Site Reliability Engineering (SRE) is a discipline that incorporates aspects of software engineering and applies them to infrastructure and operations problems to create scalable and highly reliable software systems. Crucial for maintaining the reliability and efficiency of complex software systems.
The ability of a system to maintain its state and data across sessions, ensuring continuity and consistency in user experience. Crucial for designing reliable and user-friendly systems that retain data and settings across interactions.
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.
The capability of a system to continue operating properly in the event of the failure of some of its components, ensuring that user experience is not significantly affected by errors or issues, similar to Postel's Law. Essential for designing reliable and resilient systems, such as a form that normalizes user input for compatibility rather than returning an error (e.g., unconstrained phone number format).
The process of anticipating, detecting, and resolving errors in software or systems to ensure smooth operation. Important for creating reliable and user-friendly software applications.
A principle stating that a system should be liberal in what it accepts and conservative in what it sends, meaning it should handle user input flexibly while providing clear, consistent output, similar to the principle of fault tolerance. Essential for designing robust and user-friendly interfaces that accommodate a wide range of user inputs and behaviors while maintaining reliability and clarity in responses.
A bias that occurs when the sample chosen for a study or survey is not representative of the population being studied, affecting the validity of the results. Important for ensuring the accuracy and reliability of research findings and avoiding skewed data.
The practice of managing and resolving incidents that disrupt normal operations, ensuring minimal impact on business activities. Essential for maintaining service reliability and managing operational disruptions effectively.
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.
In AI, the generation of incorrect or nonsensical information by a model, particularly in natural language processing. Important for understanding and mitigating errors in AI systems.
A bias that occurs when researchers' expectations influence the outcome of a study. Crucial for designing research methods that ensure objectivity and reliability.
A method used in AI and machine learning to ensure prompts and inputs are designed to produce the desired outcomes. Essential for improving the accuracy and relevance of AI responses.
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.
A type of bias that occurs when the observer's expectations or beliefs influence their interpretation of what they are observing, including experimental outcomes. Essential for ensuring the accuracy and reliability of research and data collection.
The hardware and software environment used to deploy and manage applications and services. Essential for ensuring reliable and scalable application deployment.
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.
The process of identifying, assessing, and mitigating potential threats that could impact the success of a digital product, including usability issues, technical failures, and user data security. Essential for maintaining product reliability, user satisfaction, and data protection, while minimizing the impact of potential design and development challenges.
A type of software testing that ensures that recent changes have not adversely affected existing features. Essential for maintaining software quality and reliability.
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.
A risk management model that illustrates how multiple layers of defense (like slices of Swiss cheese) can prevent failures, despite each layer having its own weaknesses. Crucial for understanding and mitigating risks in complex systems.
A design principle that ensures a system continues to function at a reduced level rather than completely failing when some part of it goes wrong. Crucial for enhancing system reliability and user experience in adverse conditions.
Data points that differ significantly from other observations and may indicate variability in a measurement, experimental errors, or novelty. Crucial for identifying anomalies and ensuring the accuracy and reliability of data in digital product design.
The quality of being uniform and coherent across different elements and touchpoints in design. Crucial for creating predictable and reliable user experiences.
Human in the Loop (HITL) integrates human judgment into the decision-making process of AI systems. Crucial for ensuring AI reliability and alignment with human values.
Application Support Engineer (ASE) is a professional responsible for maintaining and supporting software applications, ensuring their availability and performance. Crucial for ensuring the reliability and user satisfaction of digital products through effective support and maintenance.
The process of combining different systems or components in a way that ensures they work together smoothly and efficiently without disruptions. Essential for providing a cohesive user experience and ensuring the reliability of complex systems.
A Service Level Agreement (SLA) is a formal contract between a service provider and a customer that defines the level of service expected. Essential for setting clear expectations and responsibilities, ensuring quality and reliability.
Garbage In-Garbage Out (GIGO) is a principle stating that the quality of output is determined by the quality of the input, especially in computing and data processing. Crucial for ensuring accurate and reliable data inputs in design and decision-making processes.
A Japanese term for "mistake-proofing," referring to any mechanism or process that helps prevent errors by design. Crucial for designing systems and processes that minimize the risk of human error.
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.
A software development practice where code changes are automatically prepared for a release to production. Crucial for ensuring rapid and reliable deployment of updates.
The setting where software and systems are actually put into operation for their intended use. Essential for ensuring that products are fully functional and meet user requirements in a real-world setting.
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.
Application Release Automation (ARA) is the process of automating the release of applications, ensuring consistency and reducing errors. Crucial for accelerating the delivery of software updates and maintaining high-quality digital products.
The process of maintaining, updating, and improving a product or system after its initial deployment to ensure its continued functionality, performance, and relevance to users. Crucial for ensuring long-term user satisfaction, product reliability, and adaptation to changing user needs and technological advancements.
ModelOps (Model Operations) is a set of practices for deploying, monitoring, and maintaining machine learning models in production environments. Crucial for ensuring the reliability, scalability, and performance of AI systems throughout their lifecycle, bridging the gap between model development and operational implementation.
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.
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.
A range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter. Essential for making inferences about population parameters and understanding the precision of estimates in product design analysis.
Retrieval-Augmented Generation (RAG) is an AI approach that combines retrieval of relevant documents with generative models to produce accurate and contextually relevant responses. Essential for improving the accuracy and reliability of AI-generated content.
Artificially generated data that mimics real data, used for training machine learning models. Crucial for training models when real data is scarce or sensitive.
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.
An experimental design where different groups of participants are exposed to different conditions, allowing for comparison between groups. Important for understanding and applying different experimental designs in user research.
The tendency for individuals to present themselves in a favorable light by overreporting good behavior and underreporting bad behavior in surveys or research. Crucial for designing research methods that mitigate biases and obtain accurate data.
Numeronym for the word "Observability" (O + 11 letters + N), the ability to observe the internal states of a system based on its external outputs, facilitating troubleshooting and performance optimization. Crucial for monitoring and understanding system performance and behavior.
The high-level structure of a software application, defining its components and their interactions. Essential for designing robust, scalable, and maintainable digital products.
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.
The process of identifying unusual patterns or outliers in data that do not conform to expected behavior. Crucial for detecting fraud, errors, or other significant deviations in various contexts.
A logical fallacy where anecdotal evidence is used to make a broad generalization. Crucial for improving critical thinking and avoiding misleading conclusions.
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.
A non-production environment used for development and testing before deployment to production. Important for ensuring that changes are thoroughly tested before going live.
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.
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.
A set of fundamental principles and guidelines that inform and shape user research practices. Crucial for maintaining consistency and ensuring high-quality user insights.
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.
A tendency for respondents to answer questions in a manner that is not truthful or accurate, often influenced by social desirability or survey design. Important for understanding and mitigating biases in survey and research data.
The process of handling changes to software, hardware, or documentation in a systematic way. Critical for maintaining consistency and ensuring system integrity.
Trust, Risk, and Security Management (TRiSM) is a framework for managing the trust, risk, and security of AI systems to ensure they are safe, reliable, and ethical. Essential for ensuring the responsible deployment and management of AI technologies.
An environment closer to production where final testing and validation occur. Crucial for ensuring that products are ready for production deployment.
The process of self-examination and adaptation in AI systems, where models evaluate and improve their own outputs or behaviors based on feedback. Crucial for enhancing the performance and reliability of AI-driven design solutions by fostering continuous learning and improvement.