Test Environment
An environment used for testing software to identify issues and ensure quality before production deployment. Important for detecting and fixing bugs to ensure the software's reliability and performance.
42 topics found for:
“reliability testing”
Burn-In
The process of running a system for an extended period to detect early failures and ensure reliability. Important for ensuring the stability and performance of digital products before full-scale deployment.
End-To-End Testing
A testing methodology that verifies the complete workflow of an application from start to finish, ensuring all components work together as expected. Important for ensuring the reliability and performance of digital products, leading to better user satisfaction and fewer post-launch issues.
Soak Testing
A performance testing method that evaluates the system's behavior and stability over an extended period under a high load. Essential for identifying memory leaks and ensuring the reliability and performance of digital products under prolonged use.
Black Box Testing
A testing method where the internal structure of the system is not known to the tester, focusing solely on input and output. Essential for validating the functionality of digital products from an end-user perspective.
UAT
User Acceptance Testing (UAT) is the final phase of the software testing process where actual users test the software to ensure it meets their requirements. Crucial for validating that the software functions correctly in real-world scenarios before its release.
Staging Environment
An environment that replicates the production environment, used for final testing before deployment. Crucial for ensuring that digital products are thoroughly tested and perform as expected before going live.
A/A Testing
A method of testing two identical versions of a webpage or app to ensure the accuracy of the testing tool. Important for validating the effectiveness of A/B testing tools and processes.
Alpha Test
A preliminary testing phase conducted by internal staff to identify bugs before releasing the product to external testers or customers. Crucial for ensuring product quality and functionality before broader release.
Smoke Testing
A preliminary testing method to check whether the most crucial functions of a software application work, without going into finer details. Important for identifying major issues early in the development process and ensuring the stability of digital products.
Automated Testing
The use of software tools to run tests on code automatically, ensuring functionality and identifying defects without manual intervention. Crucial for maintaining high code quality and efficiency in the development process.
White Box Testing
A testing method that examines the internal structure, design, and coding of a software application to verify its functionality. Essential for ensuring the correctness and efficiency of the code in digital product development.
Shift-Right Testing
A practice of performing testing activities in the production environment to monitor and validate the behavior and performance of software in real-world conditions. Crucial for ensuring the stability, reliability, and user satisfaction of digital products in a live environment.
Shift-Left Testing
A practice of performing testing activities earlier in the software development lifecycle to identify and address issues sooner. Essential for improving software quality, reducing defects, and accelerating development cycles in digital product design.
Regression Testing
A type of software testing that ensures that recent changes have not adversely affected existing features. Essential for maintaining software quality and reliability.
Canary Testing
A deployment strategy where a new version is released to a small subset of users to detect any issues before a full rollout. Crucial for minimizing risk and ensuring the stability of digital products during updates and deployments.
TDD
Test-Driven Development (TDD) is a software development methodology where tests are written before the code that needs to pass them. Essential for ensuring high code quality and reducing bugs.
Lower Environment
A non-production environment used for development and testing before deployment to production. Important for ensuring that changes are thoroughly tested before going live.
Static Testing
A testing method that examines the code, documentation, and requirements without executing the program. Important for identifying defects early in the development lifecycle, improving the quality and reducing the cost of digital products.
Higher Environment
An environment closer to production where final testing and validation occur. Crucial for ensuring that products are ready for production deployment.
Look Elsewhere Effect
A statistical phenomenon where a large number of hypotheses are tested, increasing the chance of a rare event being observed. Crucial for understanding and avoiding false positives in data analysis.
Continuous Integration
A software development practice where code changes are frequently integrated into a shared repository, with each change being verified by automated tests. Essential for catching errors early and improving the quality of software.
Dev Environment
A development environment where software is created and modified. Crucial for allowing developers to build and experiment with new features.
Repeated Measures Design
A research design where the same participants are used in all conditions of an experiment, allowing for the comparison of different conditions within the same group. Essential for reducing variability and improving the reliability of experimental results.
Build Server
A server dedicated to automating the process of building and compiling code, running tests, and generating software artifacts. Crucial for ensuring continuous integration and maintaining the integrity of the codebase in digital product development.
Central Limit Theorem
A statistical theory that states that the distribution of sample means approximates a normal distribution as the sample size becomes larger, regardless of the population's distribution. Important for making inferences about population parameters and ensuring the validity of statistical tests in digital product design.
Train/Test
A method of splitting a dataset into two subsets: one for training a model and another for testing its performance. Fundamental for developing and evaluating machine learning models in digital product design.
Content Validity
The extent to which a measure represents all facets of a given construct, ensuring the content covers all relevant aspects. Important for ensuring that assessments and content accurately reflect the intended subject matter.
Release Management
The process of overseeing and coordinating the development, testing, and deployment of software releases to ensure they are delivered efficiently and effectively. Essential for managing software development cycles and ensuring successful product releases.
Matched Pairs Design
An experimental design where subjects are paired based on certain characteristics, and then one is assigned to the treatment and the other to the control group. Important for reducing variability and improving the accuracy of experimental results.
Continuous Deployment
A software development practice where code changes are automatically deployed to production without manual intervention. Important for maintaining a high level of productivity and quality in software development.
Blue/Green Deployment
A deployment strategy that reduces downtime and risk by running two identical production environments, switching traffic between them. Crucial for ensuring seamless updates and minimizing disruptions in digital product deployment.
Synthetic Data
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
Standard Deviation
A statistical measure that quantifies the amount of variation or dispersion of a set of data values. Essential for understanding data spread and variability, which helps in making informed decisions in product design and analysis.
Empirical Rule
Also known as the 68-95-99.7 Rule, it states that for a normal distribution, nearly all data will fall within three standard deviations of the mean. Important for understanding the distribution of data and making predictions about data behavior in digital product design.
Instrumentation
The process of designing, developing, and managing tools and techniques for measuring performance and collecting data. Essential for monitoring and improving system performance and user experience.
Scaling Fallacy
A cognitive bias where people underestimate the complexity and challenges involved in scaling systems, processes, or businesses. Important for understanding the difficulties of scaling and designing systems that address these challenges.
Release
The distribution of a new or updated software product to users. Important for delivering new features, improvements, and fixes to users, ensuring continuous enhancement of the product.
Courtesy Bias
The tendency for individuals to give positive responses or feedback out of politeness, regardless of their true feelings. Crucial for obtaining honest and accurate user feedback.
Error Prevention
Design strategies aimed at preventing user errors before they occur. Crucial for enhancing usability and ensuring a smooth user experience.
POUR
Perceivable, Operable, Understandable, and Robust (POUR) are the four main principles of web accessibility. These principles are essential for creating inclusive digital experiences that can be accessed and used by people with a wide range of abilities and disabilities.
Data Distribution
The spread and pattern of data values in a dataset, often visualized through graphs or statistical measures. Critical for understanding the characteristics of data and informing appropriate analysis techniques in digital product development.