This guide explains how to set up Mocha for TypeScript unit testing, configure NYC for coverage reporting, and integrate both tools within a Lerna monorepo to maintain high branch coverage across multiple packages.
This article explores the challenges of iOS code coverage, explains the generation of .gcno and .gcda files using LLVM, and presents a transparent, Git‑aware incremental coverage solution that integrates into the development workflow with minimal overhead.
The article presents a comprehensive approach to improve backend service testing by building a mapping between automated test cases and code coverage, enabling selective test execution, objective impact assessment, and incremental coverage analysis to enhance test efficiency, reliability, and CI stability.
This article explains how Lighthouse can measure JavaScript execution time, reveal unused code through coverage, and offers practical steps—such as sending only needed scripts, minifying, and removing dead code—to reduce JavaScript overhead and improve user experience on web pages.
This article examines common software testing antipatterns, explains why testing the wrong functionality and over‑focusing on code coverage are misguided, introduces a mental model for prioritizing critical, core, and other code, and presents practical metrics and guidelines for effective, behavior‑driven testing.
The article discusses challenges of regression testing and proposes an incremental code‑coverage solution using a diff engine, AST analysis, and JaCoCo integration within a Jenkins CI pipeline, enabling precise test scope identification, visual coverage feedback, and improved collaboration between manual and automated testing.
This article expands on common software testing antipatterns, explaining why testing the wrong functionality, focusing on internal implementation, and obsessing over code‑coverage percentages can waste effort, and it introduces a mental model, practical metrics, and guidelines for writing effective, business‑oriented tests.
This article explains the concept and advantages of continuous integration, outlines a typical CI workflow including submission, testing, building, deployment and rollback, discusses challenges in test case management, reviews popular CI tools such as Jenkins and Qone, and details code‑coverage measurement using Jacoco with Maven configuration.
The article discusses the challenges of locating dead or low‑utilization code in rapidly evolving mobile applications and presents a practical framework that combines Xcode code coverage with an automated UI testing platform and a gateway layer to achieve accurate online coverage without invasive instrumentation.
This article explains why unit testing is essential for PHP projects, introduces SOLID design principles, shows how to choose and install PHPSpec, demonstrates writing specifications, mocking, argument matching, exception testing, and configuring code‑coverage reports, all with concrete code examples.
This article explains the design and implementation of an incremental code‑coverage tool built on JaCoCo, detailing how it collects exec files, extracts changed Java methods via JGit, modifies JaCoCo to report only those methods, generates coverage reports, and integrates the whole process into a DevOps platform for automated feedback.
Learn how to enhance PHP code quality by implementing unit tests using PHPSpec and PHPUnit, covering SOLID principles, mock objects, test setup, code coverage integration, and best practices for writing maintainable, reliable backend code.
This article explains the concept, measurement dimensions, and practical issues of code coverage, compares full and differential coverage, describes JaCoCo's on‑the‑fly and offline instrumentation techniques, and outlines how to resolve instrumentation conflicts in a Java service environment.
This guide explains how to raise Spring Boot code quality to near‑zero defects by writing comprehensive unit tests for Service, DAO, and Controller layers, integrating the Cobertura plugin for detailed coverage metrics, and automating the process with Jenkins for continuous integration.
The Qunar QTest Conference held on April 21 showcased a series of technical talks covering test environment governance, code coverage platforms, dynamic BadSQL scanning, machine‑learning‑based mobile performance testing, and case‑bug management systems, providing attendees with practical insights and future development roadmaps.
This article explains how to set up gcov and lcov on Ubuntu, write a simple C++ program, compile it with coverage flags, generate .gcno and .gcda files, use lcov to create an HTML coverage report, and interpret the resulting line and function coverage percentages.
This article explains the concepts of code coverage, describes different coverage metrics such as statement, branch, and function coverage, introduces BullseyeCoverage and gcov/lcov tools, and provides detailed step‑by‑step procedures for measuring coverage in both executable binaries and dynamic libraries for C/C++ projects.
This article explains the concept of continuous integration, its benefits, and details JD Daojia's end‑to‑end CI implementation—including Git commits, static code analysis, unit testing, automated packaging, deployment, UI/JSF/APP testing, code coverage, and future automation plans—illustrated with architecture diagrams and configuration snippets.
The article describes Ctrip Hotel's evolution toward a 360‑degree quality assurance system, detailing its testing processes, continuous integration, performance testing, monitoring, and the design and features of a Java code‑coverage platform that integrates with existing CI and automation tools.
This article explains why test cases are essential for high‑quality, maintainable code, compares TDD and BDD agile models, and introduces key frontend testing tools such as Mocha, Should.js, Nock, Istanbul, and mochawesome with practical usage examples.
The article introduces php-code-coverage, an open‑source library that collects, processes, and visualizes PHP code‑coverage data, explains its measurement metrics such as line, function, and class coverage, and shows how to integrate it with PHPUnit, Composer, and Jenkins for automated testing and quality assessment.
Learn how to achieve comprehensive code coverage for multi‑process Node.js applications by using Istanbul, instrumenting child processes, writing Mocha tests, and merging coverage reports, with detailed examples of master‑worker RPC, custom fork hacks, and package.json scripts for automated testing.
