Common Data Science Terminology – SolveForce Fiber Internet, Cloud Computing & Telecommunications

5G: The fifth generation of mobile networks, characterized by faster speeds, lower latency, and support for more devices and new use cases such as IoT and Industry 4.0.
A/B testing: A method for comparing two or more versions of a software feature or user interface to determine which performs better.
A/B testing: A technique for comparing two versions of a software product to determine which one performs better.
A/B testing: A type of testing that is used to test different versions of a software application by randomly assigning users to different groups and comparing the results.
Acceptance Test-Driven Development (ATDD): A software development methodology that emphasizes collaboration between developers, QA, and non-technical stakeholders, and uses natural language to define acceptance tests.
Acceptance testing: A type of testing that is used to determine whether a software application is acceptable for release.
Acceptance testing: The process of testing a software application to ensure it meets the requirements and is ready for production.
Acceptance testing: The process of testing a software application to ensure it meets the requirements and is ready for release.
Accessibility testing: The process of testing software applications to ensure they can be used by people with disabilities.
Agile development: A set of principles and practices for software development that emphasizes flexibility, collaboration, and rapid iteration.
Agile development: A software development methodology that emphasizes flexibility, collaboration, and rapid iteration in response to changing requirements and feedback.
Alpha testing: A type of testing that is performed by internal employees or developers before a software application is released to external users.
Ansible: An open-source tool for configuration management and automation, used to automate the deployment and management of IT infrastructure.
API analytics: The practice of collecting and analyzing data about the usage and performance of an API, in order to gain insights and make informed decisions.
API automation: The use of technology to automate tasks that interact with application programming interfaces (APIs).
API design: The process of designing the structure and behavior of an API, with the goal of making it easy to use and understand.
API documentation: The process of creating documentation that describes the functionality and usage of an API.
API economy: The ecosystem of businesses, developers, and users that creates and consumes APIs to drive digital innovation and growth.
API gateway: A server that acts as an intermediary between an application and a set of microservices, and is responsible for tasks such as routing, authentication, and rate limiting.
API governance: The practice of managing and controlling the usage of APIs to meet an organization’s compliance, risk management, and business objectives.
API management platform: A set of tools and services that provide a unified platform for designing, publishing, documenting, and managing APIs.
API management: The process of designing, publishing, documenting, and managing APIs in a secure and scalable way.
API mocking: The process of simulating the behavior of an API, in order to test the functionality of an application that depends on it.
API monetization: The process of generating revenue from APIs by charging for access, usage, or other value-added services.
API monitoring: The practice of monitoring the performance and availability of an API, in order to detect and diagnose issues.
API performance: The measure of how well an API can handle requests, in terms of response time, throughput, and resource utilization.
API portal: A web-based interface that provides documentation, tools, and resources for developers to interact with an API.
API scalability: The ability of an API to handle increasing loads and traffic, by adding more resources or distributing the load across multiple servers.
API security gateway: A software or hardware gateway that sits between an API and its consumers, and is responsible for tasks such as authentication, authorization, and threat protection.
API security: The practice of securing APIs and the data that flows through them, against threats such as injection attacks, cross-site scripting, and unauthorized access.
API testing: The process of testing the functionality and performance of an API.
API versioning: The practice of making changes to an API while maintaining backward compatibility for existing clients.
API: A set of protocols, routines, and tools for building software and applications.
Appium: An open-source tool for automating mobile applications, used for mobile application testing.
Artificial Intelligence (AI): A branch of computer science that deals with the creation of intelligent machines that work and learn like humans.
Artificial Intelligence (AI): The simulation of human intelligence in machines that are programmed to think and learn like humans.
Artificial intelligence: The simulation of human intelligence in machines that are programmed to think and learn like humans.
As previously mentioned, this list is not exhaustive and there may be more terms used in the context of software automation depending on the specific area or industry.
Authentication: The process of verifying the identity of a user, device, or system.
Authorization: The process of granting or denying access to a resource based on a user’s identity and privileges.
Automated testing: The practice of using tools and scripts to automate the execution of tests, in order to improve efficiency and reduce human error.
Automation framework: A set of tools, libraries, and conventions that are used to structure and organize automation code.
Automation libraries: A collection of pre-written code that can be used to perform common automation tasks.
Automation testing framework: A set of tools, libraries, and conventions that are used to structure and organize automation testing code.
Automation Testing Life Cycle (ATLC) : A series of phases that defines the process of automating the testing of software applications. This includes phases like planning, designing, executing, and reporting.
Automation testing: The practice of using tools to automate the execution of tests, in order to improve efficiency and reduce human error.
Automation testing: The process of using automation tools and scripts to test software applications.
Automation: The use of technology to perform a task without human intervention.
Autonomous systems: Systems that can operate independently of human intervention, using techniques from AI and control systems.
Auto-scaling: The process of automatically adjusting the number of servers running in a cluster in response to changes in traffic or workload.
AWS Lambda: A serverless compute service provided by Amazon Web Services (AWS) that runs the code in response to an event and automatically manages the compute resources.
Azure Functions: A serverless compute service provided by Microsoft Azure that enables developers to run event-triggered code in the cloud, without worrying about provisioning or managing infrastructure.
Backup: The process of creating copies of data and storing them in a separate location, as a safeguard against data loss.
Batch job: A program or script that is executed to perform a batch processing task.
Batch processing: The execution of a set of commands or instructions in a batch, without user interaction.
Batch processing: The practice of processing large amounts of data in batches, rather than one record at a time, in order to improve performance and resource utilization.
Bayesian Networks: A subset of Machine Learning that uses a probabilistic graphical model to represent uncertain knowledge.
Behavioral Driven Development (BDD): A software development methodology that emphasizes collaboration between developers, QA, and non-technical stakeholders, and uses natural language to define tests.
Behavior-driven development (BDD): A software development approach that emphasizes collaboration between developers, QA, and non-technical stakeholders to define the behavior of the application.
Behavior-driven development (BDD): A software development methodology that emphasizes the use of natural language in the development process to express the expected behavior of the system.
Beta testing: A type of testing that is performed by external users or customers before a software application is released to the general public.
Big Data: A term used to describe extremely large datasets that cannot be managed or analyzed using traditional methods.
Big Data: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and insights.
Bitbucket: A web-based version control repository management service for Git and Mercurial, with built-in issue tracking, wikis, and collaboration features.
Black-box testing: A type of testing that is used to test the external behavior of a software application, without testing its internal structure or design.
Blockchain: A distributed ledger technology that uses cryptography to secure and verify transactions across a network of computers.
Blue-green deployment: A technique for minimizing downtime during deployments by keeping a second version of the application running, and switching traffic over to it when the new version is ready.
Blue-green deployment: A technique for releasing new versions of a software product by maintaining two identical production environments, and switching traffic between them.
Blue-green deployment: A technique for releasing software changes, where two identical production environments are run in parallel, one for the live application (blue) and one for the updated application (green). The traffic is routed to the blue environment and after the new version is verified to be stable, it is routed to the green environment.
Bug: An error, flaw, failure, or fault in a software application that causes it to behave in unintended ways.
Bug: An error, flaw, failure, or fault in a software program or system that causes it to produce an incorrect or unexpected result, or to behave in unintended ways.
Build automation: The use of software tools to automate the process of building, packaging, and deploying software applications.
Bullet Point List all Software Automation Terminology and Related Definitions.
Business continuity planning (BCP): The process of identifying and mitigating potential risks to an organization’s operations, and ensuring that essential functions can continue during and after a disruption.
Business process automation: The use of technology to automate tasks that are part of a business process, such as invoicing, payroll, and customer service.
Canary deployment: A technique for releasing new versions of a software product to a small subset of users before releasing it to the entire user base.
Canary release: A technique for gradually rolling out a new software feature or change to a small subset of users before releasing it to the entire user base.
Canary releasing: A technique for releasing software changes, where a small subset of users are exposed to the new version of the application and the system’s response and performance are monitored before releasing to all users.
Canary testing: A type of testing that is used to test new features or changes in a software application by releasing them to a small subset of users before releasing them to the general public.
Cassandra: A highly-scalable, NoSQL database that is designed to handle large amounts of data across many commodity servers.
Chaos engineering: The practice of intentionally introducing controlled failure scenarios to test and improve the resilience of systems.
Chef: An open-source tool for configuration management and automation, similar to Ansible and Puppet but with a focus on the use of recipes to automate infrastructure.
CircleCI: A cloud-based continuous integration and delivery service that supports various programming languages and platforms.
Cloud automation: The use of technology to automate tasks related to cloud computing, such as provisioning, scaling, and monitoring.
Cloud auto-scaling: The process of automatically adjusting the number of virtual machines running in a cloud environment in response to changes in traffic or workload.
Cloud Backup: The process of creating copies of data and storing them on remote servers, as a safeguard against data loss.
Cloud backup: The process of making copies of data and storing them on remote servers, as a safeguard against data loss.
Cloud bursting: A technique used to dynamically allocate more resources from the cloud to handle sudden and unexpected increases in load or traffic.
Cloud compliance: The process of ensuring that an organization’s use of cloud services complies with relevant laws, regulations, and industry standards.
Cloud computing: A model of delivering computing resources and services over the internet on a pay-as-you-go basis.
Cloud computing: The delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.
Cloud computing: The practice of delivering computing resources, such as storage, processing power, and applications, over the internet, on-demand and scalable.
Cloud cost optimization: The practice of reducing the cost of cloud-based resources and services while maintaining or improving performance and availability.
Cloud disaster recovery (CDR): The process of restoring critical systems and data after a disaster or interruption using cloud-based services.
Cloud governance: The practice of managing and controlling the use of cloud-based resources and services to meet an organization’s compliance, risk management, and business objectives.
Cloud migration: The process of moving applications, data, and other resources from on-premises or other cloud-based environments to a different cloud environment.
Cloud migration: The process of moving data and applications from on-premises infrastructure to the cloud.
Cloud migration: The process of moving data, applications, and infrastructure from on-premises or other cloud environments to a new cloud environment.
Cloud providers: Companies that provide cloud computing services, such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and others.
Cloud security: The practice of securing cloud-based resources and services, including data, applications, and infrastructure.
Cloud security: The process of protecting data and resources in the cloud from unauthorized access, use, disclosure, disruption, modification, or destruction.
Cloud storage: The storing of data on remote servers, accessed over the internet.
Cloud testing: The process of testing software applications on cloud-based platforms and infrastructure.
Cloud-based virtualization: Using a cloud provider’s infrastructure to create and run virtual machines.
Cloud-native security: the practice of securing applications and services that are built and run in cloud environments.
Cloud-native: A software development practice that emphasizes the use of cloud-based platforms and technologies to build, test, and deploy software applications.
Cloud-native: The practice of building and running applications and services that are designed to fully leverage the benefits of cloud computing.
Clustering: A method of grouping multiple servers together to provide a single, high-availability service.
Code analysis: The process of analyzing and reviewing the code to identify potential issues or improvements.
Code analysis: The process of analyzing code for potential security vulnerabilities or other issues.
Code analysis: The process of analyzing code to identify issues and measure code quality.
Code complexity analysis tools: A software that helps in measuring the complexity of the code.
Code complexity analysis: The process of analyzing the code to measure its complexity and understand how easy or hard it is to maintain.
Code coverage tools: A software that helps in measuring the code coverage of a software application.
Code coverage: The measure of how much of the codebase is executed by automated tests.
Code coverage: The percentage of code that is executed during testing.
Code maintenance: The process of updating, modifying, and troubleshooting existing code to ensure its continued functionality.
Code modularity: The practice of breaking down code into smaller, independent, and reusable modules.
Code optimization: The process of making code more efficient in terms of performance and memory usage.
Code profiling tools: A software that helps in measuring and analyzing the performance of a software application.
Code profiling: The process of measuring and analyzing the performance of a software application.
Code quality tools: A software that helps in analyzing and measuring the code quality of a software application.
Code quality: A measure of how well-written, maintainable, and reliable a piece of software is.
Code quality: The level of adherence of the code to best practices, design patterns, and industry standards.
Code refactoring: The process of restructuring existing code to improve its quality, maintainability, or performance, without changing its behavior.
Code reuse: The ability to use existing code to perform similar tasks in different parts of a system.
Code review tools: A software that helps in automating the process of code review.
Code review: The process of reviewing and analyzing code by other developers to ensure it meets standards, is well-structured, and is free of errors.
Code review: The process of reviewing and evaluating code changes before they are integrated into a software application.
Code review: The process of reviewing and testing code changes before they are integrated into a shared repository.
Code review: The process of reviewing code to identify issues, improve code quality, and share knowledge among team members.
Code style analysis tools: A software that helps in analyzing the code to ensure it adheres to a specific coding style or standard.
Code style analysis: The process of analyzing the code to ensure it adheres to a specific coding style or standard.
CodeClimate: a cloud-based tool that analyzes the maintainability of your code, provides actionable insights, and helps you prioritize technical debt.
Cold start: The initial execution of a function after it has been idle for an extended period, which takes longer than a subsequent execution.
Compliance testing: The process of testing software applications to ensure they meet industry regulations and standards.
Computer Vision (CV): A subfield of AI that deals with the ability of computers to interpret and understand visual information from the world, such as images and videos.
Computer Vision (CV): A subset of AI that deals with the ability of computers to understand and interpret visual information from the world, such as images and videos.
Computer vision: A field of artificial intelligence that deals with the ability of computers to interpret and understand visual information from the world.
Conditional automation: A type of automation where tasks are executed based on specific conditions or inputs.
Configuration management: The practice of managing and maintaining the configuration of software systems throughout their lifecycle.
Configuration management: The process of maintaining consistent and predictable configurations across an organization’s IT infrastructure.
Connected cars: The use of IoT technology to connect vehicles to the internet, allowing for improved safety, navigation, and infotainment.
Consul: An open-source tool for service discovery, configuration, and orchestration that can be used as a service mesh.
Container orchestration: The process of managing and coordinating the deployment, scaling, and management of containers in a distributed environment.
Container: A lightweight, stand-alone executable package that includes everything needed to run a piece of software, including code, runtime, system tools, libraries, and settings.
Containerization: A method of packaging software applications and their dependencies together in a container, allowing them to be easily deployed and run on any platform.
Containerization: A software packaging and deployment technique that packages an application and its dependencies together in a container to ensure consistency and portability across different environments.
Containerization: The practice of packaging software into a container, which is a lightweight and portable format that can run consistently across different environments.
Containerization: The process of packaging software applications and their dependencies together in a container, allowing them to be easily deployed and run on any platform.
Continuous delivery (CD): A practice of automatically building, testing, and deploying software changes to production environments.
Continuous Delivery (CD): The practice of automatically building, testing, and deploying software changes to production or other environments as soon as they are ready.
Continuous delivery (CD): The practice of continuously delivering code changes to a staging or production environment, after they have passed through a series of automated tests and quality checks.
Continuous delivery: The practice of automatically delivering code changes to a production environment after they have been thoroughly tested.
Continuous Deployment (CD): A software development practice where code changes are automatically deployed to production environments after passing tests and quality checks.
Continuous deployment (CD): The practice of automatically deploying code changes to a production environment, after they have passed through a series of automated tests and quality checks.
Continuous Deployment (CD): The practice of automatically deploying software changes to production or other environments as soon as they are ready, without the need for manual approval.
Continuous Integration (CI): A software development practice where code changes are regularly integrated and tested to detect errors and conflicts early on.
Continuous integration (CI): The practice of continuously integrating and testing code changes into a shared repository, in order to identify and fix issues early.
Continuous Integration (CI): The practice of integrating code changes into a shared repository multiple times a day, and automatically building and testing the changes to ensure that the software remains in a releasable state.
Continuous integration/continuous delivery (CI/CD): A software development practice that emphasizes the frequent integration of code changes into a shared repository and the automation of the build, test, and deployment process.
Continuous integration: The practice of frequently integrating code changes into a shared repository, and automatically building and testing the resulting code.
Continuous monitoring: The practice of monitoring systems and applications in real-time to detect and respond to issues quickly.
Continuous testing: A software testing strategy where testing is integrated into the software development process and performed throughout the lifecycle of the application.
Continuous testing: The practice of continuously testing software applications throughout the development process, as opposed to waiting until the end of the development cycle.
Continuous Testing: The practice of executing automated tests as part of the CI/CD pipeline to obtain faster feedback on the quality of the application.
Convolutional Neural Networks (CNNs): A class of neural networks designed to process data with a grid-like topology, commonly used in image and video processing.
Cryptocurrency: A digital or virtual currency that uses cryptography for security and operates independently of a central bank.
Cucumber: An open-source tool for behavior-driven development (BDD) testing, used to write tests in a natural language format that is easily understandable by non-technical stakeholders.
Cybersecurity: The practice of protecting computer systems, networks, and data from unauthorized access, use, disclosure, disruption, modification, or destruction.
Data anonymization: The process of removing or replacing personal information from data to protect the privacy of individuals.
Data governance: The process of managing and controlling data throughout its lifecycle, including data quality, security, and compliance.
Data lake: A centralized repository that allows data to be stored in its raw format, facilitating the storage of structured and unstructured data at any scale.
Data lake: A centralized repository that allows you to store all your structured and unstructured data at any scale.
Data mining: The process of discovering patterns and knowledge from large data sets using machine learning, statistics, and database systems.
Data mining: The process of discovering patterns and knowledge from large datasets.
Data pipeline: A set of processes and tools for moving data from one step to another in a data science project, from data collection to modeling and deployment.
Data pipeline: A set of processes that move data from one system to another, including data extraction, transformation, and loading.
Data preprocessing: The process of cleaning, transforming, and preparing data for analysis.
Data privacy: The process of protecting personal information from unauthorized access, use, disclosure, or destruction.
Data quality: The level of completeness, accuracy, consistency, and timeliness of data.
Data Science: A field that involves using statistical and computational techniques to extract insights and knowledge from data.
Data scraping: The process of extracting data from websites or other sources for use in automated tasks or analysis.
Data security: The process of protecting data from unauthorized access, use, disclosure, disruption, modification, or destruction.
Data visualization: The process of creating visual representations of data to help communicate insights and information.
Data visualization: The process of representing data in graphical or pictorial format to facilitate understanding and decision making.
Data warehouse: A large, centralized repository for storing and managing data from multiple sources for reporting and analysis.
Data warehousing: The process of collecting, storing, and managing data from various sources to support business intelligence and analytics.
Data wrangling: The process of cleaning, transforming, and organizing data for analysis.
Database automation: The use of technology to automate tasks related to the management of databases, such as backup, recovery, and migration.
Debugging: The process of identifying and resolving errors in code or software.
Decentralized finance (DeFi): A financial system built on blockchain technology that allows for peer-to-peer transactions and eliminates the need for intermediaries.
Decision Trees: A subset of Machine Learning that uses a tree-like model of decisions and their possible consequences, used for both classification and regression.
Deep Learning (DL): A subset of Machine Learning that uses neural networks with multiple layers to learn from data and improve performance.
Deep Learning: A subfield of machine learning that involves training models using deep neural networks with many layers.
Defect density: The number of defects per unit of measure, such as lines of code or function points.
Defect management: The process of managing and tracking defects throughout their lifecycle, from identification to resolution.
Defect tracking: The process of identifying, reporting, and tracking defects or bugs in a software application.
Defect tracking: The process of identifying, reporting, and tracking issues or bugs in a software product.
Defect: A non-conformance to a requirement or a design.
Deployment automation: The use of automation tools and scripts to deploy software changes to different environments.
Deployment pipeline: A set of automated steps that are used to deploy software changes to different environments.
DevOps: A set of practices and principles that aims to bring development and operations teams together to collaborate, automate and improve the software delivery process.
DevOps: A set of practices and tools that aim to automate and optimize the software development and delivery process, by bringing development and operations teams together.
DevOps: A software development practice that emphasizes collaboration and communication between development and operations teams to improve the speed and quality of software releases.
DevSecOps: A methodology that integrates security practices into the software development process, and emphasizes collaboration between development, security, and operations teams.
Differential backup: The process of creating a backup that contains the changes made since the last full backup.
Disaster recovery (DR): The process of restoring critical systems and data after a disaster or interruption.
Disaster recovery testing: A type of testing that is used to determine how a software application behaves when recovering from a simulated disaster or major outage.
Distributed ledger technology (DLT): A type of database that is spread across a network of computers, rather than being stored in a single location.
Distributed testing: The process of running test cases across multiple machines or devices to test the performance of a system in a distributed environment.
Docker: An open-source containerization platform that makes it easy to create, deploy, and run applications in containers.
Docker: An open-source platform for developing, shipping, and running containerized applications.
Domain-driven development (DDD): A software development methodology that emphasizes the importance of understanding the problem domain and the business requirements before starting the development process.
dynamic code analysis: A type of code analysis that involves analyzing code while it is being executed.
Edge computing: A paradigm of computing where data is processed at the edge of the network, closer to the source of data, rather than in a centralized location.
Edge computing: A paradigm of distributing computing power, data storage and application logic closer to the devices that generate data, instead of sending all data to a centralized location for processing.
Elasticsearch: An open-source search and analytics engine that can be used for full-text search and real-time analytics.
Encryption: The process of converting plaintext into ciphertext, making it unreadable to anyone without the decryption key.
Endpoint protection: A security system that monitors and protects the endpoint devices such as computers, mobile devices, and servers from malware and other threats.
End-to-end testing: A type of testing that is used to ensure that a software application is working correctly from start to finish, including all components, modules, and interfaces.
Endurance testing: A type of testing that is used to determine how a software application behaves over a prolonged period of time.
Envoy: An open-source L7 proxy and communication bus developed by Lyft, that is often used as a data plane in service mesh architectures.
Error handling: The process of identifying, debugging and resolving errors in an automated process.
Event broker: A software component that acts as a mediator between event producers and event consumers, providing features such as routing, transformation, and persistence.
Event bus: A messaging infrastructure used to allow different microservices or applications to communicate through events.
Event sourcing: A pattern of storing the complete history of state changes in an event log, allowing to track the state of the system over time and replay the events to recreate the current state.
Event streaming: The practice of continuously processing and analyzing a stream of events, in real-time, using technologies such as Apache Kafka or Apache Pulsar.
Event triggers: The events that cause the serverless function to execute
Event: A change of state or occurrence of an action that is relevant to the system, that can trigger a reaction or computation.
Event-driven architecture: A pattern of software architecture that focuses on the production, detection, consumption of, and reaction to events.
Event-driven architecture: A software architecture pattern where the system reacts to specific events or triggers.
Event-driven automation: A type of automation where tasks are executed in response to specific events or triggers.
Event-driven automation: The practice of using events to trigger automated actions, such as the execution of a workflow or the calling of an API.
Event-driven integration: The practice of connecting different systems and services through events, rather than point-to-point connections.
Event-driven programming: A style of programming that focuses on the detection and reaction to events, rather than the traditional flow of control.
Exploratory testing: A type of testing where tester actively controls the design of the tests as those tests are performed and uses information gained while testing to design new and better tests.
Exploratory testing: An approach to testing where testers actively explore the software application to find defects and improve understanding of the system.
Exploratory testing: The process of testing software applications in an unstructured manner, without a pre-determined test plan or script.
Fault injection testing: A type of testing that is used to determine how a software application behaves when subjected to simulated failures or errors.
Feature flagging: A technique for controlling the rollout of new features by enabling or disabling them using configuration settings, rather than deploying new code.
Feature toggle: A technique that allows developers to enable or disable new features in a software product without changing the codebase.
Firewall: A security system that monitors and controls incoming and outgoing network traffic based on a set of security rules.
Fog computing: An extension of edge computing that brings data processing and storage closer to the edge of the network, with the ability to handle intermittent connectivity.
Function as a Service (FaaS): A category of serverless computing that allows developers to build and run applications and services in the form of single function.
Functional programming: A programming paradigm that emphasizes the use of pure functions and immutable data.
Functional testing: A type of testing that is used to ensure that a software application is working correctly according to its specified requirements and functions.
Functional testing: The process of testing the functional requirements of a software application.
Functional testing: The process of testing the functionality of a software application to ensure it meets the requirements and behaves as expected.
Gaussian Mixture Model (GMM): A subset of Machine Learning that uses a probabilistic model to represent a mixture of multiple Gaussian distributions.
Generative Adversarial Networks (GANs): A class of neural networks that consist of two parts, a generator that creates new data and a discriminator that tries to distinguish between real and generated data.
Generative models: A subset of Machine Learning that focuses on creating new examples or data that are similar to the examples or data it was trained on.
Git: A popular open-source version control system that allows for distributed collaboration and decentralized management of code changes.
Git: An open-source distributed version control system, used to manage and track changes to source code and other files.
GitFlow: A branching model for Git, which is a workflow for managing branches and releases in a software project.
GitHub: A web-based platform for version control and collaboration, built on top of the Git version control system.
GitHub: A web-based platform that provides Git-based version control and collaboration tools for software development.
GitLab: An open-source web-based Git repository manager that provides Git-based version control and collaboration tools for software development.
GitLab: An open-source web-based Git repository manager that provides source code management (SCM), continuous integration, and more features.
GitOps: A methodology to manage and operate infrastructure, services, and applications by using Git as a single source of truth for declarative infrastructure and applications.
Google Cloud Functions: A serverless compute service provided by Google Cloud Platform (GCP) that allows developers to run code without provisioning or managing servers.
Gradient Boosting: A subset of Machine Learning that uses an ensemble of decision trees to improve the accuracy and stability of predictions.
Grey-box testing: A type of testing that is used to test the internal structure and design of a software application, while still testing its external behavior.
Hadoop: An open-source framework for distributed storage and processing of large datasets using the Hadoop Distributed File System (HDFS) and the MapReduce programming model.
Hidden Markov Models (HMMs): A subset of Machine Learning that uses a probabilistic model to predict a sequence of events.
High availability (HA): The ability of a system to continue operating even if one or more of its components fail.
Hybrid blockchain: A blockchain that combines features of both public and private blockchains, allowing for both public access and restricted access.
Hybrid cloud: A cloud computing model where an organization uses a combination of public and private clouds to meet their specific requirements.
Hybrid cloud: A combination of public and private cloud services, used to create a unified, flexible, and secure IT environment.
Hypervisor: A piece of software that allows multiple virtual machines to share the same physical resources.
Incremental backup: The process of creating a backup that contains only the changes made since the last backup.
Industrial Internet of Things (IIoT): The use of IoT technology in industrial and manufacturing settings to improve productivity, efficiency, and safety.
Industry 4.0: The fourth industrial revolution, characterized by the integration of advanced technologies such as IoT, AI, and automation in manufacturing and other industries.
Infrastructure as a Service (IaaS): A cloud computing model that provides virtualized computing resources such as servers, storage, and networking over the internet.
Infrastructure as a Service (IaaS): A cloud computing service that provides virtualized computing resources, such as virtual machines, storage, and networking, over the internet.
Infrastructure as a Service (IaaS): Cloud computing services that provide virtualized computing resources over the internet.
Infrastructure as Code (IaC): A software development practice that uses code to manage and provision IT infrastructure, allowing for easier scaling, deployment, and management of resources.
Infrastructure as Code (IaC): The practice of managing and provisioning infrastructure using machine-readable definition files, rather than manual configuration.
Infrastructure as code: A practice of managing and provisioning infrastructure using code, rather than manual configuration.
Infrastructure automation: The use of technology to automate tasks related to the management of IT infrastructure, such as provisioning, scaling, and monitoring.
Integration testing: A type of testing that is used to ensure that different components or modules of a software application are working together as expected.
Integration testing: The process of testing how different units or components of a software application interact with each other.
Integration testing: The process of testing how different units or components of a software application work together.
Internet of Things (IoT): A network of interconnected devices and sensors that collect, process, and transmit data over the internet.
Internet of Things (IoT): A network of physical devices, vehicles, buildings, and other items embedded with electronics, software, sensors, and connectivity which enables these objects to connect and exchange data.
Intrusion detection and prevention system (IDPS): A security system that monitors network traffic for suspicious activity and attempts to block or alert on any detected intrusions.
Istio: An open-source service mesh that provides features such as traffic management, service discovery, and telemetry collection for microservices application.
Jenkins: An open-source automation server that can be used to automate various aspects of software development, including building, testing, and deploying code.
Jenkins: An open-source tool for continuous integration and continuous delivery (CI/CD), used to automate the building, testing, and deployment of software applications.
Job chaining: The practice of connecting a sequence of batch jobs, so that the output of one job is used as the input for the next job.
Job execution: The process of running a batch job.
Job monitoring: The practice of monitoring the status and performance of batch jobs, in order to detect and diagnose issues.
Job prioritization: The practice of determining the order in which batch jobs will be executed, based on factors such as importance, deadline, or resource availability.
Job queue: A list of batch jobs that are waiting to be executed.
Job recovery: The process of restoring the state of a batch job after a failure.
Job scheduler: A tool that is used to schedule and manage batch jobs.
JUnit: An open-source tool for unit testing Java applications.
Kafka: An open-source, distributed streaming platform that can handle high volumes of data and enables real-time data processing.
Kanban: A method for managing and optimizing workflows, originally developed for manufacturing but now widely used in software development.
Kanban: A visual method for managing and improving workflows, commonly used in Agile development.
K-Nearest Neighbors (KNN): A subset of Machine Learning that uses the similarity of data points to classify new data points into different classes.
Kubernetes: An open-source container orchestration system for automating the deployment, scaling, and management of containerized applications.
Kubernetes: An open-source container orchestration system that automates the deployment, scaling, and management of containerized applications.
Lean development: A set of principles and practices for software development that emphasizes minimizing waste and maximizing value.
Lean development: A software development methodology that emphasizes the elimination of waste and continuous improvement.
Linkerd: An open-source service mesh that is lightweight and easy to use, with a focus on cloud-native environments.
Linting: The process of automatically checking code for potential errors, issues, or violations of coding standards.
Load balancing: The process of distributing incoming network traffic across multiple servers to ensure that no single server is overwhelmed.
Load testing: A type of testing that is used to determine how a software application behaves under a specific load or stress.
Load testing: The process of testing a software application to ensure it can handle expected levels of traffic and usage.
Load testing: The process of testing a software application under a heavy load to ensure it can handle the expected number of users and transactions.
Logging and monitoring: The practice of collecting and analyzing log data to understand the behavior of systems and applications.
Logistic Regression: A subset of Machine Learning that uses a mathematical equation to model the relationship between a dependent variable and one or more independent variables.
Loops: A type of control flow in programming that allows for a set of instructions to be executed repeatedly.
Machine Learning (ML): A subfield of Artificial Intelligence (AI) that involves training models to learn from and make predictions or decisions based on data.
Machine Learning (ML): A subset of Artificial Intelligence that focuses on the development of algorithms and models that allow systems to improve from experience automatically without being explicitly programmed.
Machine learning: A subset of artificial intelligence that involves the use of algorithms to enable machines to improve their performance on a task with experience.
Macro: A series of commands or instructions that can be executed with a single command or keystroke.
Mercurial: An open-source distributed version control system, similar to Git but with a different syntax and approach.
Microservices: A software architecture style that emphasizes the use of small, independent, and loosely-coupled services to build and deploy software applications.
Microservices: An architectural style where a large application is broken down into a collection of small, loosely-coupled services that can be developed, deployed, and scaled independently.
Mining: The process of using computer power to validate and record transactions on a blockchain.
Mobile testing: The process of testing software applications on mobile devices and platforms.
Model-based testing: A type of testing that is used to automatically generate test cases based on a model of the system or software application.
MongoDB: A popular open-source NoSQL database that uses a document-based data model.
Monitoring and logging: The process of collecting and analyzing data about the performance and behavior of software applications and infrastructure to identify and troubleshoot issues.
Mutation testing: A type of testing that is used to determine how robust a software application’s tests are by introducing small, controlled changes to the code, called mutants, and checking if the tests can detect the changes.
Naive Bayes: A subset of Machine Learning that uses Bayes’ theorem with strong independence assumptions between features.
Natural Language Processing (NLP): A subfield of AI that deals with the interaction between computers and human language, including understanding and generating text and speech.
Natural language processing (NLP): A subfield of artificial intelligence that deals with the interaction between computers and human languages.
Natural Language Processing (NLP): A subset of AI that deals with the interaction between computers and human language, including speech and text.
Natural Language Processing (NLP): The process of using computational techniques to analyze, understand, and generate the languages that humans use to communicate.
Network Function Virtualization (NFV): The virtualization of network functions, such as firewalls, load balancers, and routers, to run on general-purpose hardware.
Network service: A software service that is responsible for managing network related functionality in a microservices application.
Neural Networks: A class of models inspired by the structure and function of biological neural networks, used in deep learning.
Neural Networks: A subset of Machine Learning that is inspired by the structure and function of the human brain, used for both supervised and unsupervised learning.
Non-functional testing: A type of testing that is used to ensure that a software application is working correctly in terms of performance, scalability, security, and other non-functional requirements.
Non-Fungible Tokens (NFTs): A type of digital asset that represents ownership of a unique item, such as a digital art or collectible.
NoSQL: A term used to describe non-relational databases that do not use the traditional table-based structure of relational databases.
NoSQL: A type of database that does not use a fixed schema, allowing for more flexible and scalable data storage.
Object-oriented programming (OOP): A programming paradigm that uses objects and classes to organize and structure code.
OpenShift: An open-source container application platform that is built on top of Kubernetes.
Parallel testing: The process of running multiple test cases simultaneously on different machines or browsers to reduce the execution time.
Penetration testing: A type of security testing that simulates a cyber-attack to identify vulnerabilities in a system.
Performance testing: The process of testing a software application to ensure it can handle expected levels of traffic and usage.
Performance testing: The process of testing a system or software application to determine how it performs under a specific load or stress.
Performance testing: The process of testing the performance of a software application to ensure it can handle the expected load and usage.
Platform as a Service (PaaS): A cloud computing model that provides a platform for developing, testing, and deploying software applications over the internet.
Platform as a Service (PaaS): A cloud computing service that provides a platform for developing, running, and managing applications and services, without the need to manage the underlying infrastructure.
Platform as a Service (PaaS): Cloud computing services that provide a platform for developing, running, and managing applications without the complexity of building and maintaining the infrastructure.
Predictive maintenance: The use of data, analytics, and AI to predict when equipment or machinery is likely to fail, in order to schedule maintenance and avoid unplanned downtime.
Predictive modeling: A subset of Machine Learning that uses statistical techniques to analyze historical data and make predictions about future events.
Private blockchain: A blockchain that is controlled by a single entity or a group of entities, and is often used in a business context.
Private cloud: A cloud computing model where the infrastructure and services are owned and operated by a single organization and made available only to that organization.
Private cloud: Cloud computing services that are owned and operated by a single company or organization, and are used solely by that company or organization.
Property-based testing: A type of testing that is used to automatically generate test cases based on the properties or characteristics of the system or software application.
Public blockchain: A blockchain that is open to anyone and is decentralized, meaning that no single entity controls it.
Public cloud: A cloud computing model where the infrastructure and services are owned and operated by a third-party provider and made available to the general public over the internet.
Public cloud: Cloud computing services that are owned and operated by a third-party company and made available to the public over the internet.
Puppet: An open-source tool for configuration management and automation, similar to Ansible but with a different syntax and approach.
Quality assurance (QA): The process of ensuring that a software application meets the quality standards and requirements set for it.
Quality Assurance (QA): The process of ensuring that a software product meets the quality standards and requirements set by the organization.
Quality control (QC): The process of inspecting a software application to ensure it meets the quality standards and requirements set for it.
Quality Control (QC): The process of verifying that a product, service, or system meets the quality standards and requirements set by the organization.
Quantum Algorithm: Algorithms that run on quantum computers and take advantage of the unique properties of quantum mechanics to solve problems more efficiently than classical computers.
Quantum annealing: A optimization algorithm that uses quantum mechanics to find the global minimum of a function
Quantum bits (qubits): The basic unit of quantum information, which can exist in multiple states simultaneously.
Quantum Circuit: A quantum circuit is a model for quantum computation in which a quantum computer is represented as a directed graph of quantum gates, which are the reversible quantum analogs of classical logic gates.
Quantum Computing: A type of computing that uses quantum-mechanical phenomena, such as superposition and entanglement to perform operations on data.
Quantum entanglement: A phenomenon where two or more quantum systems become correlated in such a way that the state of one system cannot be described independently of the state of the other systems.
Quantum error correction: Techniques used to protect quantum information from errors and noise, which can occur during computation and transmission.
Quantum key distribution (QKD): A method for distributing a secret key between two parties using the properties of quantum mechanics to prevent eavesdropping.
Quantum machine learning (QL): A field that combines quantum computing and machine learning to perform tasks such as optimization and sampling more efficiently.
Quantum state: A set of parameters that describe the state of a quantum system.
Random Forest: A subset of Machine Learning that uses an ensemble of decision trees to improve the accuracy and stability of predictions.
Recovery testing: A type of testing that is used to determine how a software application behaves when recovering from simulated failures or errors.
Recurrent Neural Networks (RNNs): A class of neural networks designed to process sequential data, commonly used in natural language processing and speech recognition.
Redis: An open-source, in-memory data structure store that can be used as a database, cache, and message broker.
Regression testing: The process of re-testing a software application after changes have been made to ensure that existing functionality has not been affected.
Regression testing: The process of retesting a software application after changes have been made to ensure that the changes did not cause any unintended consequences.
Regression testing: The process of retesting a system or software application after changes or updates have been made to ensure that it still functions correctly.
Reinforcement learning (RL): A subset of Machine Learning that deals with training an agent to take actions in an environment to maximize a reward.
Reinforcement Learning: A type of machine learning where the model learns by interacting with its environment and receiving feedback in the form of rewards or penalties.
Release management: The process of planning, coordinating, and controlling the release of new software versions.
Release management: The process of planning, scheduling, and controlling the movement of software releases to different environments.
Release pipeline: A set of automated steps that are used to build, test, and deploy software changes to different environments.
RESTful API: A type of web API that conforms to the architectural principles of REST (Representational State Transfer).
Robotic process automation (RPA): The use of software robots to automate repetitive, rule-based tasks typically performed by humans.
Robotics: The branch of AI that deals with the design, construction, and operation of robots, including their control systems, sensors, and actuators.
Robotics: The branch of engineering that deals with the design, construction, operation, and use of robots.
Rollback: The process of undoing a change, such as a software update, that caused issues.
Ron Legarski Data Science: Telecommunications Data Compiler and General Electrician.
Root cause analysis: The process of identifying the underlying cause of a defect or problem in a software application.
Root cause analysis: The process of identifying the underlying cause of a problem or issue in a software product.
Sanity testing: A type of testing that is used to ensure that the basic functionality of a software application is working as expected before proceeding with more in-depth testing.
Scalability testing: A type of testing that is used to determine how a software application behaves as the load or number of users increases.
Scheduling: The practice of determining when a batch job will be executed.
Screen scraping: The process of automatically extracting text or other information from the display of a computer.
Scripting languages: Programming languages that are commonly used for scripting and automation tasks, such as Python, JavaScript, and Perl.
Scripting: The process of writing code to automate a task or set of tasks.
Scrum: An Agile framework for managing and completing complex projects.
Scrum: An Agile framework for software development that emphasizes teamwork, accountability, and iterative progress.
Security Information and Event Management (SIEM): A security system that aggregates and analyzes log data from multiple sources to detect and respond to security incidents.
Security testing: The process of testing software applications to identify vulnerabilities and ensure the security of the system.
Selenium: An open-source tool for automating web browsers, used for web application testing.
Serverless computing: A cloud-based computing model where the cloud provider is responsible for managing and allocating the underlying infrastructure and resources, allowing developers to focus on building and deploying their applications.
Serverless: A cloud-computing execution model in which the cloud provider is responsible for executing a piece of code by dynamically allocating the resources.
Service discovery: The process of automatically locating and identifying the network address of a service in a distributed system.
Service mesh: A configurable infrastructure layer for microservices application that makes communication between service instances flexible, reliable, and fast.
Service mesh: A configurable infrastructure layer for microservices application that makes communication flexible, reliable, and fast.
Service mesh: A dedicated infrastructure layer for managing service-to-service communication in a microservices environment.
Sidecar pattern: A microservices architecture pattern in which a separate process is used to manage specific functionality, such as service discovery, traffic management, or security, for a main service.
Smart cities: The use of technology and data to improve the quality of life, efficiency, and sustainability of urban environments.
Smart contracts: A self-executing contract with the terms of the agreement directly written into lines of code.
Smart grids: The use of IoT technology to improve the efficiency, reliability, and sustainability of electricity generation, distribution, and consumption.
Smart homes: The use of IoT technology to control and automate home appliances and systems, such as lighting, heating, and security.
Smart property: A physical or digital asset that is registered and tracked on a blockchain, allowing for secure and transparent transfer of ownership.
Smoke testing: A type of testing that is used to ensure that the most critical functionality of a software application is working correctly before proceeding with more in-depth testing.
Snapshot: A point-in-time copy of data that can be used for backup or recovery purposes.
Soak testing: A type of testing that is used to determine how a software application behaves when subjected to prolonged load or stress.
SOAP: A protocol for exchanging structured data in the implementation of web services in computer networks.
Software as a Service (SaaS): A cloud computing model that provides software applications over the internet on a subscription basis.
Software as a Service (SaaS): A cloud computing service that provides software applications over the internet, typically on a subscription basis.
Software as a Service (SaaS): Cloud computing services that provide access to software applications over the internet, on a subscription basis.
SonarQube: An open-source tool for code analysis and quality management, that performs static code analysis and provides a web-based interface for reviewing and managing code quality.
SourceForge: A web-based platform that provides source code management and other tools for software development.
Spark: An open-source, distributed computing system that can process large datasets quickly using in-memory processing and directed acyclic graph (DAG) execution.
Spike testing: A type of testing that is used to determine how a software application behaves when subjected to sudden and extreme increases in load or traffic.
Stateful: A function that maintains data in memory between executions.
Stateless: A function that does not maintain any data in memory between executions.
static code analysis: A type of code analysis that involves analyzing code without executing it.
Storm: An open-source, distributed real-time computation system that can process large streams of data in real-time.
Streaming data: Data that is generated in real-time, such as social media feeds, sensor data, and financial market data.
Stress testing: A type of testing that is used to determine the breaking point of a software application by subjecting it to extreme loads or conditions.
Stress testing: The process of testing a software application beyond its expected load to identify its breaking point.
Stress testing: The process of testing a software application to ensure it can handle unexpected levels of traffic and usage.
Supervised Learning: A type of machine learning where the model is trained on labeled data, where the outcome variable is provided.
Support Vector Machines (SVMs): A subset of Machine Learning that uses a boundary, called a hyperplane, to separate data into different classes.
SVN: An open-source centralized version control system.
SVN: An open-source version control system that uses a centralized model for managing code changes.
Task scheduling: The process of planning and coordinating the execution of tasks at specific times or intervals.
Test Automation Architecture: The overall structure, design and organization of the test automation system and its components.
Test automation best practices: A set of guidelines and recommendations for automating tests in an effective and efficient manner.
Test Automation Environment: The hardware and software setup used for executing test automation scripts, including the operating system, testing tools, and test data.
Test automation framework: A set of tools and practices that provide a structured approach to automating tests.
Test automation framework: A set of tools, libraries, and conventions that are used to structure and organize test automation code.
Test automation library: A collection of reusable functions or modules that can be used to automate tests.
Test automation pyramid: A concept that describes the balance between different types of tests such as unit tests, integration tests, and end-to-end tests in a software development process.
Test automation pyramid: A concept that suggests that the majority of tests should be automated at the unit level, with fewer tests automated at the service and UI levels.
Test Automation reporting: The process of generating reports on the results of test automation activities, including test execution results, pass/fail status, and test metrics.
Test automation script: A set of instructions that are executed by a test automation tool to automate a test.
Test automation strategy: A plan or approach for how to use automation tools and scripts to test software applications effectively.
Test automation strategy: The approach and plan for automating tests across an organization.
Test automation suite: A collection of test automation tools that are used together to automate tests.
Test Automation Tool: A software that assists in the automation of testing activities, such as creating, executing, and reporting on test cases. Examples include Selenium, Appium, and TestComplete.
Test automation tool: A software tool that is used to automate the execution of tests.
Test automation: The use of software tools and scripts to automate the process of testing software applications.
Test automation: The use of software tools to automatically execute test cases and check the system’s behavior against expected results.
Test automation: The use of technology to automate the testing of software applications.
Test bed: A testbed environment that is set up specifically for testing a software product.
Test case: A set of conditions or variables under which a system or component will be tested.
Test case: A set of inputs, execution conditions, and expected outcomes used to test a specific aspect of a software application.
Test case: A set of inputs, expected outcomes, and steps that are used to test a specific aspect of a software application.
Test closure: The process of finalizing all activities related to testing and documenting the results.
Test coverage: A measure of how much of the code or functionality of a software application has been tested.
Test coverage: The percentage of the code or functionality of a software application that is covered by a set of tests.
Test data anonymization: The process of removing or replacing personal information from test data to protect the privacy of individuals.
Test data anonymization: The process of replacing sensitive data with non-sensitive data or data that is not personally identifiable, in order to protect the privacy of the individuals represented in the test data.
Test data generation: The process of creating or generating test data to be used in test automation.
Test data generation: The process of creating test data for use in testing a software product.
Test data management tools: A software that helps in creating, managing, and maintaining test data for test automation.
Test Data Management: The process of creating, managing and maintaining test data for the purpose of automating tests.
Test data management: The process of managing and maintaining test data throughout the testing process.
Test data masking: The process of obscuring or replacing sensitive information from test data to protect security of the system.
Test data provisioning: The process of providing test data to testing environments.
Test data virtualization: The process of creating virtual copies of data to use in test automation instead of using actual data.
Test data: Data that is used to test a software product.
Test driver: A program module that invokes a module or system under test and provides test inputs, monitors execution, and provides test outputs.
Test environment: The hardware, software, and network configurations in which a software product is tested.
Test execution: The process of running tests on a software product.
Test harness: A set of tools and frameworks that are used to automate the execution of tests.
Test lab: A facility where software products are tested.
Test Management Tool: A software that assists in managing the test automation process, such as creating, executing, and reporting on test cases. Examples include TestRail, Zephyr, and qTest.
Test management tool: A software tool that is used to manage the testing process, including test planning, execution, and reporting.
Test plan: A document that outlines the testing strategy, objectives, resources, and schedule for a software product.
Test reporting: The process of documenting and reporting the results of testing.
Test script: A set of instructions that are executed by a test automation tool to automate a test.
Test stub: A small program module that replaces a called module in a system or component during testing.
Test suite: A collection of test cases that are used to test a software product.
Test suite: A collection of test cases that are used to test a specific aspect or feature of a software application.
Test-driven development (TDD): A software development approach where tests are written before the corresponding code is written, in order to ensure that the code satisfies the requirements.
Test-driven development (TDD): A software development methodology that emphasizes writing automated tests before writing code.
Test-driven development (TDD): A software development methodology where developers write automated test cases before writing the actual code.
TestNG: An open-source tool for automating test cases and generating test reports, similar to JUnit but with additional features.
Time-series data: Data that is collected over time, and can be used to analyze trends and patterns in events that happen over a period of time.
Transfer Learning: A technique where a model trained on one task is used as a starting point to train a model on a related task.
Travis CI: A cloud-based continuous integration and delivery service that supports various programming languages and platforms.
Two-factor authentication (2FA): A method of authentication that requires the use of two different forms of authentication, such as a password and a fingerprint or a token.
Unit testing: The process of testing individual units or components of a software application to ensure they function as expected.
Unit testing: The process of testing individual units or components of a software application, such as functions or methods.
Unsupervised Learning: A type of machine learning where the model is trained on unlabeled data, with no predefined outcome variable.
Usability testing: The process of testing software applications to ensure they are easy to use and understand for end-users.
User acceptance testing (UAT): The process of testing a software application by end-users to ensure it meets their needs and is ready for release.
User acceptance testing: A type of testing that is performed by end-users to ensure that a software application meets their needs and requirements.
Version control: The management of changes made to a software application over time, allowing for collaboration, rollback, and auditing.
Virtual Desktop Infrastructure (VDI): A technology that allows users to access a virtualized desktop environment from any device.
Virtual machine (VM): A software implementation of a physical machine that can run its own operating system and applications.
Virtual machine: A software-based simulation of a computer or other device.
Virtual Private Network (VPN): A method of creating a secure, encrypted connection between a device and a remote network, allowing for secure remote access and communication.
Virtualization testing: The process of testing software applications on virtualized environments.
Virtualization: The creation of a virtual version of a computing resource, such as a server, operating system, or storage device.
Virtualization: The practice of creating a virtual version of something, such as a virtual machine, network, or storage device, in order to improve efficiency and flexibility.
Virtualization: The process of creating a virtual version of a physical resource, such as a server, operating system, or network, to improve resource utilization and reduce costs.
Volume testing: A type of testing that is used to determine how a software application behaves when handling large amounts of data or transactions.
Waterfall development: A traditional, linear software development method that emphasizes strict planning and progress moving in a downward, linear fashion through distinct phases.
Waterfall model: A traditional software development methodology that follows a linear and sequential process, with distinct phases for planning, analysis, design, implementation, testing, and maintenance.
Wearables: IoT devices that can be worn on the body, such as smartwatches and fitness trackers, that collect, process, and transmit data over the internet.
Web automation: The use of technology to automate tasks on the web, such as filling out forms, clicking buttons, and navigating pages.
Web crawling: The process of automatically traversing the web to discover and index content.
Web scraping: A type of data scraping that specifically deals with extracting data from websites.
Web scraping: The process of automatically extracting data from websites using software tools.
Web testing: The process of testing websites and web applications to ensure they function correctly.
White-box testing: A type of testing that is used to test the internal structure and design of a software application, without testing its external behavior.
Workflow automation: The use of technology to automate the steps of a business process.
Workflow management: The process of designing, implementing, and monitoring the flow of work in an organization.

This list is not exhaustive, and there may be more terms used in the context of software automation depending on the specific area or industry.