An overview of PHPUnit Automated testing in Drupal 8, including a comparison of manual testing vs automated testing, the pros and cons of automated testing, and the types of automated tests available in Drupal 8.
Fog computing is a distributed computing paradigm that extends cloud computing and services to the edge of the network. It aims to address issues with cloud computing like high latency and privacy concerns by processing data closer to where it is generated, such as at network edges and end devices. Fog computing characteristics include low latency, location awareness, scalability, and reduced network traffic. Its architecture involves sensors, edge devices, and fog nodes that process data and connect to cloud services and resources. Research is ongoing in areas like programming models, security, resource management, and energy efficiency to address open challenges in fog computing.
ATDD (Acceptance Test Driven Development) is a practice where the team discusses acceptance criteria and tests needed to ensure a feature meets user expectations. It has four stages: discuss functionality and tests, define test scenarios and data, develop using TDD, and demo tests to stakeholders. A sample shows a calculator user story, criteria, and tests for adding, subtracting, etc. ATDD improves understanding, collaboration, and catches defects earlier.
The huge volume of text documents available on the internet has made it difficult to find valuable
information for specific users. In fact, the need for efficient applications to extract interested knowledge
from textual documents is vitally important. This paper addresses the problem of responding to user
queries by fetching the most relevant documents from a clustered set of documents. For this purpose, a
cluster-based information retrieval framework was proposed in this paper, in order to design and develop
a system for analysing and extracting useful patterns from text documents. In this approach, a pre-
processing step is first performed to find frequent and high-utility patterns in the data set. Then a Vector
Space Model (VSM) is performed to represent the dataset. The system was implemented through two main
phases. In phase 1, the clustering analysis process is designed and implemented to group documents into
several clusters, while in phase 2, an information retrieval process was implemented to rank clusters
according to the user queries in order to retrieve the relevant documents from specific clusters deemed
relevant to the query. Then the results are evaluated according to evaluation criteria. Recall and Precision
(P@5, P@10) of the retrieved results. P@5 was 0.660 and P@10 was 0.655.
In this quality assurance training, you will learn Test Case Design and Technique. Topics covered in this session are:
• Test Case Design and Techniques
• Black-box: Three major approaches
• Steps for drawing cause-Effect Diagram:
• Behavior Testing
• Random Testing
• White Box Techniques
• Path Testing
• Statement Coverage
• Data Flow Testing
For more information, visit this link: https://www.mindsmapped.com/courses/quality-assurance/software-testing-training-beginners-and-intermediate-level/
This document discusses how machine learning can be applied to various activities in software testing. It describes how machine learning works using training and test data to make predictions. Supervised and unsupervised learning techniques are discussed. Specific applications mentioned include software defect prediction, test planning, test case management, debugging, and refining blackbox test specifications. Challenges include availability of past data and finding predictable patterns, while potential steps forward include expanding machine learning to more blackbox techniques, identifying the right patterns for different test activities, algorithm analysis, and crowdsourcing.
Dockerization of real mobile device farm and scalable QA automation ecosystemAlexey Khursevich
Share our expertise in building up of highly scalable and effective infrastructure for Test Automation based on open-source solutions like Selenium and Appium by means of Docker.
We are going to show real case studies of our production challenges. Also we are going to talk about self-hosted mobile cloud solutions as part of QA ecosystem and how Docker simplifies our life in this area.
This document discusses MLOps, which aims to standardize and streamline machine learning model development and deployment through continuous delivery. MLOps applies agile principles to machine learning projects and treats models and datasets as first-class citizens within CI/CD systems. The document outlines three levels of MLOps implementation from manual to fully automated pipelines. It also describes common MLOps platform tools for data management, modeling, and operationalization, including tools for data labeling, versioning, experiment tracking, hyperparameter optimization, model deployment, and monitoring.
Getting in the zone is harder to do these days with the infinite sources of distractions that are readily available. In this talk, I’ll give you a framework, tools and actionable tips to help you become more efficient, both in work and play. I will draw on my experiences as a family man, college student, developer, business owner, employee and manager using techniques I’ve learned reading countless books and articles in the area of “Productivity”. The end goal is for you to have the focused time needed to do the things that you are passionate about, whether that be writing awesome software or spending more time with friends and family.
What MLflow is; what problem it solves for machine learning lifecycle; and how it solves; How it will be used with Databricks; and CI/CD pipeline with Databricks.
The document discusses unit testing, defining it as testing the smallest piece of code in isolation. It provides an example of a unit (a math class with an Add method) and unit test (a test method that calls Add and asserts the expected result). It outlines considerations for unit testing like interfaces, data structures, and paths. It describes the unit testing procedure of using a driver and stubs to provide a complete testing environment and isolate the module under test.
The document discusses unit testing and automated testing. It defines various testing terminology like unit tests, integration tests, system tests, and regression tests. It emphasizes the importance of testing early and often to find bugs quickly, increase quality assurance, and improve code design for testability. Automating tests through continuous integration is recommended to efficiently run tests on new code commits and catch errors early. Test-driven development is introduced as a practice of writing tests before code to ensure all tests initially fail and the code is developed to pass the tests.
This document provides an overview of Google Colab and how it can be used for deep learning and Python programming. It discusses how Colab provides free GPU access in the cloud and can be connected to Google Drive. It also demonstrates how to set up a Colab notebook, access files from Drive, clone GitHub repos, check the GPU status, and restart sessions. The document is a classroom presentation on using Colab for cloud computing and deep learning applications.
This document discusses automated testing for web applications. It defines automated testing as writing a program to perform tests that would otherwise need to be done manually. The document outlines advantages of automated testing like ability to rerun tests quickly and ability to perform cross-browser compatibility testing. It also discusses disadvantages like initial costs. The document provides examples of automated testing frameworks and demonstrates Watir and Selenium frameworks. It ends with asking for any questions.
Representing TF and TF-IDF transformations in PMMLVillu Ruusmann
This document discusses representing term frequency (TF) and TF-IDF transformations in the Predictive Model Markup Language (PMML). It provides details on encoding TF, TF-IDF, and text indexing in PMML, including defining a centralized TF-IDF function and invoking it for multiple documents. It also covers techniques for string normalization, tokenization, and counting terms during text transformations. Finally, it discusses ensuring interoperability between PMML and scikit-learn for text feature extraction.
“Houston, we have a model...” Introduction to MLOpsRui Quintino
The document introduces MLOps (Machine Learning Operations) and the need to operationalize machine learning models beyond just model deployment. It discusses challenges like data and model drift, retraining models, software dependencies, monitoring models in production, and the need for automation, testing, and reproducibility across the full machine learning lifecycle from data to deployment. An example MLOps workflow is shown using GitHub and Azure ML to enable experiment tracking, automation, and continuous integration and delivery of models.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2J5SAcT.
Detlef Nauck explains why the testing of data is essential, as it not only drives the machine learning phase itself, but it is paramount for producing reliable predictions after deployment. Testing the decisions made by a deployed machine learning model is equally important to understand if it delivers the expected business value. Filmed at qconlondon.com.
Detlef Nauck is Chief Research Scientist for Data Science with BT's Research and Innovation Division. He is leading a group of scientists working on research into Data Science, ML and AI. He focuses on establishing best practices in DS for conducting analytics professionally and responsibly leading to new ways of analysing data for achieving better insights.
MRUnit is a testing library that makes it easier to test Hadoop jobs. It allows programmatically specifying test input and output, reducing the need for external test files. Tests can focus on individual map and reduce functions. MRUnit abstracts away much of the boilerplate test setup code, though it has some limitations like a lack of distributed testing. Overall though, the benefits of using MRUnit to test Hadoop jobs outweigh the problems.
Performance testing validates an application's responsiveness, stability, and other quality attributes under various workloads. It involves load testing, stress testing, endurance testing, spike testing, volume testing, availability testing, and scalability testing. The key parameters analyzed are response time, throughput, and memory utilization. Performance testing helps determine an application's speed, scalability, stability, and ability to handle changes in load and traffic over time.
Software Quality Assurance (QA) Testing Interview Questions & AnswersJanBask Training
Here are most frequently asked Quality Assurance (QA) Interview Questions and Answers: I would ask if interviewing a Quality Assurance engineer. The questions will emphasize more on the quality processes and the strategy, so please see that the question will not be asked for Testing.
The document discusses unit testing and its benefits and limitations. It notes that while tests provide confidence that code works as intended, they cannot prevent all problems. The Boeing 737 MAX crashes are discussed as an example of issues despite passing tests due to sensor problems. Proper unit testing involves automated, repeatable, and continuous testing of isolated units with mocked dependencies. Test-driven development and design can lead to more testable and maintainable code, but tests also have costs and limitations.
This is short course on Defect Analysis & Prevention.
The course covers following main topics:
1. What is Defect Analysis?
2. Defect Prevention
3. Defect Analysis Procedure
4. Defect Prevention Action Planning
5. Summary
It discusses use of Pareto Charts & Fish bone diagrams, to focus on main causes that are causing 80% of the defects, and then perform in-depth analysis of those causes.
This helps to come up with Defect Prevention Plan for your project/product.
Model-based Testing: Taking BDD/ATDD to the Next LevelBob Binder
Slides from presentation at the Chicago Quality Assurance Association, February 25, 2014.
Acceptance Test Driven Development (ATDD) and Behavior Driven Development (BDD) are well-established Agile practices that rely on the knowledge and intuition of testers, product owners, and developers to identify and then translate statements into test suites. But the resulting test suites often cover only a small slice of happy-path behavior. And, as a BDD specification and its associated test code base grows over time, work to maintain it either crowds out new development and testing or, typically, is simply ignored. Either is high-risk. That’s how Agile teams get eaten by the testing BackBlob.Model Based Testing is a tool-based approach to automate the creation of test cases. This presentation will outline the techniques and benefits of MBT, and show how model-based testing can address both problems. A detailed demo of Spec Explorer, a free model-based testing tool shows how a model is constructed and used to create and maintain a test suite.
Cloud migration involves moving data, applications, and other business elements from an organization's on-premises servers to the cloud. There are different approaches to cloud migration such as shallow integration which moves applications to the cloud without changes, deep integration which modifies applications to use cloud capabilities, refactoring applications to optimize them for the cloud, and retiring old applications in favor of SaaS solutions. Successful cloud migration requires planning such as assessing what to migrate, choosing cloud providers and environments, determining architecture, selecting providers, planning the migration process, and reviewing after migration. While cloud migration provides benefits, it also carries risks such as complex architectures not working correctly in the cloud, loss of control over data, and increased latency.
This document provides an introduction to automation testing. It discusses the need for automation testing to improve speed, reliability and test coverage. The document outlines when tests should be automated such as for regression testing or data-driven testing. It also discusses automation tool options and the process for automating tests. While automation testing provides benefits like time savings, it also has limitations such as the need for programming skills and maintenance of test code. Key challenges of automation testing include unrealistic expectations of tools and dependency on third party integrations.
This document discusses automated testing in Drupal using SimpleTest, a PHP testing framework. It provides an overview of what SimpleTest is, why automated testing is important, how to install SimpleTest, how tests are structured and run, and the various assertions and helper functions available. Automated testing helps ensure code works as intended, makes development and refactoring easier, and prevents regressions.
The document discusses different types of automated testing methodologies in Drupal 8 including unit testing, kernel testing, and functional testing. It provides details on how to register and run tests using PHPUnit and describes best practices for mocking dependencies and configurations.
This document discusses different types of software testing including unit testing and integration testing. Unit testing involves writing code to test individual units or modules in isolation with few dependencies. Integration testing evaluates how modules function together as a group in a black box manner. The benefits of testing include facilitating code changes, enhancing software quality by catching bugs, serving as documentation, and improving code quality. Test-driven development is introduced as an approach where tests are written before code to drive the design. Continuous integration is also discussed as a way to fully automate testing, build, and code quality checks.
Getting in the zone is harder to do these days with the infinite sources of distractions that are readily available. In this talk, I’ll give you a framework, tools and actionable tips to help you become more efficient, both in work and play. I will draw on my experiences as a family man, college student, developer, business owner, employee and manager using techniques I’ve learned reading countless books and articles in the area of “Productivity”. The end goal is for you to have the focused time needed to do the things that you are passionate about, whether that be writing awesome software or spending more time with friends and family.
What MLflow is; what problem it solves for machine learning lifecycle; and how it solves; How it will be used with Databricks; and CI/CD pipeline with Databricks.
The document discusses unit testing, defining it as testing the smallest piece of code in isolation. It provides an example of a unit (a math class with an Add method) and unit test (a test method that calls Add and asserts the expected result). It outlines considerations for unit testing like interfaces, data structures, and paths. It describes the unit testing procedure of using a driver and stubs to provide a complete testing environment and isolate the module under test.
The document discusses unit testing and automated testing. It defines various testing terminology like unit tests, integration tests, system tests, and regression tests. It emphasizes the importance of testing early and often to find bugs quickly, increase quality assurance, and improve code design for testability. Automating tests through continuous integration is recommended to efficiently run tests on new code commits and catch errors early. Test-driven development is introduced as a practice of writing tests before code to ensure all tests initially fail and the code is developed to pass the tests.
This document provides an overview of Google Colab and how it can be used for deep learning and Python programming. It discusses how Colab provides free GPU access in the cloud and can be connected to Google Drive. It also demonstrates how to set up a Colab notebook, access files from Drive, clone GitHub repos, check the GPU status, and restart sessions. The document is a classroom presentation on using Colab for cloud computing and deep learning applications.
This document discusses automated testing for web applications. It defines automated testing as writing a program to perform tests that would otherwise need to be done manually. The document outlines advantages of automated testing like ability to rerun tests quickly and ability to perform cross-browser compatibility testing. It also discusses disadvantages like initial costs. The document provides examples of automated testing frameworks and demonstrates Watir and Selenium frameworks. It ends with asking for any questions.
Representing TF and TF-IDF transformations in PMMLVillu Ruusmann
This document discusses representing term frequency (TF) and TF-IDF transformations in the Predictive Model Markup Language (PMML). It provides details on encoding TF, TF-IDF, and text indexing in PMML, including defining a centralized TF-IDF function and invoking it for multiple documents. It also covers techniques for string normalization, tokenization, and counting terms during text transformations. Finally, it discusses ensuring interoperability between PMML and scikit-learn for text feature extraction.
“Houston, we have a model...” Introduction to MLOpsRui Quintino
The document introduces MLOps (Machine Learning Operations) and the need to operationalize machine learning models beyond just model deployment. It discusses challenges like data and model drift, retraining models, software dependencies, monitoring models in production, and the need for automation, testing, and reproducibility across the full machine learning lifecycle from data to deployment. An example MLOps workflow is shown using GitHub and Azure ML to enable experiment tracking, automation, and continuous integration and delivery of models.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2J5SAcT.
Detlef Nauck explains why the testing of data is essential, as it not only drives the machine learning phase itself, but it is paramount for producing reliable predictions after deployment. Testing the decisions made by a deployed machine learning model is equally important to understand if it delivers the expected business value. Filmed at qconlondon.com.
Detlef Nauck is Chief Research Scientist for Data Science with BT's Research and Innovation Division. He is leading a group of scientists working on research into Data Science, ML and AI. He focuses on establishing best practices in DS for conducting analytics professionally and responsibly leading to new ways of analysing data for achieving better insights.
MRUnit is a testing library that makes it easier to test Hadoop jobs. It allows programmatically specifying test input and output, reducing the need for external test files. Tests can focus on individual map and reduce functions. MRUnit abstracts away much of the boilerplate test setup code, though it has some limitations like a lack of distributed testing. Overall though, the benefits of using MRUnit to test Hadoop jobs outweigh the problems.
Performance testing validates an application's responsiveness, stability, and other quality attributes under various workloads. It involves load testing, stress testing, endurance testing, spike testing, volume testing, availability testing, and scalability testing. The key parameters analyzed are response time, throughput, and memory utilization. Performance testing helps determine an application's speed, scalability, stability, and ability to handle changes in load and traffic over time.
Software Quality Assurance (QA) Testing Interview Questions & AnswersJanBask Training
Here are most frequently asked Quality Assurance (QA) Interview Questions and Answers: I would ask if interviewing a Quality Assurance engineer. The questions will emphasize more on the quality processes and the strategy, so please see that the question will not be asked for Testing.
The document discusses unit testing and its benefits and limitations. It notes that while tests provide confidence that code works as intended, they cannot prevent all problems. The Boeing 737 MAX crashes are discussed as an example of issues despite passing tests due to sensor problems. Proper unit testing involves automated, repeatable, and continuous testing of isolated units with mocked dependencies. Test-driven development and design can lead to more testable and maintainable code, but tests also have costs and limitations.
This is short course on Defect Analysis & Prevention.
The course covers following main topics:
1. What is Defect Analysis?
2. Defect Prevention
3. Defect Analysis Procedure
4. Defect Prevention Action Planning
5. Summary
It discusses use of Pareto Charts & Fish bone diagrams, to focus on main causes that are causing 80% of the defects, and then perform in-depth analysis of those causes.
This helps to come up with Defect Prevention Plan for your project/product.
Model-based Testing: Taking BDD/ATDD to the Next LevelBob Binder
Slides from presentation at the Chicago Quality Assurance Association, February 25, 2014.
Acceptance Test Driven Development (ATDD) and Behavior Driven Development (BDD) are well-established Agile practices that rely on the knowledge and intuition of testers, product owners, and developers to identify and then translate statements into test suites. But the resulting test suites often cover only a small slice of happy-path behavior. And, as a BDD specification and its associated test code base grows over time, work to maintain it either crowds out new development and testing or, typically, is simply ignored. Either is high-risk. That’s how Agile teams get eaten by the testing BackBlob.Model Based Testing is a tool-based approach to automate the creation of test cases. This presentation will outline the techniques and benefits of MBT, and show how model-based testing can address both problems. A detailed demo of Spec Explorer, a free model-based testing tool shows how a model is constructed and used to create and maintain a test suite.
Cloud migration involves moving data, applications, and other business elements from an organization's on-premises servers to the cloud. There are different approaches to cloud migration such as shallow integration which moves applications to the cloud without changes, deep integration which modifies applications to use cloud capabilities, refactoring applications to optimize them for the cloud, and retiring old applications in favor of SaaS solutions. Successful cloud migration requires planning such as assessing what to migrate, choosing cloud providers and environments, determining architecture, selecting providers, planning the migration process, and reviewing after migration. While cloud migration provides benefits, it also carries risks such as complex architectures not working correctly in the cloud, loss of control over data, and increased latency.
This document provides an introduction to automation testing. It discusses the need for automation testing to improve speed, reliability and test coverage. The document outlines when tests should be automated such as for regression testing or data-driven testing. It also discusses automation tool options and the process for automating tests. While automation testing provides benefits like time savings, it also has limitations such as the need for programming skills and maintenance of test code. Key challenges of automation testing include unrealistic expectations of tools and dependency on third party integrations.
This document discusses automated testing in Drupal using SimpleTest, a PHP testing framework. It provides an overview of what SimpleTest is, why automated testing is important, how to install SimpleTest, how tests are structured and run, and the various assertions and helper functions available. Automated testing helps ensure code works as intended, makes development and refactoring easier, and prevents regressions.
The document discusses different types of automated testing methodologies in Drupal 8 including unit testing, kernel testing, and functional testing. It provides details on how to register and run tests using PHPUnit and describes best practices for mocking dependencies and configurations.
This document discusses different types of software testing including unit testing and integration testing. Unit testing involves writing code to test individual units or modules in isolation with few dependencies. Integration testing evaluates how modules function together as a group in a black box manner. The benefits of testing include facilitating code changes, enhancing software quality by catching bugs, serving as documentation, and improving code quality. Test-driven development is introduced as an approach where tests are written before code to drive the design. Continuous integration is also discussed as a way to fully automate testing, build, and code quality checks.
Principles and patterns for test driven developmentStephen Fuqua
Developed to help introduce key topics in Test Driven Development, for new and veteran developers alike. Some examples are language-specific (C# / MSTest / Moq), but the principles apply to any object oriented language.
Java 201 Intro to Test Driven Development in Javaagorolabs
This document provides an overview and agenda for a Java 201 course on test-driven development (TDD) in Java. It introduces TDD workflows and concepts like the feedback loop and test frameworks. The agenda covers setting up an IDE, writing the first unit test, test anatomy, and concludes with a hands-on exercise to build components of a card game using TDD.
The document introduces automated testing in Drupal, noting that SimpleTest is the PHP testing framework integrated into Drupal core to write automated tests. Automated testing is important as it defines expected code behavior, makes development and refactoring easier, and helps prevent regressions by verifying code changes don't break existing functionality. Tests are written as PHP files containing test classes that extend SimpleTest and use helper methods to simulate user actions and check site behavior.
The document introduces automated testing in Drupal, noting that SimpleTest is the PHP testing framework integrated into Drupal core to write automated tests. Automated testing is important as it defines expected code behavior, makes development and refactoring easier, and helps prevent regressions by verifying code changes don't break existing functionality. Tests are written as PHP files containing test classes that extend SimpleTest and use helper methods to simulate user actions and check site behavior.
This document discusses strategies for managing a Drupal project across multiple environments like development, testing, and production. It recommends using source control to manage code changes, taking database snapshots to migrate data between environments, and programmatically applying administrative changes through custom modules to keep all environments in sync. This systematic approach helps ensure seamless code integration, efficient database changes, and minimal downtime when updating production.
Breaking Dependencies To Allow Unit Testing - Steve Smith | FalafelCON 2014FalafelSoftware
Unit testing software can be difficult, especially when the software wasn't designed to be testable. Dependencies on infrastructure concerns and software we don't control are one of the biggest contributors to testing difficulty. In this session, you'll learn the difference between unit tests and other kinds of tests, how to recognize and invert dependencies, and how to unit test your code's interactions with these dependencies without testing the infrastructure itself.
Breaking Dependencies to Allow Unit TestingSteven Smith
Unit testing software can be difficult, especially when the software wasn't designed to be testable. Dependencies on infrastructure concerns and software we don't control are one of the biggest contributors to testing difficulty. In this session, you'll learn the difference between unit tests and other kinds of tests, how to recognize and invert dependencies, and how to unit test your code's interactions with these dependencies without testing the infrastructure itself.
Presented at FalafelCON 2014, San Francisco, September 2014
Generalization in Auto-Testing. How we put what we had into new Technological...SQALab
This document discusses generalization in test automation and the XML2Selenium test automation platform. It provides an overview of the platform's architecture, including its use of an XML domain-specific language to define tests, contexts to decouple entities, plugins for extensibility, and support for techniques like polymorphism, page objects, and data-driven testing. The document also covers how the platform has evolved over time and can be used to test itself.
Breaking Dependencies to Allow Unit TestingSteven Smith
Unit testing software can be difficult, especially when the software wasn't designed to be testable. Dependencies on infrastructure concerns and software we don't control are one of the biggest contributors to testing difficulty. In this session, you'll learn the difference between unit tests and other kinds of tests, how to recognize and invert dependencies, and how to unit test your code's interactions with these dependencies without testing the infrastructure itself.
This document discusses test-driven development with JavaFX. It covers testing JavaFX applications at the unit, integration, and system levels. It also discusses continuous integration/continuous delivery and different tools that can be used for testing JavaFX applications, including TestFX, MarvinFX, JemmyFX, and Automaton. TestFX is highlighted as the recommended tool, with details provided on how to interact with JavaFX applications using its fluent API. The document also discusses using the view object pattern to write more readable tests and testing JavaFX applications that use DataFX or Afterburner.fx frameworks. It provides an example of using CDI to inject mocks when testing.
QtTest is a unit testing framework that comes with Qt. It provides classes and methods for building test fixtures to test units of code. QtTest allows testing signals without needing to write slots using QSignalSpy. It can also simulate user events like mouse clicks and key presses. While QtTest is useful for unit testing, it does not provide isolation of dependencies between units. Tests using QtTest should focus on testing one thing at a time and creating new objects for each test to isolate the code being tested.
JUnit is a unit testing framework for Java programming language. It was originally written by Kent Beck and Erich Gamma. Some key points:
- JUnit was one of the first unit testing frameworks for Java and has become the de facto standard.
- It allows writing and running repeatable tests to help find and prevent bugs. Tests are written in plain Java classes and methods.
- JUnit provides annotations like @Test, @Before, @After to identify test methods and set up/tear down methods.
- It includes assertions for validations and will report failures immediately. Tests can be organized into test suites.
Into The Box 2018 | Assert control over your legacy applicationsOrtus Solutions, Corp
This document discusses how to add tests to legacy code using the TestBox testing framework for ColdFusion applications. It defines unit testing and different types of tests like unit, integration, and end-to-end tests. TestBox allows writing automated tests for ColdFusion code and isolating dependencies using test doubles. The document outlines best practices for test-driven development including writing tests before code, using life cycle methods, and refactoring code without changing external behavior. Implementing a testing framework like TestBox helps assert control over legacy applications by adding tests and the ability to refactor code safely.
Infinum Android Talks #17 - Testing your Android applications by Ivan KustInfinum
van will show you how to setup unit tests using Robolectric, how to test user interactions using Espresso and provide some tips and tricks for stress-free testing.
This document provides an overview of Apache DeltaSpike, an open source CDI extension library. It discusses DeltaSpike's history and goals of being portable, reusable, and collecting best practices. Key features covered include core CDI capabilities like scopes, testing support, JSF integration for navigation and scopes, and additional modules. The document demonstrates examples and advantages of using DeltaSpike for testing CDI applications and customizing CDI metadata. It aims to close gaps in the CDI specification and provide a solid base for other extensions.
The slides or my talk at SoftShake (soft-shake 2013) about unit test maintenance.
The code can be found here : https://github.com/tsimbalar/UnitTestsMaintenanceHell
Abstract (in French, sorry ...) :
“Oui, les tests unitaires, c’est cool, mais à chaque fois que je fais une petite modification dans le code, je dois réparer tous mes tests, c’est pénible et ça me prend un temps fou! Alors j’hésite à laisser tomber ma suite de tests, ou alors je fais l’impasse sur ce refactoring …”
Vous avez déjà entendu ça, non ? Quand on se met à écrire des tests unitaires (que ce soit avant ou après le code à tester), au début, c’est la galère, puis, peu à peu, avec l’habitude et l’expérience, on les écrit mieux et plus rapidement … mais quand il s’agit de modifier du code existant, on a quand même l’impression de se prendre les pieds dans les tests.
Dans cette session, je vous présenterai quelques techniques, trucs et outils pour écrire des tests plus maintenables et vous sentir moins gênés lors des refactorings du code. Cette présentation se basera sur des exemples de code et des démos (en C# et avec Visual Studio)
Les tests apportent une forte valeur ajoutée à nos projets, ce qu’il faut, c’est essayer d’en réduire le coût…
Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo
From predictive maintenance to robotic automation, AI is driving the future of manufacturing. But without high-quality annotated data, even the smartest models fall short.
Discover how data annotation services are powering accuracy, safety, and efficiency in AI-driven manufacturing systems.
Precision in data labeling = Precision on the production floor.
Buckeye Dreamin 2024: Assessing and Resolving Technical DebtLynda Kane
Slide Deck from Buckeye Dreamin' 2024 presentation Assessing and Resolving Technical Debt. Focused on identifying technical debt in Salesforce and working towards resolving it.
TrustArc Webinar: Consumer Expectations vs Corporate Realities on Data Broker...TrustArc
Most consumers believe they’re making informed decisions about their personal data—adjusting privacy settings, blocking trackers, and opting out where they can. However, our new research reveals that while awareness is high, taking meaningful action is still lacking. On the corporate side, many organizations report strong policies for managing third-party data and consumer consent yet fall short when it comes to consistency, accountability and transparency.
This session will explore the research findings from TrustArc’s Privacy Pulse Survey, examining consumer attitudes toward personal data collection and practical suggestions for corporate practices around purchasing third-party data.
Attendees will learn:
- Consumer awareness around data brokers and what consumers are doing to limit data collection
- How businesses assess third-party vendors and their consent management operations
- Where business preparedness needs improvement
- What these trends mean for the future of privacy governance and public trust
This discussion is essential for privacy, risk, and compliance professionals who want to ground their strategies in current data and prepare for what’s next in the privacy landscape.
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Technology Trends in 2025: AI and Big Data AnalyticsInData Labs
At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including:
-Artificial Intelligence Market Overview
-Strategies for AI Adoption in 2025
-Anticipated drivers of AI adoption and transformative technologies
-Benefits of AI and Big data for your business
-Tips on how to prepare your business for innovation
-AI and data privacy: Strategies for securing data privacy in AI models, etc.
Download your free copy nowand implement the key findings to improve your business.
Automation Hour 1/28/2022: Capture User Feedback from AnywhereLynda Kane
Slide Deck from Automation Hour 1/28/2022 presentation Capture User Feedback from Anywhere presenting setting up a Custom Object and Flow to collection User Feedback in Dynamic Pages and schedule a report to act on that feedback regularly.
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
Procurement Insights Cost To Value Guide.pptxJon Hansen
Procurement Insights integrated Historic Procurement Industry Archives, serves as a powerful complement — not a competitor — to other procurement industry firms. It fills critical gaps in depth, agility, and contextual insight that most traditional analyst and association models overlook.
Learn more about this value- driven proprietary service offering here.
Mobile App Development Company in Saudi ArabiaSteve Jonas
EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
What is Model Context Protocol(MCP) - The new technology for communication bw...Vishnu Singh Chundawat
The MCP (Model Context Protocol) is a framework designed to manage context and interaction within complex systems. This SlideShare presentation will provide a detailed overview of the MCP Model, its applications, and how it plays a crucial role in improving communication and decision-making in distributed systems. We will explore the key concepts behind the protocol, including the importance of context, data management, and how this model enhances system adaptability and responsiveness. Ideal for software developers, system architects, and IT professionals, this presentation will offer valuable insights into how the MCP Model can streamline workflows, improve efficiency, and create more intuitive systems for a wide range of use cases.
"Client Partnership — the Path to Exponential Growth for Companies Sized 50-5...Fwdays
Why the "more leads, more sales" approach is not a silver bullet for a company.
Common symptoms of an ineffective Client Partnership (CP).
Key reasons why CP fails.
Step-by-step roadmap for building this function (processes, roles, metrics).
Business outcomes of CP implementation based on examples of companies sized 50-500.
2. • @jaypan on Drupal.org and drupal.stackexchange.com
• Acquia certified Drupal Grandmaster
• Acquia Certified Developer
• Acquia Certified Developer – Front end specialist
• Acquia Certified Developer – Back end specialist
• Living in Japan since 2000
• Working with Drupal since 2007
• Currently writing a book on coding in Drupal
JAY FRIENDLY
3. JAY FRIENDLY
• Owner of Jaypan in Yokohama
• English https://www.jaypan.com
• Japanese https://www.jaypan.jp
• Jaypan specializes in
• high-performance AJAX/JavaScript heavy applications
• Custom module/theme development
• App Development
• Drupal integration with outside technologies
• JavaScript libraries
• Linux programs
• Multiple Drupal instances
• Technical consulting and translation (Japanese/English)
4. OVERVIEW
• Manual testing vs. automated testing
• How do automated tests work?
• History of testing in Drupal
• Types of automated tests in Drupal
• Discussion of each type of test
5. MANUAL TESTING
• Developer creates new functionality
• Developer tests new functionality
• Other people (client, team-members) test new functionality
• Found bugs are fixed
6. PROBLEMS WITH MANUAL TESTING
• Complex functionality requires significant time to test
• Easy to forget to test a given scenario
• Changes to code require re-testing every scenario after any change
• Changes to code in other parts of the system may break functionality
without anyone realizing it
• As time passes, the conditions that need to be tested may be
forgotten or lost as project members change
• Coming back to code after a period of time means the developer is
not sure what is required
7. AUTOMATED TESTING
• Developer creates automated tests that run through all testing
scenarios
• Testing system runs all tests and reports any behaviors that are
unexpected (aka bugs)
8. BENEFITS OF AUTOMATED TESTING
• Removes requirement of getting humans to test after each code
change
• Tests are faster than human testing
• Tests for the whole system can be run before committing any code to
a production server, to ensure new functionality in one part of the
system hasn’t broken functionality in another part of the system.
• If (or rather, when) new bugs are found, tests can be added along
with the fix, to ensure that the bug never arises again
• Overall system is more stable.
9. PROBLEMS WITH AUTOMATED TESTING
• Writing tests takes a significant amount of time
• Extra time means extra development resources
• Clients cannot directly see tests, and may not understand the extra costs
in developing when there is nothing for them to look at.
• Tests only test what they have been written to test. If the developer does
not think to test something, a bug could exist. The existence of automated
tests in such a case may create a false sense of security
• Tests happen in a virtual (non-visible) environment, making them hard to
debug (and even create)
• Changing code often requires re-writing old tests
10. OR IN OTHER WORDS, WHAT IS IT?
HOW DOES A TEST WORK?
• A test will first run some code, then do ‘assertions’ to ensure that the results work as
expected.
• For example, in Drupal 8, nodes have a method id(), that returns the ID of the
node.
• An automated test would create a new node object, then run $node->id() and test:
• Was a result returned?
• Is the result an integer?
• Is the returned ID the expected (correct) ID of the node that was created?
• Any failed assertions result in a failed test, telling the developer exactly what failed,
so they know what to fix.
11. AUTOMATED TESTING IN DRUPAL - HISTORY
• Drupal 6 - Simpletest module
• Drupal 7 - Simpletest in core
• Drupal 8 -
• Simpletest still in core but deprecated (to be removed in D9)
• PHPUnit introduced into core
• This presentation focuses exclusively on PHPUnit
12. TYPES OF TESTS IN DRUPAL 8
• Unit tests
• Kernel tests
• Functional tests
• JavaScript functional tests
13. UNIT TESTS IN DRUPAL 8
• A Unit is a PHP Class (OOP)
• Unit tests test code, not user-end functionality
• Unit tests will test a single class, to ensure that it behaves as
expected
• Unit tests test the unit (class) alone in isolation, rather than in the
Drupal environment
• Unit testing is very fast, as the system does not need to be
bootstrapped to run the tests
14. UNIT TEST OVERVIEW
• Unit tests go in [MODULENAME]/tests/src/Unit
• Namespace is DrupalTests[MODULENAME]Unit
• Unit tests extend DrupalTestsUnitTestCase
• Annotation should include (at a minimum)
• Class:
• @coversDefaultClass - the class the the test is testing
• @group - the group of tests (usually module name) that the test belongs to
• Method:
• @covers - the method of the class being tested
15. UNIT TEST OVERVIEW
• Testing class name should end in ‘Test’
• Example: the test for the Person class would be PersonTest
• Testing method (function) name must start with ‘test’
• Example: the method used to test the id() method of Person
would be testId()
16. UNIT TEST OVERVIEW
• Test classes can have a setUp() method, that sets up
conditions/objects before each test in the test class.
• Useful when all tests require a similar set of conditions
• Test classes can also have a tearDown() method, where objects
are cleaned up after each test in the test class.
• Test classes can have a setUpBeforeClass() method that is run
once before the test class is run
• Test classes can have a tearDownAfterClass() method that is run
once after the test class is finished
17. CLASS (UNIT) TO BE TESTED
class Person {
private $id;
public function setId($id) {
$this->id = $id;
}
public function id() {
return $this->id();
}
}
18. EXAMPLE UNIT TEST FOR PERSON CLASS
/**
* @coversDefaultClass Person
*/
class PersonTest {
/**
* @covers ::setId()
*/
public testId() {
$person = new Person();
$person->setId(123);
$this->assertNotNull($person->getId(), ‘A value is returned for the ID’);
$this->assertTrue(is_int($person->id()), ‘The returned ID is an
integer’);
$this->assertEqual(123, $this->getId(), ‘The returned ID is correct’);
}
}
19. KERNEL TESTS
KERNEL TESTS IN DRUPAL 8
• Kernel tests are executed in a limited Drupal environment, similar to
the environment before Drupal has been installed
• Tests can access files and the database
• Declared modules are loaded, but not installed. Installation needs
to be done manually
• Kernel tests are slower than Unit tests, but faster than Functional
tests
20. KERNEL TEST OVERVIEW
• Kernel tests go in [MODULENAME]/tests/src/Kernel
• Namespace is DrupalTests[MODULENAME]Kernel
• Kernel tests extend DrupalTestsKernelTestBast
22. FUNCTIONAL TESTS IN DRUPAL 8
• Functional tests work in an installed Drupal environment
• Modules needed for the test need to be declared for installation
• Tests use an internal browser (aka code - not visual), where links
can be clicked, form elements can be filled out, and pages can be
requested, all programmatically
• Users, roles, blocks and other entities can be created
• Tests are slow as each test builds a new Drupal instance in which
to run the tests
23. FUNCTIONAL TEST OVERVIEW
• Unit tests go in [MODULENAME]/tests/src/Functional
• Namespace is DrupalTests[MODULENAME]Functional
• Unit tests extend DrupalTestsBrowserTestBase
• Annotation should include (at a minimum)
• Class:
• @group - the group of tests (usually module name) that the test belongs to
• $modules property contains an array of modules to be installed
• example: $modules = [‘node’, ‘restrict_ip’]
24. FROM RESTRICT IP MODULE
EXAMPLE FUNCTIONAL TEST
namespace DrupalTestsrestrict_ipFunctional;
/**
* @group restrict_ip
*/
class RestrictIpAccessTest extends RestrictIpBrowserTestBase // which extends BrowserTestBase
{
protected static $modules = ['restrict_ip', 'node'];
/**
* Test that a user is blocked when the module is enabled
*/
public function testModuleEnabled()
{
$adminUser = $this->drupalCreateUser(['administer restricted ip addresses', 'access administration
pages', 'administer modules']);
$this->drupalLogin($adminUser);
$this->drupalGet('admin/config/people/restrict_ip');
$this->assertStatusCodeEquals(200);
$this->checkCheckbox('#edit-enable');
$this->click('#edit-submit');
$this->assertSession()->pageTextContains('The page you are trying to access cannot be accessed from
your IP address.');
}
}
25. JAVASCRIPT FUNCTIONAL TESTS IN DRUPAL
8
• JavaScript Functional tests work in a fully installed Drupal
environment
• Extends Functional tests to include JavaScript
• Require PhantomJS browser
• Virtual browser (no GUI)
• Requires installation on testing computer/server
• Can test #ajax methods, as well as other JavaScript functionality
26. JAVASCRIPT FUNCTIONAL TEST OVERVIEW
• Unit tests go in [MODULENAME]/tests/src/FunctionalJavascript
• Namespace is
DrupalTests[MODULENAME]FunctionalJavascript
• Unit tests extend
DrupalFunctionalJavascriptTestsJavascriptTestBase
• Annotation should include (at a minimum)
• Class:
• @group - the group of tests (usually module name) that the test
belongs to
27. FROM FANCY LOGIN MODULE
EXAMPLE JAVASCRIPT FUNCTIONAL TEST
namespace DrupalTestsfancy_loginFunctionalJavascript;
/**
* @group fancy_login
*/
class FancyLoginJavascriptTest extends FancyLoginJavascriptTestBase // which extends JavascriptTestBase
{
public function testDimmerBackgroundColor()
{
$this->openPopup();
$this->assertJsCondition('jQuery("#fancy_login_dim_screen").not(":animated")');
$this->assertJsCondition('jQuery("#fancy-login-user-login-form").not(":animated")');
$script = $this->createScriptString('#fancy_login_dim_screen', 'background-color', 'rgb(255, 255,
255)');
if(!$this->getSession()->evaluateScript($script))
{
throw new Exception('background-color is not white');
}
// …
}
}
28. WRITE TESTS THEN CODE
TESTING DRIVEN DEVELOPMENT
• Write tests as the start of the project, rather than the end
• Developers build and test as they go
• Gives a clear indicator of what to do next
• Gives a clear indication of when the project is complete
• Note - testing driven development is purely theoretical for me - I’ve
never done it
29. WHERE TO GO FROM HERE
• PHPUnit in Drupal 8 - https://www.drupal.org/docs/8/phpunit
• Documentation is limited
• Lots and lots of Googling