed Alyafeai; Hakim Hacid Published: 2025-08-01T14:25:21Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Benchmark](), [LLM evaluation / benchmarking](), [Large language models (LLMs)](), [datasets](), [Code generation]() Tags: [benchmark](), [code](), [code-generation](), [datasets](), [llms](), [ocr](), [quality-assurance](), [stem]() ### AI overview The article introduces 3LM, a benchmark for evaluating Arabic Large Language Models on STEM subjects, structured reasoning, formal logic, and code generation. It combines native educational multiple-choice questions, synthetic high-difficulty STEM questions, and translated code-generation tasks. ### Source excerpt A Blog post by Technology Innovation Institute on Hugging Face ## Tailor-made browser extensions for increased testing efficiency DevFeed: [Tailor-made browser extensions for increased testing efficiency]() Original publisher: [Read original article]() Author: Raymond Saba I'm an SDET who enjoys building test automation; Finding Ways Published: 2025-04-23T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Testing](), [Browser Extension](), [Extension](), [Development]() Tags: [browser](), [development](), [extensions](), [quality-assurance](), [reverse-engineering](), [test-automation](), [testing]() ### AI overview The article describes how trivago's QA team used custom browser extensions to support exploratory testing, reduce cumbersome testing processes, and save time. It discusses an extension created through reverse engineering to inspect Google Tag Manager data-layer values and another that identified the version of the trivago website being viewed. ### Source excerpt When doing exploratory testing on a web application, the nature of activities involves discovering things and not repeating the same patterns. However, there might be aspects that could be impro... ## Reliability recommendations when adopting Kubernetes DevFeed: [Reliability recommendations when adopting Kubernetes]() Original publisher: [Read original article]() Author: Andre Newman Published: 2024-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Gremlin Blog]() Topics: [Kubernetes](), [migration](), [configuration](), [Performance Testing](), [Application Development]() Tags: [best-practices](), [configuration](), [containers](), [deployment](), [kubernetes](), [migration](), [performance-testing](), [platform](), [production](), [qa](), [quality-assurance](), [resource](), [scale]() ### AI overview This article presents four reliability strategies for organizations adopting Kubernetes. It emphasizes treating reliability as an ongoing practice through regular reliability testing, configuration scans, failure recovery, scalable and replicated deployments, failover planning, and correctly configured liveness, readiness, and resource settings. ### Source excerpt General reliability best practices when adopting Kubernetes. ## Streamlining GraphQL Service Testing with Karate DevFeed: [Streamlining GraphQL Service Testing with Karate]() Original publisher: [Read original article]() Author: Armin Aminian SRE; Cloud Native technologies GitHub profile Linkedin profile Published: 2024-07-08T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [GraphQL](), [Testing](), [Microservice](), [API](), [gateway]() Tags: [api](), [architecture](), [backend](), [devops](), [gateway](), [graphql](), [integrity](), [microservice-architecture](), [quality-assurance](), [release-engineering](), [rest](), [schema](), [services](), [testing]() ### AI overview The article describes trivago's move from a GraphQL monolith to a federated microservice architecture and explains the testing challenges this creates. It covers complex nested queries, schema validation, relationships, data integrity, and GraphQL's partial-data error responses. ### Source excerpt Over the last year trivago refactored the existing GraphQL monolith and moved to a microservice architecture, in what is also known as a federated setup. Federated GraphQL, as championed by Apol... ## QA Meetup - 2nd Edition: Presentations and Recap DevFeed: [QA Meetup - 2nd Edition: Presentations and Recap]() Original publisher: [Read original article]() Author: Saurav Sharma QA Lead; Loves APIs; Databases; All Things Backend; Cricket fanatic Linkedin profile Published: 2024-05-21T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Testing](), [Test automation](), [Open Source]() Tags: [engineering-culture](), [meetup](), [open-source](), [qa](), [quality-assurance](), [recap](), [release](), [test-automation]() ### AI overview A recap of trivago's second QA Meetup, held on April 25, 2024, with more than 65 attendees. The article summarizes three presentations covering a custom test reporting solution, quality coaching, and QA ownership of the app release process, along with the event's discussions and social activities. ### Source excerpt The 2nd edition of the qa Meetup was held at the trivago campus on April 25th, 2024 with over 65 attendees and was a huge success. ## CI/CD for MLOps on GitLab (part 1) DevFeed: [CI/CD for MLOps on GitLab (part 1)]() Original publisher: [Read original article]() Author: Raphaël Hoogvliets Published: 2024-02-07T09:17:27Z Content type: tutorial Language: en Sources: [Raphaël Hoogvliets]() Topics: [CI/CD](), [GitLab](), [MLOps](), [Python](), [Testing]() Tags: [ci-cd](), [gitlab](), [mlops](), [pre-commit-hooks](), [python](), [quality-assurance]() ### AI overview A tutorial introducing CI pipelines as part of CI/CD for MLOps and demonstrating how to build a simple CI pipeline with GitLab CI/CD for a Python project. It explains benefits including consistency, speed, scalability, version control, reproducibility, and automated testing. ### Source excerpt Code your way to your first CI pipeline ## End-to-end tests retry strategies DevFeed: [End-to-end tests retry strategies]() Original publisher: [Read original article]() Author: Giuseppe Donati Web Test Automation Engineer Not a stereotypical Italian guy; Except Published: 2023-09-27T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Testing](), [Test automation](), [GitHub Actions](), [CI/CD](), [Continuous Deployment (CD)](), [Front end](), [web applications]() Tags: [continuous-deployment](), [core](), [feature](), [frontend](), [github-actions](), [pull-requests](), [quality-assurance](), [test-automation](), [tests]() ### AI overview This article describes how trivago's frontend QA team uses different end-to-end test retry strategies in GitHub Actions. It explains unconditional retries for core tests, filtered retries for specific failure causes in extended tests, and manual retries for a subset of scenarios, balancing stable results, pull-request confidence, and feedback speed. ### Source excerpt Why should you retry all tests on failure? Why not? This article will not go into details, listing pros and cons of each approach. There are already enough ## QA meetup: Presentations Summary DevFeed: [QA meetup: Presentations Summary]() Original publisher: [Read original article]() Author: Saurav Sharma QA Lead; Loves APIs; Databases; All Things Backend; Cricket fanatic Linkedin profile Published: 2023-08-02T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Testing](), [Robot Framework](), [Test automation](), [observability](), [API](), [Mainframe](), [API Economy]() Tags: [api-testing](), [engineering-culture](), [events](), [open-source](), [qa](), [quality-assurance](), [test-automation](), [testing]() ### AI overview A summary of trivago's QA meetup held on June 22, 2023, covering presentations on Robot Framework, mainframe test modernization, observability, API testing, and the Karate framework, followed by networking among attendees. ### Source excerpt Meetups are one of our favourite events at trivago. They give us the opportunity to learn, share knowledge & experience, as well as network with fellow professionals and enthusiasts in the i... ## How we improved reporting and monitoring of test automation results DevFeed: [How we improved reporting and monitoring of test automation results]() Original publisher: [Read original article]() Author: Giuseppe Donati Web Test Automation Engineer Not a stereotypical Italian guy; Except Published: 2023-02-15T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Testing](), [Test automation](), [GitHub Actions](), [Selenium](), [CI/CD](), [Cloud](), [Kafka](), [elasticsearch](), [kibana](), [logstash](), [Grafana](), [Cucumber]() Tags: [article](), [elasticsearch](), [frontend](), [gcp](), [github-actions](), [grafana](), [kafka](), [kibana](), [logstash](), [monitoring](), [quality-assurance](), [selenium](), [test-automation]() ### AI overview The article describes trivago's updated system for executing, reporting, and monitoring Selenium-based end-to-end tests. It covers GitHub Actions workflows, custom runners on Google Cloud, cloud report storage, Kafka and Logstash processing, Elasticsearch and Kibana visualization, and Grafana and Slack alerting. ### Source excerpt Over the last few years, we completely refactored what was described in our previous article about how we use the ELK sta... ## Database Testing at Yugabyte DevFeed: [Database Testing at Yugabyte]() Original publisher: [Read original article]() Published: 2023-01-13T23:00:00Z Content type: article Language: en Sources: [Dennis Felsing]() Topics: [Testing](), [Database](), [Cloud](), [Scalability](), [rdbms]() Tags: [cloud](), [cloud-native](), [database](), [qa](), [quality](), [quality-assurance](), [rdbms](), [scalability](), [testing]() ### AI overview This article introduces YugabyteDB's database testing philosophy, approaches, and implementations. It describes YugabyteDB as a cloud-native database designed for continuous availability, horizontal scalability, and strong RDBMS features, and directs readers to the YugabyteDB blog for the full post. ### Source excerpt YugabyteDB is a cloud-native database for business-critical enterprise applications. It is designed to provide continuous availability as well as horizontal scalability, while retaining a strong set of RDBMS features. This objective creates a strong quality incentive for us in the Yugabyte Quality Assurance (QA) team. As a member of this team, I am giving an overview of the testing philosophy, approaches, and implementations for YugabyteDB. Read the rest of the blog post over on the YugabyteDB blog. ## Our Android testing process DevFeed: [Our Android testing process]() Original publisher: [Read original article]() Author: Headspace Published: 2022-06-22T05:16:52Z Content type: article Language: en Sources: [Stories by Headspace on Medium]() Topics: [Testing](), [Android](), [unit test](), [Mocking]() Tags: [android](), [android-testing](), [firebase](), [junit](), [mental-health](), [mobile-app-development](), [mobile-test-automation](), [mocking](), [quality-assurance](), [test-lab](), [testing]() ### AI overview Headspace describes its Android testing strategy after rewriting its app, including unit-test coverage standards, selective end-to-end testing, mocking, dependency injection, Robolectric, and Firebase Test Lab. ### Source excerpt Our testing strategy, how we integrate it into our development workflow, and what the future holds for testing at Headspace. By John Qualls, Senior Software Engineer, Greg Rami, Principal Software Engineer, and Anton Gerasimenko, Software Engineer Introduction When we took on the daunting task of rewriting our entire app at the end of 2019, one of our key focus areas was testability. At the time, we had less than 20% of our code covered by unit tests -- no integration or end-to-end (E2E) tests -- and adding any tests to the code base was a big effort. From the beginning, we agreed on the standard of at least 80% unit test coverage on all Pull Requests (PRs), E2E tests for critical flows, and an architecture that was focused on testability. The result is a more robust codebase. Today, we're able to capture critical bugs via our tests suite coverage before they hit our users and, very importantly, it's now trivial, and very fast, to add new tests. Let's detail our testing strategy, how we integrate it into our development workflow, and what the future holds for testing at Headspace. Testing Strategy Shortly after the rewrite, we had hundreds of unit tests and around twenty E2E tests. We wanted to optimize the number of E2E tests that we do to only certain use cases where it makes sense to take on the additional time and costs in exchange for the reduced risk. Test selection pyramid for cost and speedUnit Tests Our unit tests are straightforward. They test a very small unit of code by relying on JUnit 5 and different layers of mocking: MockK to mock all the dependencies that are unrelated to the current test. This is made easier by using good architecture principles such as Single Responsibility Principle (SRP) and Dependency Injection (DI) We introduced a Java Faker library, that provides "fakes" for primitive values that do not need to have a specific value Similarly, we have our own "model Fakers" that provide domain objects. That way when the underlying classes chang ## Spark Joy by Running Fewer Tests DevFeed: [Spark Joy by Running Fewer Tests]() Original publisher: [Read original article]() Author: Jessica Xie Published: 2020-06-11T18:56:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [ci](), [Scalability](), [Development](), [Pull Request](), [Shopify](), [Code](), [Docker]() Tags: [analysis](), [bugs](), [ci](), [code](), [containers](), [developers](), [docker](), [docker-containers](), [performance](), [pull-request](), [quality-assurance](), [scalability](), [shopify](), [spark](), [speed]() ### AI overview This Shopify Engineering article explains how large test suites can slow development, reduce scalability, and increase intermittent failures. It introduces a dynamic test-selection system intended to make continuous integration faster and more stable by running fewer, more relevant tests. ### Source excerpt Developers write tests to ensure correctness and allow future changes to be made safely. However, as the number of features grows, so does the number of tests. Tests are a double-edged sword. On one hand, well-written ones catch bugs and maintain a program's stability, but as the code base grows, a high number of tests impedes scalability because they take a long time to run and increase the likelihood of intermittently failing tests. ## Cluecumber Report Maven Plugin for Cucumber test reporting DevFeed: [Cluecumber Report Maven Plugin for Cucumber test reporting]() Original publisher: [Read original article]() Author: Benjamin Bischoff Test Automation Engineer; Author; Speaker; Magician Github Profile Linkedin Profile Published: 2017-11-16T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Cucumber](), [test](), [Maven](), [Java]() Tags: [css](), [frontend](), [html](), [images](), [java](), [maven](), [maven-plugin](), [monitoring](), [open-source](), [plugin](), [quality-assurance](), [release](), [reporting](), [test]() ### AI overview The article explains why trivago replaced its existing Cucumber reporting Maven plugin and describes the design of the Cluecumber Report Plugin. The new plugin uses HTML templates and generates overview and scenario-detail pages from Cucumber JSON results. ### Source excerpt We were not as happy as we could be with out Cucumber test reporting solution - so we decided to build a new and shiny one from scratch. ## Cucable Maven plugin for parallel execution of Cucumber scenarios DevFeed: [Cucable Maven plugin for parallel execution of Cucumber scenarios]() Original publisher: [Read original article]() Author: Benjamin Bischoff Test Automation Engineer; Author; Speaker; Magician Github Profile Linkedin Profile Published: 2017-06-30T00:00:00Z Content type: tutorial Language: en Sources: [Trivago]() Topics: [Cucumber](), [Maven](), [test](), [Selenium](), [ci]() Tags: [browser](), [ci](), [frontend](), [maven](), [maven-plugin](), [open-source](), [parallel](), [plugin](), [quality-assurance](), [speed](), [test](), [user-experience]() ### AI overview Trivago describes creating Cucable, a Maven plugin that splits Cucumber scenarios into smaller chunks for parallel execution. The article explains limitations of custom runners, tag-based execution, feature-based execution, and existing plugins, particularly for scenario outlines and CI automation. ### Source excerpt Running Cucumber scenarios in parallel can be tricky, especially when a custom test runner is used. That's why we created Cucable - a Maven plugin to split test scenarios into smaller chunks that can be run at the same time. [Next page]()