This article examines the challenges of manual mobile UI testing, introduces AI‑driven self‑healing techniques that combine multimodal perception, visual models and semantic analysis, and details the architecture, diagnostic workflow, smart popup handling, change‑aware engines, practical results and future directions.
This article outlines a comprehensive checklist of mobile testing items—including functional, conflict, performance, signal, low‑battery, compatibility, stress, field, stability, regular, user‑experience, and hardware tests—to help engineers ensure thorough coverage of a phone's quality and reliability.
This article breaks down the eight core differences between mobile and web testing, covering platform fragmentation, network scenarios, interaction models, installation processes, performance metrics, interruption handling, security concerns, and automation tools, and explains why mobile testing demands broader technical breadth.
This article details the design and implementation of an AI‑driven smart inspection platform for a mobile app, covering background challenges, system architecture, core detection features—including layout, visual, consistency, and AI‑operation checks—platform configuration, result feedback, and the measurable improvements achieved.
This article dissects the practical challenges of using Midscene.js for Android UI automation, demonstrates why auto‑planning can fail, and provides concrete step‑by‑step solutions—including instant operation APIs, assertion checks, refined prompts, coordinate clicks, conditional scrolling, and smart waiting—to make AI‑driven mobile testing reliable and efficient.
This article introduces Sonic, an open‑source cloud‑device testing platform that leverages a Spring Cloud micro‑service architecture and Docker deployment to provide low‑cost, scalable, and flexible automated testing, remote debugging, CI/CD integration, and multi‑platform support for mobile applications.
This guide explains how to use the Android Monkey tool to generate random user events for stress testing, configure its parameters for different testing scenarios, and analyze the resulting logs to identify crashes, ANRs, and event sequences that affect app stability.
This article explains the various pseudo‑random event types that the Android Monkey tool can generate, shows the command‑line parameters for each type, and provides a sample ADB command to set custom event‑type percentages for more realistic mobile app stress testing.
This article discusses implementing UI automation testing for auto insurance systems using Playwright, addressing the challenges of testing multiple insurance providers and demonstrating how automated testing can significantly improve efficiency and reduce manual testing workload.
This article explores how Hu Jia‑chun and the Huolala testing team tackled the exploding complexity of mobile app testing across Android, iOS, and HarmonyOS by applying task decomposition, parallel execution, cloud‑based device farms, OCR‑driven precise control, and custom multi‑device synchronization tools to dramatically improve coverage and speed.
This article describes how a team built a custom automated testing platform for React Native mobile apps using Airtest and Poco, covering tool selection, script examples, platform architecture with React frontend and Python backend, and solutions for parallel execution and mock services.
This article explains how to bypass the secure keyboard restriction in HarmonyOS by using the hdc command to simulate key events, allowing password entry through a custom front‑end keyboard and mapping keycode values to automate input on devices with privacy‑protected screens.
Learn the essential mobile app compatibility testing tools—including Appium, Calabash, Robot Framework, Selendroid, TestFlight, BrowserStack, and more—plus a practical Python unittest example that demonstrates how to verify element presence across devices and OS versions.
The article recounts the author’s journey building and scaling front‑end UI automation for desktop, web, and mobile, exposing challenges such as fragile end‑to‑end tests, high maintenance cost, and merge‑induced regressions, and proposes a three‑layer strategy of unified frameworks, collaborative case sharing, and enhanced reporting that yielded 70% business coverage, 93% pass rates, and thousands of bugs discovered, while outlining future AI‑driven test generation.
The article presents the background, full‑scenario construction, core architecture, device‑pool strategy, remote iOS control via WebDriverAgent, screen‑sync using ffmpeg, streaming pipeline, data collection, and practical lessons of the Cloud Touch platform that enables unified remote testing and customer‑support workflows for mobile applications.
This article examines how integrating real‑time debugging with large‑model AI can dramatically improve the accuracy and success rate of automatically generated UI test scripts, presenting a LangChain‑based architecture, toolchain design, experimental results, and future challenges in AI‑driven UI automation.
Meituan’s Technical Salon #77 presented a client‑side robustness testing framework that mutates API responses using semantic rules, injects them via a proxy, and detects crashes or hangs through static code scans and dynamic monitoring, employing array‑deduplication techniques to cut test volume while maintaining coverage, now deployed in Meituan and Youxuan apps.
This article details the background, design, architecture, and real-world impact of NetEase Cloud Music’s Saturn platform, a comprehensive solution for mobile UI test case recording, management, device scheduling, and execution that dramatically improves testing efficiency and product quality.
This article examines how Shentong, a logistics company, built a comprehensive mobile testing ecosystem—including functional, compatibility, and specialized tests—leveraging automation, a DevOps‑driven packaging pipeline, and a Jenkins‑based framework to dramatically improve testing efficiency and product quality.
To meet NetEase Cloud Music’s tight regression schedule, the team built Athena, a one‑stop UI automation framework that combines record‑and‑play case entry, Dawn telemetry‑based stable element locating, white‑box communication for efficiency, and integrated network‑mock and multi‑account support, delivering 73% coverage, over 90% success, and cutting regression time by more than 60%.
This article details HuoLala's journey from a weekly release cycle to a cloud‑based record‑and‑replay mobile UI automation platform, covering background challenges, industry analysis, technical design—including deep‑learning based control detection, SIFT image matching, script generation, playback handling, and platform features—while demonstrating significant testing efficiency gains and future AI‑driven enhancements.
This comprehensive guide explains Appium’s cross‑platform architecture, walks through setting up an Android testing environment on macOS, demonstrates a full‑stack test case for an in‑app H5 page, and shares advanced techniques like a WebSocket‑based JS agent and OpenCV image‑recognition for challenging hybrid scenarios.
The article proposes a six‑layer methodology for mobile compatibility testing that starts with hardware specs, adds market and roadmap insights, prioritizes high‑share and bug‑prone models, classifies performance tiers, accounts for special devices, aligns scenarios with key parameters, and uses an algorithm to group, rank, and flag representative devices for efficient testing.
This article introduces the open‑source Airtest framework for mobile UI automation, outlines its cross‑platform features, and provides a complete Python script that automates an end‑to‑end e‑commerce purchase, payment, return, and refund process with test data cleanup.
This article provides a comprehensive reference for Airtest's automation APIs, covering script initialization functions, device connection methods, application lifecycle commands, and frequently used interaction utilities such as screenshot, touch, swipe, text input, and key events, each illustrated with Python code examples.
DoKit achieves non‑intrusive first‑line JavaScript injection for Android WebView by intercepting HTML requests, parsing with Jsoup to prepend a script tag, and employing ASM bytecode hooks to wrap WebViewClient, ensuring the OMMC feature reliably controls multiple devices on H5 pages.
This guide explains the fundamentals of automation testing, identifies suitable projects, compares common automation types such as web, mobile, and API testing, introduces essential tools like Selenium, Appium, uiautomator2, and JMeter, and provides step‑by‑step code examples to help beginners quickly implement automated tests in real‑world scenarios.
The guide outlines community-driven client‑side testing practices—functional, compatibility, exploratory, UX, performance, stability, and security testing—detailing tool use such as TeslaLab, automation and AI techniques, cross‑device and platform challenges, memory‑leak and crash analysis, and combined black‑box/white‑box approaches to improve app quality and data privacy.
This article details Huolala's evolution from a simple wireless ADB setup to the sophisticated TianGong system, covering architecture, video streaming, input handling, large‑scale device pool management, and future plans for intelligent scheduling and iOS optimization.
This guide outlines essential mobile app testing practices, including device and platform selection, handling user mobility and network changes, multitasking scenarios, gesture conflicts, user experience factors, permissions, caching, web dependency, layered and exploratory testing, security checks, and effective log usage.
Huolala's data team created a cloud‑based real‑device platform that centralizes phone resources, enables remote screen casting and control via adb, minicap, and minitouch, and uses Spring Boot and Netty‑WebSocket to deliver scalable, cost‑effective mobile testing across multiple scenarios.
This tutorial explains how to set up the open‑source screen‑copy tool Scrcpy and the Python‑based UiAutomator2 library to control an Android device from a computer, covering installation, environment configuration, sample code for interacting with apps, and practical use cases such as automated liking and commenting.
This guide outlines practical steps for selecting devices, weighing real devices versus emulators, leveraging cloud and network testing, and conducting UX, performance, and security checks to ensure high‑quality mobile app deployments in today’s fast‑paced digital landscape.
This guide explains why QA engineers should learn to code, outlines five concrete steps—including choosing a language, intensive coding practice, creating a GitHub profile, building a simple test, and continuous learning—to successfully move from manual testing to mobile automation development.
This article details Meituan’s AlphaTest platform, explaining how it tackles the growing complexity of Meituan Waimai’s multi‑business, multi‑technology, multi‑app environment by introducing a low‑learning‑cost, highly maintainable, data‑consistent record‑replay automation solution that improves test efficiency, stability, and traceability.
The article presents the TARS UI automation platform, analyzes its core performance indicators and the problems uncovered, and details a comprehensive redesign that introduces automatic case recording, tag generation, data mocking, smart assertions, and an upgraded cloud‑based testing infrastructure to improve coverage, accuracy, and efficiency for mobile app releases.
This article examines the evolution of mobile UI testing toward AI‑driven approaches, outlines the challenges of large‑scale apps, and details a practical workflow for constructing image‑based anomaly datasets, training a ResNet‑18 model, and iterating on detection performance.
This article describes the design and implementation of an intelligent compatibility testing platform for mobile games, covering device fragmentation challenges, testing strategies, hardware setup, automated and AI‑driven testing services, real‑time monitoring, remote maintenance, and the integration of Airtest for script automation.
This article explores the challenges of mobile white‑box testing and presents an AOP‑driven solution that injects flow‑recording and replay code suites into Android and iOS apps, detailing technical goals, testability metrics, instrumentation techniques, and practical engineering outcomes.
The article summarizes the 28th 58 Technology Salon held on April 27, 2022, featuring presentations on R&D efficiency measurement and mobile quality assurance, including detailed Q&A sessions that discuss metrics, data handling, performance testing tools, and practical improvements for development and testing teams.
This collection presents five technical articles covering the Spock unit‑testing framework, reasons why automation testing struggles, backend automated testing with continuous deployment, AI‑based UI defect detection, and practical automation of HarmonyOS applications, offering valuable insights for developers and testers.
The article presents an end‑to‑end automated validation platform—combining teslaLab, Android and iOS SDKs, mock‑recording, scheduling and reporting—to reliably verify e‑commerce mobile app event tracking, address data‑source instability, streamline bug detection, and outline future cloud‑device integration.
The article explains how Baidu’s Smart Mini Program, which runs hybrid H5‑Native code in separate Logic and View threads within the Baidu app, cannot be tested with standard tools, so the Smart Mini Program Testing Platform created the smartapp‑automator framework that combines a Selenium‑like engine for in‑process UI control with a unified device‑control API abstracting Android, iOS, Chrome and cloud‑phone automation tools.
Flybirds is a BDD‑based, open‑source UI automation framework for mobile apps that provides a plug‑in architecture, CLI scaffolding, multilingual support, rich reporting, and seamless integration with CI pipelines, enabling developers to write natural‑language feature files and run them on Android and iOS devices.
This guide explains how to set up and use WEditor for Android and tidevice/Appium for iOS to locate UI elements efficiently, covering prerequisite tools, installation commands, step‑by‑step connection procedures, error handling, and best‑practice strategies for reliable mobile UI automation.
The article explains the importance of responsive web design for mobile users, outlines various mobile web testing approaches—including Chrome DevTools, simulators, real devices, and cloud‑based automation—and highlights how these strategies ensure consistent, high‑quality user experiences across diverse devices and network conditions.
This article provides a detailed overview of iOS UI automation testing, covering XCUITest fundamentals, Accessibility considerations, sample Swift code, manual and recorded test case creation, command‑line toolchains, Web Service‑based architecture, and a comparison of Appium and Macaca for building scalable, CI‑integrated mobile testing platforms.
iQIYI’s iOS Cloud Recording and Playback Platform leverages a cloud‑device farm and a Swift‑based driver to capture user actions, generate Python scripts, and replay them across shared devices, dramatically cutting automation costs while providing fast DOM access, multi‑method element identification, CI integration, and visual reporting for reliable regression testing.
This article describes Alipay’s client‑side high‑availability strategy that combines offline risk mining, function‑interface “minesweeping”, RPC/config/jsapi checks, and automated fuzz testing on Android and iOS to detect and eliminate crash‑inducing bugs before release.
This article provides a detailed overview of iOS UI automation testing, covering XCUITest fundamentals, accessibility considerations, code examples, manual and recorded test case creation, command‑line toolchains, Web Service‑based architecture, and a comparison of Appium and Macaca for building scalable, cross‑platform mobile testing platforms.
This article details ByteDance’s Fastbot system, an AI‑driven cross‑platform automated testing framework for Android and iOS that leverages model‑based testing, reinforcement learning, and image‑based UI analysis to improve test coverage, fault injection, and scalability across mobile applications and games.
This article introduces a cloud‑based mobile testing platform, detailing its architecture, implementation with Spring Boot and Appium, and real‑world applications in compliance verification and online inspection, highlighting how AI integration and automated workflows dramatically improve testing efficiency and product quality.
Users uninstall apps when friction—such as privacy worries, useless features, crashes, slow performance, or intrusive ads—overrides desire, so applying a five‑level APP experience pyramid (Security, Functionality, Stability, Usability, Delight) with privacy checklists, exploratory testing, full‑stack monitoring, and real‑user usability sessions can markedly boost retention.
This article details a UI automation regression framework for mobile event tracking, covering background challenges, Android and iOS log collection methods, H5 integration strategies, performance comparisons, and practical recommendations to improve testing efficiency by about 50%.
This article describes how a logistics company tackled the growing complexity of mobile app testing by evaluating automation tools, creating a real‑device platform, and implementing a Jenkins‑driven automation framework that supports parallel execution, script management, and future AI‑enhanced compatibility testing.
This guide walks you through what Airtest is, how to install it, connect devices, use its visual interface for basic operations, and follow a step‑by‑step example that records actions and generates runnable test scripts for Android and iOS apps.
A six‑month study of app trends and user surveys uncovers four major mobile testing directions—continuous testing adoption, cloud‑based infrastructure migration, AI‑enhanced testing, and the rise of low‑code platforms—highlighting how they reshape development speed, quality, and remote work capabilities.
This article details a comprehensive UI automation framework for the Dali Smart Homework Lamp, covering business context, product features, testing challenges, automated script generation, modular case design, multi-device coordination, error analysis, and future directions for intelligent UI validation.
This hands‑on guide explains how to set up the cross‑platform Airtest framework, connect Android or iOS devices, record and edit Python‑based UI scripts, use image‑and value‑based assertions, generate HTML reports, and leverage Poco for element‑level automation in agile mobile development.
This document outlines a detailed testing strategy for the Cloud Classroom mobile app, covering security, static code analysis, performance, compatibility, and overall quality assurance to ensure stable functionality and a positive user experience.
iQIYI’s technology team built a non‑intrusive robustness‑testing platform that injects engineered “dirty data” into intercepted HTTP responses via an ASM‑hooked SDK, letting users configure mutation rules through a web console and run UI, monkey, or manual tests that have already uncovered numerous hidden crashes, achieving over 50 % defect‑closure and markedly improving app stability.
This tutorial explains how to set up Python and Appium on a PC, configure Android SDK and device debugging, and write a Python script that automatically reads and replies to QQ messages on an Android phone.
The article presents DuLab, a distributed UI‑automation platform for mobile apps that leverages Airtest, Python/Tornado, Celery, and Redis to schedule and run bulk test cases on remote devices, detailing its layered case management, package database, mock services, and the resulting scalable, efficient regression testing across diverse devices.
This step‑by‑step guide shows how to set up Python, Appium, JDK, Android SDK and a screen‑mirroring tool, connect an Android phone, verify the ADB connection, and run a complete Appium script that automatically reads and replies to QQ chats.
Youku’s intelligent component‑testing solution for its mobile app combines mock‑driven data factories, image‑recognition layout verification, and a data‑driven automation framework to dramatically cut regression effort, boost test stability, and now automates over 60% of component cases while covering more than 90% of frequently used UI components.
Hydra, Baidu’s one‑device‑multi‑control tool, lets a tester operate a master mobile device while simultaneously replicating actions to multiple slave devices via cloud platforms, using WebSocket‑based architecture and high‑performance image algorithms to ensure accurate UI mapping, thereby boosting mobile UI compatibility testing efficiency by 20‑70 % weekly.
This guide outlines the typical career ladder for mobile test engineers, the essential technical and device knowledge, hands‑on experience tips, and the most useful testing tools and methodologies to help newcomers advance from junior roles to senior leadership positions.
This article introduces Hydra, a PC‑based tool that enables one‑controller‑multiple‑device (one‑machine‑multiple‑control) testing for mobile UI compatibility, detailing its background, design principles, core modules such as the group‑control engine, real‑time image streaming, high‑performance image algorithms, consistency repair, and deployment results within Baidu.
This article introduces Ke‑FUT, a home‑grown Flutter UI automation solution that leverages VMService and InspectorService to obtain element IDs, generates stable IDs via a custom IdGenerator, and drives view interactions through a Python‑based FUTClient, providing a low‑cost, cross‑platform testing approach for mobile apps.
This tutorial walks through a complete Android app packet‑capture workflow—installing a Wi‑Fi‑proxy‑compatible emulator, deploying the app via UI or ADB, configuring Charles or Fiddler as a debugging proxy, handling HTTPS certificates for Android 7+, and capturing traffic for functional, security, or development testing.
This article describes the background, challenges, and solution design for building a cross‑platform mobile UI automation framework at QuNar, covering test classification, element locating strategies, the POCO library, Page Object Model, JSON mock handling, and continuous integration with Jenkins to improve testing efficiency and reduce manual effort.
This guide explains how to lower Android device power usage and avoid unexpected shutdowns during large‑scale automated testing by controlling battery thresholds, adjusting screen brightness, terminating background apps, setting screen timeout, synchronizing time, and managing audio settings through ADB shell commands.
The JDTC platform, launched by JD.com’s testing department, provides a cross‑platform automated testing center for Android, iOS, iPad, and web applications, offering script frameworks, remote distributed execution, comprehensive reporting, and integration with agile development cycles to improve test efficiency and product quality.
The article introduces the TcloudServer cloud testing platform, outlines its key features such as cloud devices, workflow management, and dashboards, and provides detailed configuration instructions including Python settings, Jenkins pipeline setup, and parameter definitions for automated mobile monkey testing.
This article explores why mobile app testing is crucial, explains the mobile testing pyramid, details functional, regression, performance, security, usability, and compatibility testing types, compares automation frameworks and tools, and discusses practical challenges such as device fragmentation, network variability, and battery constraints.
This tutorial explains how to use Python, Appium, and OpenCV to automatically perform WeChat’s “punch” (拍一拍) action by locating a friend’s avatar via image recognition and simulating a double‑tap, all within roughly 30 lines of code.
This article outlines a systematic, step‑by‑step approach for testers to gather evidence, reproduce, and diagnose production bugs on mobile and web platforms, emphasizing thorough data collection, environment checks, and collaborative debugging to improve response speed and reliability.
The article outlines the evolution, architecture, and key components of a mobile cloud testing platform—including record‑replay, test management, service orchestration, and cloud device management—while discussing practical challenges, future directions, and recommendations for effective mobile testing at scale.
iQIYI’s technology team created a cloud‑based automated recording‑playback platform that configures environments with one click, generates Python scripts from UI actions, distributes tasks to Docker‑managed devices, captures video, uses OCR and image matching for element interaction, handles pop‑ups, installs apps, and is slated for CI/CD integration to accelerate mobile UI regression testing.
This article explains why UI automation testing is essential for complex mobile apps, why Appium is chosen, how to organize test cases with Cucumber, implement custom steps, locate elements using various strategies, handle common exceptions, and outlines the overall workflow and improvement suggestions.
This article explains why UI automation testing is crucial for complex mobile apps, introduces Appium as a cross‑platform open‑source solution, demonstrates organizing test cases with Cucumber and Page Object patterns, details element locating strategies, custom steps, workflow architecture, and discusses current limitations and improvement plans.
This guide explains how to adopt Xcode’s UITesting framework for iPad applications—COOHOM, KuJiaLe Cloud Design, and Enterprise Cloud Design—detailing setup, test case creation, execution, code coverage collection, and seamless integration with GitLab CI to automate testing and reporting.
The guide explains automated testing fundamentals, classifications, and current platform implementations—covering unit, interface, and UI testing on PC, Android, and iOS—while detailing data‑driven and keyword‑driven approaches and forecasting AI‑powered visual and functional testing using deep‑learning frameworks.
The article details Youzan Retail’s precise iOS testing platform, which instruments Objective‑C code via GCC/LLVM to generate .gcno/.gcda files, processes them with LCOV, and provides both full and git‑diff‑based incremental coverage visualizations across CI, data collection, parsing, and reporting layers to improve manual and automated test quality.
The article describes how JD's mobile testing team created a component‑based "use‑case meta" framework that abstracts common test scenarios into six reusable categories, integrates them into the testing platform, and dramatically improves coverage, efficiency, and knowledge reuse across large‑scale app releases.
This article provides a detailed tutorial on Cucumber‑JVM, covering its BDD concepts, Gherkin syntax, feature file structure, step definition implementation, tagging strategies, command‑line execution, and Maven/IDEA project setup for Android mobile testing.
QTC, iQIYI’s extended ATC tool combining a dual‑NIC workstation, wireless hotspot and Python‑based software, dynamically simulates static and changing network conditions while providing low‑interference automated packet capture, enabling rapid mobile video app testing across diverse scenarios and uncovering numerous bugs.
This article explains how to automate the login process for multiple Android channel packages by packaging a Java UiAutomator script into a JAR, pushing it to the device, iterating over each APK, executing ADB commands, and logging success or failure for each login method.
This article presents a customizable Java helper class for Android UiAutomator that streamlines test project creation, build modification, APK deployment, and test execution, complete with detailed code, command explanations, and logging utilities for flexible mobile UI testing.
Testin Pro is a cloud‑based mobile automation testing platform that details its network topology, server configuration, user roles, key functionalities such as online script recording and automated execution, and its integration with the bank's ATP DevOps pipeline to improve testing efficiency and resource utilization.
This article explains how to set up a Windows environment with Node.js, Python, and Appium, dynamically generate desired capabilities for multiple phones, launch parallel Appium services and tests using multi‑process techniques, and handle port detection and release to achieve efficient multi‑device automation.
This article shares a comprehensive collection of reusable UiAutomator methods—including swipe gestures, screenshot handling, image analysis, UI element lookup, waiting strategies, click actions, scrolling, app launch/stop commands, and miscellaneous helpers—providing Android developers with ready‑to‑use Java code for robust UI automation testing.
When UiAutomator reports controls with bounds [0,0][0,0] and they cannot be clicked, you can compute click positions from a previously‑found element’s size and use those coordinates to tap the left or right half of the target control.
This article presents a comprehensive 18‑point checklist covering smoke testing, installation/uninstallation, upgrade methods, business functionality, stability, performance, interface testing, usability, device compatibility, platform integration, special scenarios, permissions, security, data protection, phone features, competitor comparison, common oversights, and feedback channel establishment for mobile app testing engineers.
This guide explains how to use Java's Runtime API within a UiAutomator test to start, filter, and safely terminate an adb logcat process, including a complete Logcat thread implementation and tips for preventing oversized log files.
This guide enumerates the most important software, frameworks, commands, and knowledge areas—from mind‑mapping and bug‑tracking tools to HTTP protocols, performance testing, Android debugging, CI integration, IDEs, databases, servers, version control, and automation frameworks—required for a competent mobile app testing engineer.
Soloπ is an open‑source, wireless, non‑intrusive Android testing solution that lets developers control phones directly to perform functional, performance, compatibility, and stability tests, featuring wireless ADB, accessibility‑based UI automation, Chrome DevTools integration, image‑recognition, multi‑device control, and built‑in performance metrics such as CPU, memory, FPS, and mobile Lighthouse.
Totoro is an Ant Financial‑developed mobile automation testing framework that supports Android, iOS, HTML5, mini‑programs, Weex and Cube, featuring a two‑layer C/S architecture, full‑link stability mechanisms, intelligent app installation, comprehensive popup governance, AI‑assisted image detection, and a roadmap toward standardization and extensibility.
This article explains how to perform ordered UI traversal of an Android application with UIAutomator on the 360 Open Testing platform, covering required parameters, a seven‑step implementation process, UI change listening via AccessibilityService, element dumping, sorting, comparison methods, interaction automation, and termination conditions.
The new mobile UI automation framework for the Xianyu app reduces script maintenance costs by using random monkey testing to record clicks and expected UI elements, organizing page‑specific scripts in MongoDB, executing them via Appium, and delivering data‑driven tests that doubled script count, achieved 98% pass rate, and cut update time from half a day to under an hour.
