# test-coverage Published articles for test-coverage. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Improving Test Coverage in the Interview Coach Application with GitHub Copilot DevFeed: [Improving Test Coverage in the Interview Coach Application with GitHub Copilot]() Original publisher: [Read original article]() Author: Aaron Powell Published: 2026-09-15T16:00:21Z Content type: tutorial Language: en Sources: [Visual Studio Blog]() Topics: [test-coverage](), [Test coverage](), [GitHub Copilot](), [App](), [Visual Studio](), [Code]() Tags: [agent](), [analysis](), [code-coverage](), [copilot](), [github-copilot](), [productivity](), [projects](), [run](), [test](), [test-coverage](), [testing](), [tests](), [unit-tests](), [visual-studio]() ### AI overview This tutorial explains how to improve test coverage in the Interview Coach application by establishing a baseline, using GitHub Copilot's Test Agent to generate unit tests, and analyzing coverage across the solution. The article reports coverage increasing from 37% to 81%. ### Source excerpt Today I want to look at how we can improve on the test coverage that we have in the Interview Coach application. This application has some unit tests in it already, but I'm sure there is room for improvement. Defining our baseline Before we start writing tests, it's a good idea to have a baseline [...] The post Today I will... improve test coverage appeared first on Visual Studio Blog. ## Making testing work better for AI driven development DevFeed: [Making testing work better for AI driven development]() Original publisher: [Read original article]() Author: Pete Miloravac Published: 2026-09-10T09:28:14Z Content type: release Language: en Sources: [Semaphore Engineering]() Topics: [Testing](), [Development](), [Artificial Intelligence](), [Test coverage](), [RSpec]() Tags: [ai](), [flaky](), [framework](), [product-news](), [test-coverage](), [testing](), [xunit]() ### AI overview Semaphore describes product work to make testing more useful for developers and AI agents. The update covers richer test reports from testing frameworks, signal-based flaky-test detection, and code-graph-based selection of tests affected by code changes. ### Source excerpt AI agents can generate and modify code quickly. But when a test fails, speed is no longer enough. The agent needs enough context to understand what happened, why it happened, and whether the code or the test itself needs to change. In our latest product update, Marko Gaćeša is joined by Semaphore engineers Nick and [...] The post Making testing work better for AI driven development appeared first on Semaphore. ## The Rider 2026.3 Early Access Program Is Open DevFeed: [The Rider 2026.3 Early Access Program Is Open]() Original publisher: [Read original article]() Author: Alexander Kurakin Published: 2026-09-07T09:35:21Z Content type: release Language: en Sources: [The JetBrains Blog]() Topics: [debugging](), [C++]() Tags: [c-plus-plus](), [code-completion](), [debugger](), [debugging](), [eap](), [game-development](), [gamedev](), [net-tools](), [rider](), [test-coverage](), [unreal-engine]() ### AI overview Rider 2026.3's first Early Access build adds rainbow brackets, streamlined data-breakpoint setup, a consolidated game-development plugin category, and code-completion filters. ### Source excerpt The first Early Access build for Rider 2026.3 is now available! It includes rainbow brackets, an easier way to set data breakpoints, a new Game Development plugin category, and filters in code completion. You can get the build from our website or through the Toolbox App. Here's a look at what's new. Rainbow brackets: You [...] ## ESP-BIST in Action: A Self-Testing HMI using ESP32-P4 DevFeed: [ESP-BIST in Action: A Self-Testing HMI using ESP32-P4]() Original publisher: [Read original article]() Author: John Lee Published: 2026-08-22T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32-P4](), [Testing](), [Test coverage](), [Espressif](), [Microcontroller](), [RISC-V](), [boot]() Tags: [blog](), [display](), [esp32-p4](), [espressif](), [hmi](), [lvgl](), [modbus](), [risc-v](), [safety](), [startup](), [test-coverage](), [testing]() ### AI overview This tutorial explains how ESP-BIST provides IEC 60730 Class B self-test coverage on Espressif SoCs. It demonstrates an ESP32-P4-powered HMI using an M5Stack Tab5, Modbus communication with a simulated motor controller, and an LVGL touchscreen. The article describes post-boot and runtime tests, safe-state handling, and Host Diagnostics using the low-power RISC-V core. ### Source excerpt ESP-BIST brings IEC 60730 Class B self-test coverage to Espressif SoCs, and this article walks through what that actually means before putting it to work. Using an ESP32-P4-based M5Stack Tab5 as a Modbus HMI for a simulated motor controller, we show how a SoC that can prove its own health is a chip you can trust to draw a "safe" screen without lying about it. ## Porting Django to Go with AI Without the Naive Rewrite DevFeed: [Porting Django to Go with AI Without the Naive Rewrite]() Original publisher: [Read original article]() Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Django](), [Go Language](), [Artificial Intelligence](), [migration](), [Testing](), [test-coverage](), [Refactoring](), [proxy]() Tags: [ai](), [claude](), [copilot](), [django](), [go](), [migration](), [proxy](), [refactoring](), [test-coverage](), [testing]() ### AI overview The article argues that porting a Django API to Go with AI requires strong engineering foundations rather than line-by-line translation. It recommends tests, production-shaped traffic, clear module boundaries, code review, and incremental rollout mechanisms to establish behavioral parity and reduce migration risk. ### Source excerpt . [Porting Django to Go with AI Without the Naive Rewrite](porting-django-to-go-with-ai-cover... ## A Gradle Module Structure for Clear Boundaries, Faster Builds, and Test Coverage DevFeed: [A Gradle Module Structure for Clear Boundaries, Faster Builds, and Test Coverage]() Original publisher: [Read original article]() Author: Jesse Wilson Published: 2026-07-15T13:38:11Z Content type: article Language: en Sources: [Blogs on Ryan Harter]() Topics: [Gradle](), [build times](), [modules](), [test-coverage](), [Android Gradle Plugin](), [Kotlin]() Tags: [android-gradle-plugin](), [build](), [build-times](), [gradle](), [kotlin](), [modules](), [test-coverage]() ### AI overview The article describes a Gradle module convention that separates each feature's public API from its implementation. The author says this structure reduced build times and coupling between features while helping enforce test coverage, with Android Gradle Plugin support making a separate test-fixtures module unnecessary. ### Source excerpt Flattening my Dependency Graph Jesse Wilson Jesse explores a Gradle module structure that he likes. Read now ➡ I liked this not-so-recent post from Jesse Wilson, which outlines a Gradle module convention that prioritizes clear public/private boundaries to support encapsulation and build speed. The approach, adapted from one presented by Ralf Wondratschek, also happens to closely resemble one that I helped institute at Dropbox shortly after I joined four and a half years ago. ## Mutation testing comes to DAML DevFeed: [Mutation testing comes to DAML]() Original publisher: [Read original article]() Author: "Kamil Chmielewski" Published: 2026-07-08T11:00:00Z Content type: article Language: en Sources: [The Trail of Bits Blog](), [The Trail of Bits Blog]() Topics: [mutation-testing](), [test-coverage](), [Authorization](), [Code](), [Open Source]() Tags: [applications](), [blockchain](), [bug](), [code](), [development](), [mutation-testing](), [open](), [open-source](), [production](), [test-coverage](), [testing](), [tool-release]() ### AI overview Mewt, an open-source mutation-testing engine, now supports DAML, the language used for Canton Network applications. The article explains how mutation testing exposes gaps that ordinary coverage reports can miss by generating modified versions of code and checking which changes survive the existing test suite. ### Source excerpt In April we released Mewt, our open-source mutation-testing engine that finds the gaps in your test suite. Today we're expanding it with support for DAML, the language Canton Network applications are written in. Mewt now reads DAML, generates several classes of mutants (including two built for DAML's authorization primitives), and runs them through your existing test suite to count how many mutants survive. If you want to try it, simply install Mewt from the repository, point a mewt.toml at your project and its test command, and use mewt run. For a team shipping DAML to production, that count is what a passing test run is actually worth: it puts a number on how much your suite checks, whereas a green run on its own does not. Why DAML's coverage reports lie Test coverage is the most reassuring lie in smart-contract development. Hitting 100% line coverage tells you the test runner walked the code; it does not tell you whether any test would fail if that code stopped doing what it is supposed to. We have been grading test harnesses by how many mutants they kill since at least 2019, and our primer on finding the bugs your tests don't catch shows how a green suite can still miss the bug that matters. DAML's built-in coverage measures execution at the template and choice level: which templates were created and which choices were exercised over the test run. It reports whether each choice was exercised, not what happened inside it. A test that exercises a choice once and asserts nothing about the result reports that choice as covered. The report prints the same green percentage whether the test verifies the outcome or discards it. How mutation testing works Instead of asking whether your tests reached the code, mutation testing grades your tests by sabotaging that code. The engine generates mutants, copies of the code that each carry one small deliberate change: a flipped comparison, a removed branch, a dropped party. It then runs your test suite against each one. A mutant ## TestParameterInjector introduces an idiomatic Kotlin API DevFeed: [TestParameterInjector introduces an idiomatic Kotlin API]() Original publisher: [Read original article]() Author: Google Open Source (noreply@blogger.com) Published: 2026-05-25T18:30:00Z Content type: release Language: en Sources: [Google Open Source Blog]() Topics: [Kotlin](), [Testing](), [unit tests](), [test-coverage](), [Java](), [Open Source]() Tags: [java](), [junit4](), [junit5](), [kotlin](), [open-source](), [parameterized-tests](), [test-coverage](), [testing](), [testparameterinjector](), [unit-tests]() ### AI overview Google announces KotlinTestParameters, a Kotlin-only API for TestParameterInjector. It uses Kotlin default function arguments to make parameterized tests type-safe, concise, and compatible with refactoring tools, avoiding string-based YAML maps and verbose provider classes. ### Source excerpt by Jens Nyman, TestParameterInjector Team In March 2021, we announced the open source release of TestParameterInjector: a simple but powerful parameterized test runner for JUnit4. In September 2022, we followed up with JUnit5 support, bringing our framework to developers who had moved on to the Jupiter API. We're excited to announce our biggest update yet for our Kotlin users: KotlinTestParameters. The de facto standard for parameterized testing When we first introduced TestParameterInjector, we shared a graph showing its rapid adoption within Google. Over the past few years, that trajectory has continued to a point where TestParameterInjector is the de facto parameterized test framework. Usage of all other alternative frameworks continues to steadily decline, while TestParameterInjector's adoption keeps growing rapidly. It has fundamentally lowered the barrier to writing data-driven unit tests, empowering Googlers and open source developers alike to maximize test coverage with minimal boilerplate. We believe its ubiquity internally is a strong testament to its reliability and utility for the broader developer communities. The Kotlin challenge As Kotlin's popularity has surged, developers have naturally been writing more of their TestParameterInjector tests in Kotlin. However, specifying explicit test values in Kotlin historically meant falling back to Java-centric paradigms. If you wanted to provide specific values to a test, you typically had three options, none of which felt truly idiomatic in Kotlin: @TestParameter({"123", "456"}): This relies on string arrays, limiting you to a subset of types that the string parsing supports. @TestParameters: This allows for more complex sets of data, but relies on YAML strings (e.g.,"{age: 17, expectIsAdult: false}"). These strings however are not type-safe, and are completely ignored by IDE refactoring tools. Provider classes: For complex types that couldn't be easily represented in strings, you have to write Provider classe ## How Virgin Atlantic ships faster with Codex DevFeed: [How Virgin Atlantic ships faster with Codex]() Original publisher: [Read original article]() Published: 2026-05-22T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [codex](), [App](), [test-coverage](), [Code](), [Front end](), [Figma]() Tags: [app](), [code](), [codex](), [developers](), [engineering](), [figma](), [scrum](), [test-coverage]() ### AI overview Virgin Atlantic used Codex to deliver a revamped mobile app during the Christmas travel rush, achieving near-complete unit test coverage and zero P1 defects at launch. The team also used Codex to accelerate legacy-code refactoring and build a front-end application from a Figma prototype. ### Source excerpt How Virgin Atlantic used Codex to ship its revamped mobile app on a fixed holiday travel deadline, reaching near-total unit test coverage and zero P1 defects. ## Introducing Nova, our internal platform for coding agents DevFeed: [Introducing Nova, our internal platform for coding agents]() Original publisher: [Read original article]() Author: Yasmin McDowell,Lawrence Good,Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler Published: 2026-05-21T16:00:00Z Content type: article Language: en Sources: [Dropbox Tech Blog]() Topics: [AI-assisted coding](), [Nova](), [AI Development](), [software-development](), [Software Engineering](), [cloud-infrastructure](), [debugging](), [test-coverage]() Tags: [agents](), [ai](), [ai-agents](), [cloud](), [coding](), [debugging](), [developer](), [developer-productivity](), [developer-tools](), [developer-velocity](), [machine-learning](), [nova](), [platform](), [software-development](), [test-coverage](), [workflows]() ### AI overview Dropbox introduces Nova, an internal cloud platform for running coding agents. The platform supports parallel coding sessions and automated workflows across the software development lifecycle, including debugging, dependency updates, test-coverage improvements, and flaky-test fixes. ### Source excerpt Nova lets engineers run multiple coding sessions in parallel and lets internal systems use AI agents as part of automated workflows. ## Breakpoint 2026 Speaker Spotlight: Andrew Knight DevFeed: [Breakpoint 2026 Speaker Spotlight: Andrew Knight]() Original publisher: [Read original article]() Author: Shweta Chhillar Published: 2026-05-08T10:14:54Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [Artificial Intelligence](), [Behavior-driven development](), [Automation](), [Selenium](), [Playwright](), [MCP Server](), [Open Source]() Tags: [2026](), [ai](), [automation](), [breakpoint](), [breakpoint-spotlight](), [browser](), [enterprise](), [mcp](), [mcp-server](), [open-source](), [qa](), [quality-engineering](), [selenium](), [software](), [software-testing](), [test-coverage](), [testing](), [webdriver]() ### AI overview This speaker spotlight discusses how Andrew Knight combines open-source advocacy with enterprise software testing. It focuses on AI-powered test generation, behavior-driven development, context engineering, human oversight, and tools such as Selenium WebDriver, Playwright AI tooling, CLI skills, and an MCP server. ### Source excerpt Explore how Andrew Knight is using AI, behavior-driven development, and context engineering to reshape enterprise software testing at BrowserStack Breakpoint 2026. This speaker spotlight dives into AI-powered QA, test automation, and the future of modern quality engineering. ## How we automatically test the world's most secure Linux distribution DevFeed: [How we automatically test the world's most secure Linux distribution]() Original publisher: [Read original article]() Published: 2026-05-01T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [test-coverage](), [chainguard os](), [Linux](), [chainguard containers]() Tags: [chainguard](), [chainguard-containers](), [chainguard-os](), [chainguard-packages](), [chainguard-testing](), [linux](), [test-coverage]() ### AI overview Chainguard OS has achieved 100% test coverage across its package and subpackage catalog. The automated tests execute software, verify behavior, and test service lifecycles across x86_64 and aarch64 packages before release. ### Source excerpt Chainguard OS achieves 100% package test coverage, verifying every component runs correctly to make rolling updates secure, reliable, and enterprise-ready. ## What Happened When We Treated AI Like an Engineering Teammate DevFeed: [What Happened When We Treated AI Like an Engineering Teammate]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-04-28T13:23:51Z Content type: opinion Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [Artificial Intelligence](), [AI Engineering](), [maintenance](), [Security](), [Cloud](), [test-coverage](), [Documentation](), [Framework]() Tags: [agent](), [ai](), [ai-agent](), [ai-engineering](), [autonomous](), [cloud](), [development](), [documentation](), [maintenance](), [platform-engineering](), [security](), [software-engineering](), [test-coverage](), [the-result](), [velocity]() ### AI overview The Restaurant Topology Management team used an AI-powered, agent-based engineering assistant within its development workflows to handle repetitive maintenance work, including configuration updates, security patches, framework modernization, test coverage, and documentation. The article reports 20% faster sprint velocity, security updates completed twice as fast, and modernization delivered up to 10 times faster. ### Source excerpt How an AI engineering assistant boosted sprint velocity by 20% and modernized services up to 10x faster. by: Sumedha Shenoy, Director, Engineering Tech Lead, and Kamal Jackson, Sr Manager, Engineering Tech Lead Quick Bytes: A growing backlog of essential but repetitive "glue work" was slowing the Restaurant Topology Management (RTM) team's ability to modernize and deliver new features By treating an AI engineering assistant as an autonomous teammate -- not a chatbot -- the team offloaded end-to-end maintenance tasks at scale The result was 20% faster sprint velocity, security updates completed twice as fast, and modernization work delivered up to 10x quicker without sacrificing quality In fast-moving software environments, critical but repetitive "glue work" like configuration updates, security patches, and documentation often compete with time that could be spent delivering new features. For the Restaurant Topology Management (RTM) team -- who build and maintain the cloud-based platform that enables markets to create and deploy consistent restaurant device configurations -- this maintenance workload had grown quietly over time and began to limit the team's ability to innovate. To regain momentum and address mounting operational debt, the team turned to an AI-powered, agent-based engineering assistant embedded into our standard development workflows, capable of handling complex, repetitive tasks. The challenge: The "un-fun" backlog Before piloting the AI assistant, the RTM team was managing maintenance tasks that had persisted across multiple sprints: Security updates: Enhancing automated scanning workflows to maintain a strong security posture Framework modernization: Updating services to align with the team's current application framework Test coverage improvements: Strengthening unit test consistency across services Documentation updates: Refreshing internal service documentation to support smoother onboarding and troubleshooting This maintenance work was important b ## How Big Tech Interview Decisions and Rounds Work DevFeed: [How Big Tech Interview Decisions and Rounds Work]() Original publisher: [Read original article]() Published: 2026-04-21T00:00:00Z Content type: article Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Code](), [Architecture & Design](), [test-coverage](), [Scalability]() Tags: [api-integration](), [bug](), [career](), [clean-code](), [dsa](), [faang](), [interview](), [interviews](), [system-design](), [tech](), [test-coverage]() ### AI overview The article explains how big tech interviews are evaluated. Interviewers assess specific signals independently across coding, system design, and project-focused rounds, then discuss the combined feedback in a panel. Communication, code quality, edge-case handling, problem-solving instincts, scalability, and trade-off analysis can affect the decision, not just whether a problem is solved. ### Source excerpt Having worked at a few big tech companies and having interviewed candidates for them as well, here are some things I wish more candidates knew going in. How decisions are actually made Each interviewer is assigned a round and for every round, there are specific signals they need to collect... ## Bringing Rust to the Pixel Baseband DevFeed: [Bringing Rust to the Pixel Baseband]() Original publisher: [Read original article]() Author: Edward Fernandez (noreply@blogger.com) Published: 2026-04-10T15:12:00Z Content type: article Language: en Sources: [Google Online Security]() Topics: [Rust](), [Memory Safety](), [Security](), [Parsing](), [Google]() Tags: [android](), [android-security](), [dns](), [google](), [internet](), [library](), [memory-safety](), [none](), [open-source](), [parsing](), [pixel](), [remote-code-execution](), [rust](), [security](), [test-coverage](), [vulnerabilities]() ### AI overview Google describes integrating a memory-safe Rust DNS parser into Pixel modem firmware. The post explains the security motivation, the modem's remote attack surface, and the evaluation of open-source Rust DNS libraries, identifying hickory-proto as the best candidate based on maintenance, test coverage, and adoption. ### Source excerpt Posted by Jiacheng Lu, Software Engineer, Google Pixel Team Google is continuously advancing the security of Pixel devices. We have been focusing on hardening the cellular baseband modem against exploitation. Recognizing the risks associated within the complex modem firmware, Pixel 9 shipped with mitigations against a range of memory-safety vulnerabilities. For Pixel 10, Google is advancing its proactive security measures further. Following our previous discussion on "Deploying Rust in Existing Firmware Codebases", this post shares a concrete application: integrating a memory-safe Rust DNS(Domain Name System) parser into the modem firmware. The new Rust-based DNS parser significantly reduces our security risk by mitigating an entire class of vulnerabilities in a risky area, while also laying the foundation for broader adoption of memory-safe code in other areas. Here we share our experience of working on it, and hope it can inspire the use of more memory safe languages in low-level environments. Why Modem Memory Safety Can't Wait In recent years, we have seen increasing interest in the cellular modem from attackers and security researchers. For example, Google's Project Zero gained remote code execution on Pixel modems over the Internet. Pixel modem has tens of Megabytes of executable code. Given the complexity and remote attack surface of the modem, other critical memory safety vulnerabilities may remain in the predominantly memory-unsafe firmware code. Why DNS? The DNS protocol is most commonly known in the context of browsers finding websites. With the evolution of cellular technology, modern cellular communications have migrated to digital data networks; consequently, even basic operations such as call forwarding rely on DNS services. DNS is a complex protocol and requires parsing of untrusted data, which can lead to vulnerabilities, particularly when implemented in a memory-unsafe language (example: CVE-2024-27227). Implementing the DNS parser in Rust offers va ## Bringing Rust to the Pixel Baseband DevFeed: [Bringing Rust to the Pixel Baseband]() Original publisher: [Read original article]() Author: Jiacheng Lu Published: 2026-04-10T09:56:00Z Content type: article Language: en Sources: [Security]() Topics: [Vulnerabilities]() Tags: [android-security](), [devices](), [dns](), [google](), [memory-safety](), [none](), [open-source](), [rust](), [security](), [test-coverage](), [vulnerabilities]() ### AI overview Google describes integrating a memory-safe Rust DNS parser into Pixel 10 modem firmware to reduce risks from memory-safety vulnerabilities. ### Source excerpt Google is continuously advancing the security of Pixel devices. ## The Way of TDD DevFeed: [The Way of TDD]() Original publisher: [Read original article]() Author: Google Testing Bloggers (noreply@blogger.com) Published: 2026-03-10T12:24:00Z Content type: article Language: en Sources: [Google Testing Blog]() Topics: [Test-driven development](), [Test coverage](), [Development](), [Algorithm](), [Code]() Tags: [algorithm](), [article](), [bartosz-papis](), [bugs](), [code](), [development](), [quality](), [refactor](), [tdd](), [test](), [test-coverage](), [tests](), [tott]() ### AI overview This article explains Test-Driven Development as a red-green-refactor cycle: write a failing test, make it pass with minimal production code, and then refactor. It describes reported benefits and limitations, and demonstrates the process by modifying a voting algorithm to support abstentions. ### Source excerpt This article was adapted from a Google Tech on the Toilet (TotT) episode. You can download a printer-friendly version of this TotT episode and post it in your office. By Bartosz Papis Test-Driven Development (TDD) is the practice of working in a structured cycle where writing tests comes before writing production code. The process involves three steps, sometimes called the red-green-refactor cycle: Write a failing test Make the test pass by writing just enough production code Refactor the production code to meet your quality standards Research shows TDD has several benefits: it improves test coverage, reduces the number of bugs, increases confidence, and facilitates code reuse. This practice also helps reduce distractions and keep you in the flow. TDD also has its limitations and is not a silver bullet! See the Wikipedia article about TDD for a detailed explanation and references. Here is a short practical example. Assume you need to modify the following voting algorithm to support the option for voters to abstain: def outcome(ballots): if ballots.count(Vote.FOR) > len(ballots) / 2: return "Approved" return "Rejected" 1. We start by writing a failing test - as expected, the test doesn't even compile: def test_abstain_doesnt_count(self): self.assertEqual(outcome([Vote.FOR, Vote.FOR, Vote.AGAINST, Vote.ABSTAIN]), "Approved") 2. We fix the compilation error by including the missing enum option: class Vote(Enum): FOR = 1 AGAINST = 2 ABSTAIN = 3 Now that the test compiles, we fix the production code to get all tests passing: def outcome(ballots): if ballots.count(Vote.FOR) > (len(ballots) - ballots.count(Vote.ABSTAIN)) / 2: return "Approved" return "Rejected" 3. We now refactor the code to improve clarity, and complete an iteration of the TDD cycle: def outcome(ballots): counts = collections.Counter(ballots) return "Approved" if counts[Vote.FOR] > counts[Vote.AGAINST] else "Rejected" Learn more about TDD in the book Test Driven Development: By Example, by Kent Beck. ## Why the Author Is Skeptical of Test-Driven Development DevFeed: [Why the Author Is Skeptical of Test-Driven Development]() Original publisher: [Read original article]() Author: January Published: 2026-01-29T00:00:00Z Content type: opinion Language: en Sources: [Drew DeVault]() Topics: [Test-driven development](), [Testing](), [test-coverage]() Tags: [software-testing](), [tdd](), [test-coverage](), [testing](), [tests](), [unit-test]() ### AI overview The author argues that test-driven development can provide useful rapid feedback and test coverage, but warns that it may shape code around testability and cannot ensure that tested behavior matches actual software requirements. ### Source excerpt I've gotten a lot of flack throughout my career over my disdain towards test-driven development (TDD). I have met a lot of people who swear by it! And, I have also met a lot of people who insisted that I adopt it, too, often with the implied threat of appealing to my boss if appealing to me didn't work. The basic premise of TDD, for those unaware, is that one first writes a unit test that verifies the expected behavior for some code they want to write, observes the new test fail, and then one writes the implementation, iterating on it until the test passes. The advantage of this approach is, first, to ensure that your codebase is adequately covered by testing, and, second, to provide you a rapid feedback loop to assist in your work. I have often found elements of TDD to be quite useful. Using a unit test or something similar to provide an efficient rapid feedback loop is a technique which I have employed many times. However, I am and have always been skeptical of the cult which arises around automated software testing and in particular TDD. A lot of people adopt an unquestioning loyalty to TDD, building tools and practices and vibes around the idea. It's often too much. The flaw with TDD is that, while it ensures that you have a test for every function you write, it also exerts an influence on the tested codebase, shaping the code to be as "testable" as possible, which only sometimes leads to better code. Moreover, TDD has no means of ensuring that the behavior that your tests verify is the right behavior for your software to have. Software with a thousand passing tests and 100% test coverage could be doing whatever the user or the business or whatever needs it to, but it could just as easily not meet the requirements in spite of those comprehensive tests - and in any case it gives you confidence in your work, which may or may not be misplaced. The cult of TDD exploits the fact that TDD is very good at making you feel like a good, diligent programmer. That rapid fee ## Mutation testing at Mercado Libre: From pilot to everyday guardrail DevFeed: [Mutation testing at Mercado Libre: From pilot to everyday guardrail]() Original publisher: [Read original article]() Author: Luram Archanjo Published: 2026-01-14T12:19:03Z Content type: article Language: en Sources: [Mercado Libre Tech]() Topics: [mutation-testing](), [Testing](), [GitHub](), [Pull Request](), [test-coverage]() Tags: [ci-cd-pipeline](), [code-quality](), [github](), [github-action](), [mutation-testing](), [pull-requests](), [software-engineering](), [software-quality-testing](), [test-coverage](), [testing]() ### AI overview Mercado Envíos integrated mutation testing into Mercado Libre's Fury pipelines using a multi-stack GitHub Action, delta-first execution focused on modified code, and gradual team-level gates. The article explains how this approach supplements coverage by identifying weak test assertions and aims to improve pull request quality and reduce regressions. ### Source excerpt Beyond coverage: How we integrated mutation testing into Fury pipelines and turned it into an actionable quality indicator for teams At Mercado Envíos (ME), we saw that high test coverage didn't always prevent regressions -- pull requests (PRs) were green, but we still broke production. In this article, we share how we brought mutation testing into our pipelines on Fury, our internal developer platform (IDP). We used a single multi-stack GitHub Action, a delta-first approach, and gradual team-level gates. We walk through the design decisions, challenges, and lessons learned that helped us turn this technique into an everyday guardrail that improves test effectiveness and PR quality. Introduction: Green PR, still an incident Friday, end of the day. You've got a green PR, pipelines are passing, and coverage looks great. You merge with confidence. On Monday, production goes down because of an edge case no one asserted. Sounds familiar? Same here. Coverage tells us what runs, not whether tests fail where they should. You can have 95-100% line coverage and still let a subtle regression slip -- a slightly wrong if condition, an unexpected default value, a branch with no assertions. That's where mutation testing comes in. Instead of assuming tests are good just because they pass, you intentionally introduce small code changes (mutants) and check whether the test suite kills them. In mutation testing, "killing a mutant" means your test suite fails when run on the mutated code, proving it can detect the injected defect. If mutants survive, you get a concrete indicator of weak assertions exactly in the area you're changing. At Mercado Envíos (ME), we moved from a one-off experiment to making mutation testing part of teams' daily flow. We integrated it into our standard pipelines on Fury, Mercado Libre's internal developer platform. We did it without breaking delivery: a single, multi-stack GitHub Action; delta-first execution focused on modified code; and team-level gates with ## How The New York Times is scaling Unit Test Coverage using AI Tools DevFeed: [How The New York Times is scaling Unit Test Coverage using AI Tools]() Original publisher: [Read original article]() Author: The NYT Open Team Published: 2026-01-13T18:51:55Z Content type: article Language: en Sources: [New York Times]() Topics: [Artificial Intelligence](), [test-coverage](), [unit test](), [Testing](), [Development](), [Web app](), [React]() Tags: [ai](), [ai-tools](), [code](), [coverage](), [development](), [reliability](), [scaling](), [software](), [test-coverage](), [testing](), [unit-test](), [unit-testing]() ### AI overview The New York Times describes using AI tools to expand unit-test coverage across its News site. The agents improved efficiency and coverage on critical code, but required strict human supervision and human review of the generated tests. ### Source excerpt How AI tools are helping our software engineers write better tests at scaleIllustration by Nick Little By Eric Chima and Leonardo Quixadá At The New York Times, we're all excited to build fresh new experiences that delight our users. Our product managers are driven to find new ways to get our work in front of our audience and build reader engagement. Our engineers are motivated to solve unique technical challenges. And just when you think all that work is on track, breaking news strikes and all of our plans change at once. With all that going on, who could blame us if our test coverage couldn't quite keep up? Like every engineering organization, The Times deals with routine maintenance tasks: updating dependencies, cleaning up old code, maintaining accessibility standards, and, yes, building testing into all of our products. Our engineers are committed to quality, but when you work at the speed of news, there's always a new issue that needs to be addressed. Fortunately, generative AI has arrived with the promise of tidying up after us, taking care of the busy work, and giving time back to our developers to focus on feature development. But how far can you trust it? Recently, one of our platform teams used AI tools to build out unit tests across our flagship product, the News site. This was an opportunity for our testing to catch up with our rapid development. Our goal was to improve the reliability of our web app, but also to evaluate AI products and determine how far we could push them to do work in bulk across our codebase. As expected, the agents required strict human supervision, but they improved our efficiency in writing tests and let us quickly expand coverage on some of our most critical code. AI accelerates, but humans test the tests Unit tests are a crucial part of the development process. The idea is to divide application code into small pieces and write tests for each, creating guardrails to ensure that changing one piece of the site doesn't impact the r ## Automating Exhaustive Branch Coverage for Sealed Types in Kotlin DevFeed: [Automating Exhaustive Branch Coverage for Sealed Types in Kotlin]() Original publisher: [Read original article]() Author: Sean Amos Published: 2025-11-20T16:33:33Z Content type: tutorial Language: en Sources: [Wealthfront]() Topics: [Kotlin](), [Testing](), [Unit testing](), [test-coverage](), [Code quality]() Tags: [android](), [automation](), [code](), [code-quality](), [exhaustive](), [kotlin](), [sealed](), [sealed-classes](), [subtype](), [test-coverage](), [testing](), [unit-testing](), [wealthfront-engineering]() ### AI overview This article explains how to build an Exhaustive Test Runner for Kotlin sealed classes and interfaces. The approach ensures that functions accepting sealed parameters have at least one unit test for each subtype, including future subtypes added to the hierarchy. ### Source excerpt We take code correctness and code quality seriously at Wealthfront. One of the most effective ways we ensure that our code is correct is through rigorous unit testing and strong typing. In Kotlin, sealed classes and sealed interfaces offer a powerful tool to model restricted class hierarchies. They provide strong guarantees about the types in... Read more ## Using AI Coding Tools to Accelerate Test Coverage DevFeed: [Using AI Coding Tools to Accelerate Test Coverage]() Original publisher: [Read original article]() Published: 2025-11-01T07:00:00Z Content type: opinion Language: en Sources: [Mary Rose Cook]() Topics: [Testing](), [Test coverage](), [codex](), [Claude Code](), [Claude]() Tags: [claude-code](), [codex](), [prompt](), [review](), [test-coverage](), [testing]() ### AI overview A Notion engineer reflects on using Claude Code guidance and Codex to speed up test writing during a time-limited test-coverage sprint. The author describes prompting Codex to prioritize important or complex functions and to carry a task from a function name through creating a pull request. ### Source excerpt At Notion, we've been doing a quality sprint to increase our test coverage. Friday was the last day, and I wanted to get some more tests written. Time was short, which forced me to break my usual workflow. Not that I really have a usual workflow, these days. New AI-augmented programming tools and techniques come out every day. Everything is changing so fast that you can frequently become 10% more productive, forever, with a few minutes or a few hours invested. So, I push myself to try new things. But, inevitably, workflows are sticky. It's hard to change a habit. Extra cognitive load to monitor and refine the technique. Extra willpower to overcome the inertia of the familiar. Extra gumption to risk wasted time on something that isn't helpful. So I have my mega list of stuff to try and every few days I'll pluck something off it to try. But Friday was eye-opening. With time short, I wanted to get as much done as possible. And failure would mean only a few hours lost. We already had a bunch in place to speed things up. A Claude Code skill that my colleague, Jimmy, wrote. It laid out a careful, thorough process for writing tests. It included looking at our testing guide, tips on what to mock and an entreaty to look at surrounding test coverage. I pointed Codex at the Notion doc listing functions that needed coverage. I told it to find functions that are core parts of the system, or that have complex logic. This way, we could prioritize our time towards testing code that was important or gnarly. And, on Friday, here's the new stuff I tried - I'm rushing. Jimmy's skill is written for Claude, but I use Codex. What if I just point Codex at the skill directory in Claude's config? I'm rushing, so I have to get out of the loop. I need a process that can autonomously go from function name to PR. So I wrote a prompt with these steps: read the guidance on writing tests, write tests, create a branch, commit, review the code, refine the code, put up a PR. I'm rushing, so it's going ## Google Summer of Code 2025 Results DevFeed: [Google Summer of Code 2025 Results]() Original publisher: [Read original article]() Author: Thomas Dinges Published: 2025-10-21T09:37:52Z Content type: article Language: en Sources: [Blender]() Topics: [Google](), [Code](), [Testing](), [test-coverage](), [cpu](), [GPU]() Tags: [2025](), [code](), [cpu](), [general-development](), [google](), [gpu](), [gsoc](), [technical](), [test-coverage](), [testing]() ### AI overview Blender's Google Summer of Code 2025 results article reviews work by eight contributors on the compositor, Video Sequence editor, mesh editing, sculpting, regression test coverage, and the Flamenco render manager. It notes that most changes had already been merged and were planned for Blender 5.0. ### Source excerpt See the outcome of the eight projects to be part of Google Summer of Code 2025. ## Keycloak JS 26.2.1 released DevFeed: [Keycloak JS 26.2.1 released]() Original publisher: [Read original article]() Author: Keycloak Team Published: 2025-10-09T00:00:00Z Content type: release Language: en Sources: [Keycloak Blog]() Topics: [Keycloak](), [releases](), [JavaScript](), [test-coverage](), [migration]() Tags: [bugfixes](), [idm](), [javascript](), [kerberos](), [keycloak](), [keycloak-js-release](), [ldap](), [migration-guide](), [openid-connect](), [release](), [saml](), [sso](), [test-coverage]() ### AI overview Keycloak JS 26.2.1 is a patch release following its separation from the main project release cycle. It includes an internal refactor, modern JavaScript language features, a new test suite with more comprehensive test coverage, bug fixes, and small enhancements, with no expected breaking changes for users. ### Source excerpt Highlights This release of Keycloak JS is the first release after our initial announcement to split if off from the main project release cycle. This release is the result of a large internal refactor to make the code more maintainable and make use of modern JavaScript language features, as well as to introduce a new test suite with more comprehensive test coverage. Even though much has changed under the hood, this is a patch releases, and there should be no breaking changes for users, only bugfixes and small enhancements. Upgrading Before upgrading refer to the migration guide for a complete list of changes. [Next page]()