# Coverage Published articles for 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. ## Rider and ReSharper 2026.2.2 Are Out! DevFeed: [Rider and ReSharper 2026.2.2 Are Out!]() Original publisher: [Read original article]() Author: Alexander Kurakin Published: 2026-09-16T14:12:44Z Content type: release Language: en Sources: [The JetBrains Blog]() Topics: [rider](), [resharper](), [AI-assisted coding](), [test-coverage]() Tags: [2026](), [ai-agent-hooks](), [ai-agents](), [code-coverage](), [coverage](), [hooks](), [jetbrains](), [macos](), [mcp](), [net](), [net-tools](), [releases](), [resharper](), [rider](), [update]() ### AI overview JetBrains releases Rider, ReSharper, and .NET tools 2026.2.2, adding an AI Agent Setup widget in Rider, more targeted hook feedback for coding agents, TUnit coverage support, and fixes including a macOS freezing issue. ### Source excerpt We've released version 2026.2.2 of ReSharper, Rider, and .NET tools. You can install this update from inside the tools themselves, through the Toolbox App, or on our website. Here's what's new in this update. Rider 2026.2.2 AI Agent Setup widget We've recently added a wide range of features to help you work with coding agents [...] ## Upgrading Rails 4.2 to Rails 8: Count Sites, Not Warnings DevFeed: [Upgrading Rails 4.2 to Rails 8: Count Sites, Not Warnings]() Original publisher: [Read original article]() Published: 2026-08-08T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Rails](), [upgrade](), [version](), [Test coverage](), [RSpec](), [Docker Compose](), [Ruby]() Tags: [coverage](), [docker-compose](), [rails](), [rspec](), [ruby](), [upgrade](), [version]() ### AI overview An account of upgrading a multi-tenant JSON API from Rails 4.2.7.1 to Rails 8. The author argues that estimates focused on version gates missed the actual risks, and describes how characterization tests, an extracted frontend, and containerized development reduced the work. ### Source excerpt . [Upgrading Rails 4... ## Migrating a Large Flow Monorepo to TypeScript DevFeed: [Migrating a Large Flow Monorepo to TypeScript]() Original publisher: [Read original article]() Author: Shawn Walton, Software Engineer Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [monorepo](), [TypeScript](), [migration](), [repo](), [Code]() Tags: [coverage](), [integrity](), [migration](), [monorepo](), [open-source](), [rewrite](), [typescript]() ### AI overview Yelp describes its gradual migration of a large Flow monorepo to TypeScript. The team chose a package-by-package approach to preserve type-checking trust and type safety while Flow and TypeScript coexisted in production, using open-source conversion tooling, custom codemods, and manual fixes. ### Source excerpt In early 2017, Webcore selected Flow as Yelp's next-generation typechecker over TypeScript. At the time there was no clear frontrunner. Flow had better support for React, better performance, and a respectable repository of types for NPM packages in flow-typed. However, a few years later the landscape had shifted, and it became clear that TypeScript was emerging as the stronger choice. TypeScript's DefinitelyTyped repo had thousands more packages than flow-typed, and many more packages shipped with built-in TypeScript headers. TypeScript also proved to be more stable over time, and limited uptake in the wider JS community made finding tools and documentation... ## Code Smell 320 - Vanity Coverage DevFeed: [Code Smell 320 - Vanity Coverage]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-06-23T23:32:02Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [Testing](), [mutation-testing](), [Test coverage]() Tags: [coverage](), [mutation-testing](), [tests]() ### AI overview The article describes vanity coverage: tests that execute code without verifying meaningful behavior, which can create false confidence and allow defects to reach production. It recommends behavior-focused assertions and mutation testing to identify ineffective tests. ### Source excerpt TL;DR: You write tests that touch every line but verify nothing, creating false confidence in a broken system. Problems 😔 False confidence Hidden production defects Misleading metrics Wasted tes ## How Monk migrated 100+ workflows from Inngest to Temporal DevFeed: [How Monk migrated 100+ workflows from Inngest to Temporal]() Original publisher: [Read original article]() Author: Francesco Coacci Published: 2026-06-16T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [migration](), [Orchestration](), [business logic](), [test-coverage]() Tags: [async](), [automation](), [business-logic](), [community](), [coverage](), [migration](), [orchestration](), [pull-request](), [workflows]() ### AI overview Monk describes migrating more than 100 live workflows from Inngest to Temporal incrementally, using reversible pull requests, parallel runtimes, feature-flagged cutovers, and no migration freeze. The article explains how this approach kept each workflow coherent and recoverable during the transition. ### Source excerpt Monk migrated 100+ live workflows from Inngest to Temporal with one reversible PR at a time all while avoiding freezes and rewrites. Here's how. ## One Decade Later: Revisiting Five Front-End Kung Fu Moves DevFeed: [One Decade Later: Revisiting Five Front-End Kung Fu Moves]() Original publisher: [Read original article]() Published: 2026-04-30T23:26:34Z Content type: opinion Language: en Sources: [Ariya Hidayat]() Topics: [Front end](), [Web Development](), [JavaScript](), [Testing](), [test-coverage](), [ci](), [Instrumentation](), [ESLint](), [Git](), [web-standards]() Tags: [browser](), [code-coverage](), [continuous-integration](), [coverage](), [engineering](), [eslint](), [firefox](), [front-end](), [git-hooks](), [javascript](), [testing](), [web-standards]() ### AI overview A retrospective revisits front-end engineering techniques presented at a JavaScript meetup ten years earlier. It discusses git hooks, code complexity, code coverage, and cross-browser testing, explaining how common tools and modern workflows have changed their practical use. ### Source excerpt Exactly ten years ago, I gave a talk at the SF JavaScript meetup titled 5 Kung Fu Moves for Front-End Heroes. A full decade later, I found myself wondering: are these techniques still the secret sauce for modern front-end engineering, or have they been rendered obsolete by time? ## Introducing the Temporal Developer Skill DevFeed: [Introducing the Temporal Developer Skill]() Original publisher: [Read original article]() Author: Ethan Ruhe Published: 2026-03-20T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [AI-assisted coding](), [cursor](), [SDKs](), [Error Handling](), [retry](), [Testing](), [configuration]() Tags: [agent](), [ai-coding-agents](), [cli](), [configuration](), [coverage](), [developer](), [error-handling](), [product-news](), [retry](), [sdk]() ### AI overview Temporal announces the Developer Skill in public preview, providing coding agents with on-demand guidance on Temporal's programming model, SDK best practices, workflows, retries, error handling, testing, and worker configuration. ### Source excerpt Supercharge your AI coding agent with the Temporal Developer Skill. Get expert-level guidance on Workflow determinism, retry policies, and SDK best practices in real-time. ## White Paper on Data Science Technical Program Management DevFeed: [White Paper on Data Science Technical Program Management]() Original publisher: [Read original article]() Author: Sonu Jain Published: 2026-02-27T12:41:46Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Data Science](), [Model Development](), [Development](), [Software Engineering]() Tags: [collaboration](), [coverage](), [data-science](), [experimental](), [leadership](), [machine-learning](), [management](), [paper](), [retail](), [software-engineering](), [technical](), [technical-program-manager](), [training](), [validation](), [white-paper]() ### AI overview This white paper presents a structured approach to managing Data Science programs through technical program management. It discusses business alignment, cross-functional collaboration, data validation, model training and retraining, governance, and phased execution, using an inventory forecasting initiative as a real-world example. ### Source excerpt 1. Abstract Managing Data Science programs requires a structured approach to handle the complexities of data, model development, and business alignment. This whitepaper provides a comprehensive guide on the effective program management of Data Science programs by technical program managers. It highlights the critical role of Technical Program Managers (TPMs) in driving successful execution and outlines the key phases, challenges, and recommended best practices at every stage for effectively managing Data Science programs This white paper is grounded in a real-world inventory forecasting initiative aimed at improving stock availability and reducing overstock across multiple retail categories. The program involved cross-functional collaboration between Data Science, Engineering, Product, and Business teams to build predictive models that could dynamically adjust inventory levels based on demand signals. 2. Introduction Data Science has become a critical pillar of decision-making across industries, but organizations continue to struggle with operationalizing these initiatives. Unlike software development, which follows predictable sprint cycles, Data Science programs are inherently experimental -- requiring repeated cycles of data validation, model training, and retraining before they reach acceptable performance levels. This uncertainty often leads to misaligned expectations, delays in delivery, and inconsistent business impact. The iterative nature of model development makes predictability especially challenging: teams may require multiple iterations to achieve coverage and accuracy thresholds that satisfy business needs. Without structured program management, these efforts risk becoming siloed experiments rather than scalable, value-generating solutions. This whitepaper aims to address this gap by providing a practical framework for Technical Program Managers (TPMs) to manage Data Science programs effectively. It draws on real-world experience from a large-scale inve ## 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 ## How Mocking Affects Software Design and Testability DevFeed: [How Mocking Affects Software Design and Testability]() Original publisher: [Read original article]() Author: Denis Čahuk Published: 2025-07-19T07:45:14Z Content type: tutorial Language: en Sources: [Crafting Tech Teams]() Topics: [Mocking](), [Testing](), [Test-driven development](), [Refactoring](), [test-coverage]() Tags: [coverage](), [mocking](), [mongodb](), [refactoring](), [tdd](), [testing]() ### AI overview A recorded talk examines when mocks are useful, common misconceptions about mocking and TDD, and how excessive or poorly chosen mocks can constrain software design and testability. It discusses multi-mock tests, database mocking, and designing narrow seams around dependencies. ### Source excerpt If you've ever felt like your tests get in the way of refactoring, or like mocks make everything harder rather than simpler this stream is for you. ## Why clear, readable code is harder to write than clever code DevFeed: [Why clear, readable code is harder to write than clever code]() Original publisher: [Read original article]() Author: Engineer's Codex Published: 2023-11-20T09:58:13Z Content type: opinion Language: en Sources: [Engineer's Codex]() Topics: [Code](), [Software Engineering](), [Code review](), [Refactoring](), [C++](), [unit test]() Tags: [c-plus-plus](), [code](), [code-review](), [coverage](), [refactoring](), [software]() ### AI overview This opinion article argues that clever code, including code-golfing-style one-liners, is difficult to understand and debug. It contrasts that with clear, readable code and describes using smaller diffs, helper functions, refactoring, and reasonable unit test coverage to make a C++ change easier to review. ### Source excerpt And clear, readable code is probably the hardest code to write ## Value Statements for My New Company DevFeed: [Value Statements for My New Company]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2022-07-15T07:39:00Z Content type: opinion Language: en Sources: [Scott Johnson]() Topics: [Software](), [test-coverage](), [cloud-infrastructure](), [Support](), [unit tests](), [version-control]() Tags: [coverage](), [culture](), [distributed](), [ethics](), [github](), [software](), [startup](), [support](), [test-coverage](), [unit-tests](), [version-control](), [work-from-home]() ### AI overview The author presents draft values for a new company, emphasizing ethical behavior, written documentation, respectful communication, commitment-keeping, transparency, work-life balance, technical support, software quality, version control, and test coverage. ### Source excerpt Welp, it looks like I'm starting a new company after all. Here are the values that I drafted for it. Values These things we believe: Ethics. We will behave ethically in all situations. Writing Things Down. We will work from home and be a distributed culture. And in a distributed culture, things have to be written down. Good writing is a skill for everyone not something reserved for writing. Having the Hard Conversations Respectfully. In any culture there will be conflict. This can be done respectfully and without tripping anyone's bozo bit. Do What We Say. We will keep our commitments and do the things we say. If we fail - and that's expected - then we will apologize and accept responsibility. Openness and Transparency. Shining light on things makes the badness scurry away. That's the power of openness and transparency. Revenue Disclosure. Every organization is, correctly, driven by its revenue sources - and that's ok. Problems occur, however, when you fail to disclose your revenue sources. Ethical Capitalism. Capitalism has been the single most transforming force in history and it has done more to lift people from poverty than anything else. Now, that said, capitalism without regulation and restraint is a violent beast. The capitalism I believe in was taught to me by my grandfather and it could be summarized as "do the right thing; don't chase every dollar; pay it forward; treat everyone with kindness". Code of Conduct. We will treat each other with mutual respect. We have adoped the Github code of conduct it appends to every repo. Work Should Be Secondary to Life. This is a business but business was not supposed to take over our lives. All staff are encouraged to take acknowledge this and, hopefully, take four day work weeks as often as they like. World Class Technical Support. The high tech industry can - and should - offer world class support. Great support is possible when you have high gross margins - which is all of the technology business - although most peo ## PDFx update and new version release (v1.4.1) DevFeed: [PDFx update and new version release (v1.4.1)]() Original publisher: [Read original article]() Author: Chris Hager Published: 2021-04-12T00:00:00Z Content type: release Language: en Sources: [Chris Hager]() Topics: [pdf](), [Python](), [Command-line interface](), [PyPI](), [Pytest](), [GitHub]() Tags: [cli](), [code](), [contributors](), [coverage](), [github](), [markdown](), [pdf](), [pdfx](), [pypi](), [python](), [release](), [tests](), [update](), [version]() ### AI overview PDFx, a Python tool and library for extracting text, links, references, and metadata from PDF files and URLs, was updated and released as version 1.4.1. The update includes broader Python compatibility, a switch to pdfminer.six, linting and static checks, tests, formatting, GitHub workflows, improved PDF URL detection, and inclusion of tests in the PyPI tarball. ### Source excerpt PDFx is a tool to extract text, links, references and metadata from PDF files and URLs. Thanks to several contributors the project received a thorough update and was brought into 2021. The new release of today is PDFx v1.4.1 🎉 PDFx works like this: ## Searching for RH Counterexamples -- Productionizing DevFeed: [Searching for RH Counterexamples -- Productionizing]() Original publisher: [Read original article]() Published: 2021-03-06T10:08:59Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [CI/CD](), [CircleCI](), [Testing](), [GitHub](), [Docker](), [Containers](), [scaling]() Tags: [ci](), [circleci](), [containers](), [coverage](), [error-handling](), [github]() ### AI overview A tutorial on productionizing an application that searches for counterexamples to the Riemann Hypothesis. It covers automated tests for pull requests, error handling, cleanup after failed worker jobs, type-hint and test-coverage checks, static analysis, failure alerting, and automated updates, using CircleCI and containers. ### Source excerpt We're ironically searching for counterexamples to the Riemann Hypothesis. Setting up Pytest Adding a Database Search Strategies Unbounded integers Deploying with Docker Performance Profiling Scaling up In the last article we rearchitected the application so that we could run as many search instances as we want in parallel, and speed up the application by throwing more compute resources at the problem. This is good, but comes with a cost. The complexity of this new architecture requires us to manage many different containers and AWS instances. ## Fuzzing | Teleport OSS | Ada Logics DevFeed: [Fuzzing | Teleport OSS | Ada Logics]() Original publisher: [Read original article]() Author: info@goteleport.com (Adam Korczynski, Andrew Lytvynov) Published: 2020-12-20T00:00:00Z Content type: article Language: en Sources: [Teleport]() Topics: [Fuzzing/Fuzz testing](), [Go Language](), [Security](), [Development](), [Open Source](), [Test coverage](), [Google](), [test](), [C](), [C++]() Tags: [bugs](), [c](), [c-plus-plus](), [code](), [coverage](), [crash](), [development](), [examples](), [fuzzing](), [go](), [google](), [harness](), [oss](), [quality](), [security](), [test]() ### AI overview This article explains how Teleport integrated continuous fuzzing with help from Ada Logics. It describes building initial fuzzers, integrating Teleport with Google's OSS-Fuzz, and using go-fuzz to generate randomized inputs and explore code through coverage guidance. It also discusses crash-related bugs that fuzzing can find in Go projects. ### Source excerpt A fuzzer is a program that passes randomized data to an entrypoint of a target application. The goal is to stress test the target application. ## Treat Testing Pain with a Healthy Dose of Test Strategy DevFeed: [Treat Testing Pain with a Healthy Dose of Test Strategy]() Original publisher: [Read original article]() Author: Jeff Benton Published: 2019-10-22T16:12:57Z Content type: tutorial Language: en Sources: [RetailMeNot]() Topics: [Testing](), [Unit testing](), [test-coverage]() Tags: [coverage](), [diagnose](), [flaky](), [integration](), [test-coverage](), [testing](), [unit-testing]() ### AI overview The article presents test strategy as a low-cost way for engineering teams to diagnose testing problems and create a treatment plan. It discusses missing unit or integration tests, flaky or slow tests, maintainability issues, and test coverage pursued without clear value. ### Source excerpt We live in a day and age where consumers cannot access medication for their ailment because of cost. We are aware of the anecdotes related to delaying treatment. In the same way, some engineering teams are stuck with projects which are in an unhealthy testing state. The team goes on sprint after sprint with the situation getting worse, not better. In this article, I will introduce a low-cost tool to enable you to diagnose your situation and put together a treatment plan. Testing pain It's a sad situation, but I think we've all been in the place where testing on our project is a pain. "belchonock" © 123RF.com It's a project where no one took the time to set up unit testing. Or the unit test framework exists and no one bothered to add unit tests. There are unit tests, but no integration tests. Or there is no support for the types of integration tests you want to write. The tests are flaky. They take too long. The tests are difficult to maintain or understand. The tests don't provide value and were added to hit a random test coverage number. After all the automated tests pass, you still have this nagging doubt about releasing the latest version to the public. The pain points I have called out above are not exhaustive. We can relate to these pain points, but why don't we fix them? We know ignoring a medical problem is not a good idea. It's not a good idea to ignore your testing pain. But choosing to tackle your testing problems can seem like a daunting task. A tool that I have found helpful to start dealing with my testing pain is to develop a test strategy. "Dejan Bozic" © 123RF.com Develop your Test Strategy A strategy is "A plan of action or policy designed to achieve a long-term or overall aim." (Definition of strategy in English. https://www.lexico.com/en/definition/strategy) A test strategy is a plan of action or policy designed to achieve a long-term testing aim. To develop a test strategy, start answering the following questions. What are the testing pain points ## A gentle introduction to Linux Kernel fuzzing DevFeed: [A gentle introduction to Linux Kernel fuzzing]() Original publisher: [Read original article]() Author: Marek Published: 2019-07-09T22:00:00Z Content type: tutorial Language: en Sources: [Marek Majkowski]() Topics: [Fuzzing/Fuzz testing](), [Linux Kernel](), [Kernel](), [Testing](), [networking](), [virtualization]() Tags: [c](), [c-plus-plus](), [coverage](), [fuzzing](), [kernel](), [kvm](), [linux](), [linux-kernel](), [security](), [testing]() ### AI overview A tutorial on coverage-guided fuzzing of the Linux Kernel's netlink machinery. It explains how to use AFL with a custom kernel and a netlink shim inside KVM virtualization to explore kernel internals and find bugs. ### Source excerpt A gentle introduction to Linux Kernel fuzzing This article was first published on Cloudflare blog: For some time I've wanted to play with coverage-guided fuzzing. Fuzzing is a powerful testing technique where an automated program feeds semi-random inputs to a tested program. The intention is to find such inputs that trigger bugs. Fuzzing is especially useful in finding memory corruption bugs in C or C++ programs. ## The 100% code coverage problem DevFeed: [The 100% code coverage problem]() Original publisher: [Read original article]() Author: info@jeroenmols.com (Jeroen Mols) Published: 2017-11-28T00:00:00Z Content type: opinion Language: en Sources: [Jeroen Mols]() Topics: [test-coverage](), [Code](), [test]() Tags: [android](), [blogs](), [code-coverage](), [coverage](), [quality](), [testing](), [tests]() ### AI overview The article explains why 100% code coverage is not a reliable quality measure or goal. Coverage records executed lines, but does not show whether tests contain meaningful assertions, cover all functionality, or verify correct interactions between components. ### Source excerpt While you may be tempted to strive for 100% code coverage, that would be a horrible idea. Besides some code being hard to test, the concept of coverage is actually fundamentally limited. ## Organizing Threat Modeling Magic DevFeed: [Organizing Threat Modeling Magic]() Original publisher: [Read original article]() Author: Jonathan Marcil Published: 2017-08-29T00:00:00Z Content type: article Language: en Sources: [Shostack & Friends Blog]() Topics: [Vulnerabilities]() Tags: [application](), [coverage](), [magic]() ### AI overview The article presents a personal approach to threat modeling that represents system threats as attack trees. It describes weighting branches to estimate effort, aggregate sub-goal and root-goal priorities, and choose which technical efforts to pursue based on attacker motivations and tradeoffs. ### Source excerpt [no description provided] ## The value of a thousand little features DevFeed: [The value of a thousand little features]() Original publisher: [Read original article]() Published: 2017-04-13T00:00:00Z Content type: opinion Language: en Sources: [Robin Ward]() Topics: [Software Engineering](), [Development](), [Rails](), [Ember](), [JavaScript](), [Ruby](), [Database](), [code reviews](), [test-coverage]() Tags: [code-reviews](), [coverage](), [database](), [development](), [ember](), [javascript](), [rails](), [ruby](), [software-engineering]() ### AI overview The author reflects on five years of Discourse development, describing how the team settled on major technical choices such as Rails, ActiveRecord, Postgres, Ember.js, and JavaScript. The article also discusses codebase practices, performance improvements, and planned changes including TypeScript, replacing Sprockets, and upgrading Ruby and Rails. ### Source excerpt It's been over a year since I wrote a blog entry! And while of course the universal excuse of "I've been busy" applies, I think we reached a point in Discourse's development where we just were able to focus on the product without a lot of stuff getting in our way. I've now been working on Discourse full time for 5 years. In the beginning we had a lot more uncertainty about technical decisions. Some of the things we debated interally include: ## Measuring production code coverage with JaCoCo DevFeed: [Measuring production code coverage with JaCoCo]() Original publisher: [Read original article]() Author: Carlos Alexandro Becker Published: 2017-03-20T00:00:00Z Content type: tutorial Language: en Sources: [Carlos Becker]() Topics: [test-coverage](), [Code](), [Java](), [Puppet](), [Maven]() Tags: [code-coverage](), [coverage](), [jacoco](), [java](), [maven](), [monolith](), [production](), [puppet]() ### AI overview This tutorial explains how to use JaCoCo in a Java production environment to generate production code coverage reports. It describes instrumenting a production application server, collecting binary report files, and compiling them into HTML reports to identify code that is not executed. ### Source excerpt Microservices is the new fancy way of doing applications. Yet, most companies still have big and old monoliths in production. In fast evolving software of this size, it's usual to have lines of code which are never executed in production. Production code coverage reports can help us find those lines. ## Set up Travis CI and code coverage for open-source Android projects DevFeed: [Set up Travis CI and code coverage for open-source Android projects]() Original publisher: [Read original article]() Author: info@jeroenmols.com (Jeroen Mols) Published: 2015-11-13T00:00:00Z Content type: tutorial Language: en Sources: [Jeroen Mols]() Topics: [ci](), [test-coverage](), [Travis CI](), [GitHub](), [unit tests](), [Android]() Tags: [also](), [android](), [blogs](), [ci](), [code-coverage](), [continuous-integration](), [coverage](), [github](), [tools](), [travis-ci](), [unit-tests]() ### AI overview This tutorial explains how to configure Travis CI to build open-source Android projects, run unit tests, generate code coverage reports, and integrate with GitHub pull requests. ### Source excerpt Wouldn't you love to have your open source projects built automatically by a continuous integration server? And to have a detailed code coverage report for all your unit tests? ## Integrating Minitest with Shippable DevFeed: [Integrating Minitest with Shippable]() Original publisher: [Read original article]() Author: Carlos Alexandro Becker Published: 2015-02-04T00:00:00Z Content type: tutorial Language: en Sources: [Carlos Becker]() Topics: [Testing](), [CI/CD](), [Ruby]() Tags: [continuous-integration](), [coverage](), [guide](), [how-to](), [reports](), [ruby](), [rvm](), [setup](), [testing]() ### AI overview A practical guide to integrating Minitest with Shippable, including configuration for test and coverage reports and a workaround for a Ruby compatibility issue involving RVM. ### Source excerpt I know, everyone uses Travis. I have nothing against it. But in case you want to test and/or use Shippable, this might be just the guide for you. I will also show how to setup those nice tabs with the test and coverage reports. ## When Greedy Algorithms are Good Enough: Submodularity and the (1--1/e)-Approximation DevFeed: [When Greedy Algorithms are Good Enough: Submodularity and the (1--1/e)-Approximation]() Original publisher: [Read original article]() Published: 2014-07-07T10:00:01Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithms, Complexity](), [Algorithms](), [Computer science](), [Mathematics]() Tags: [algorithm](), [algorithms](), [approximation](), [approximation-algorithms](), [computer-science](), [coverage](), [greedy](), [greedy-algorithm](), [matroids](), [optimization](), [submodularity](), [theory]() ### AI overview This article explains when greedy algorithms can produce optimal or near-optimal solutions. It introduces submodularity as a diminishing-returns property and discusses how it can provide a reasonably good approximation to the optimal solution. ### Source excerpt Greedy algorithms are among the simplest and most intuitive algorithms known to humans. Their name essentially gives their description: do the thing that looks best right now, and repeat until nothing looks good anymore or you're forced to stop. Some of the best situations in computer science are also when greedy algorithms are optimal or near-optimal. There is a beautiful theory of this situation, known as the theory of matroids. We haven't covered matroids on this blog (edit: we did), but in this post we will focus on the next best thing: when the greedy algorithm guarantees a reasonably good approximation to the optimal solution. [Next page]()