Common Application Development Terminology – SolveForce Fiber Internet, Cloud Computing & Telecommunications

A/B testing: a method of testing in which two or more versions of a software application or system are randomly shown to users, and the results are used to determine which version is more effective.
Abstraction: a feature of OOP that allows an object to be defined in terms of its essential characteristics, without specifying the details of its implementation.
Acceptance testing: the process of testing a software application or system to ensure it meets the acceptance criteria and is ready for release.
Accessibility: The practice of designing and developing products, services, and systems to be usable by people with disabilities or impairments.
Accessibility: the practice of designing software applications and systems to be usable by people with disabilities, such as those who are visually or hearing impaired.
Agile development: a method of software development that emphasizes iterative, incremental work, and flexible response to change
Agile development: a software development methodology that emphasizes flexibility, collaboration, and rapid iteration, with a focus on delivering working software in short cycles, called sprints.
AJAX: A technique used to create dynamic and interactive web pages, by sending and receiving data from the server asynchronously, without having to refresh the entire page.
Alerting: the process of setting up notifications or alarms that trigger when certain conditions or thresholds are met, such as high error rates or low memory usage.
API (Application Programming Interface): a set of protocols, routines, and tools for building software and applications
API access control: The process of regulating who or what can access an API, and what actions they can perform.
API analytics alert: A notification that is triggered when certain conditions are met in the data collected about the usage and performance of an API.
API analytics anomaly detection: The process of identifying unusual patterns or behaviors in the data collected about the usage and performance of an API.
API analytics dashboard: A web-based interface that displays data about the usage and performance of an API, including charts, graphs, and metrics.
API analytics data: The data collected about the usage and performance of an API, such as request and response data, error data, and user data.
API analytics event tracking: The process of tracking specific actions or events that occur when interacting with an API, such as button clicks and form submissions.
API analytics log: A record of the data collected about the usage and performance of an API, including request and response data, error data, and user data.
API analytics platform: A software platform that provides tools and functionality for collecting, analyzing and interpreting data about the usage and performance of an API.
API analytics report: A document or presentation that summarizes the data collected about the usage and performance of an API, including charts, graphs, and metrics.
API analytics service: A cloud-based service offered by various vendors that provides API analytics functionality, such as AWS Pinpoint, Azure Metrics, and Google Analytics.
API analytics service: A cloud-based service offered by various vendors that provides API analytics functionality, such as AWS Pinpoint, Google Analytics and Mixpanel.
API analytics tool: A software tool that can be used to collect, analyze, and interpret data about the usage and performance of an API, such as Google Analytics and Mixpanel
API analytics: The practice of collecting and analyzing data about API usage and performance.
API analytics: The process of collecting, analyzing and interpreting data about the usage and performance of an API.
API analytics: The process of collecting, analyzing, and interpreting data about the usage and performance of an API.
API architecture style: A set of guidelines and best practices for designing the architecture of an API, such as microservices, monolithic or serverless.
API architecture: The overall structure and organization of an API, including the components, interfaces, and technologies used.
API cache: A storage location for frequently-used API responses, such as a memory cache or a disk cache.
API caching invalidation: The process of removing outdated or stale data from the cache.
API caching invalidation: The process of removing the response of an API request from the cache when it is no longer needed or has become outdated.
API caching layer: A layer that sits between the API and its clients and manages the caching of API responses.
API caching middleware: Software that sits between the API and its clients and manages the caching of API responses.
API caching reverse proxy: A reverse proxy that caches API responses and serves them to clients.
API caching server: A server that caches API responses and serves them to clients.
API caching service: A cloud-based service offered by various vendors that provides API caching functionality, such as AWS Elasticache, Azure Cache, and Google Cloud Memorystore.
API caching strategy: The approach used to cache API responses, such as using a time-to-live (TTL) value or caching based on the request parameters.
API caching strategy: The approach used to cache the response of an API request, such as caching based on a specific time period or a certain number of requests.
API caching system: A system that caches API responses and serves them to clients.
API caching: The process of storing frequently-used API responses in a cache to reduce the number of requests made to the API and improve performance.
API caching: The process of storing the response of an API request in a cache to improve performance and reduce the number of requests made to the backend.
API choreography: The process of managing and coordinating the interactions between different APIs without a central point of control, where each API communicates directly with the other APIs.
API client caching invalidation: The process of removing the response of an API request from the cache when it is no longer needed or has become outdated.
API client caching strategy: The approach used to cache the response of an API request, such as caching based on a specific time period or a certain number of requests.
API client caching: The process of storing the response of an API request in a cache to improve performance and reduce the number of requests made to the API.
API client compatibility: The level of compatibility between an API client and the API it interacts with.
API client documentation: The documentation that describes how to use an API client library or SDK.
API client error handling: The process of handling and returning errors that occur when interacting with an API using an API client.
API client example: A sample code or application that demonstrates how to interact with an API using a specific programming language or framework.
API client exception handling: The process of handling and returning exceptions that occur when interacting with an API using an API client.
API client input sanitization: The process of removing any potentially harmful or unwanted data from the input passed to an API client.
API client input validation: The process of validating the input passed to an API client to ensure it conforms to the expected format and values.
API client library: A pre-built software library that simplifies the process of interacting with an API by providing a set of functions and methods that abstract away the underlying HTTP requests and responses.
API client output validation: The process of validating the output returned from an API client to ensure it conforms to the expected format and values.
API client performance optimization: The process of improving the performance of an API client by reducing network latency, reducing the number of API calls, and caching data locally.
API client SDK: A software development kit that provides a set of tools, libraries, and documentation for developers to interact with an API.
API client support: The process of providing assistance and resources to developers who are using an API client.
API client testing: The process of testing the functionality and performance of an API client.
API client troubleshooting: The process of identifying and resolving issues with an API client.
API client versioning management: The process of managing and maintaining different versions of an API client.
API client versioning: The process of maintaining and updating multiple versions of an API client to ensure compatibility with different versions of the API.
API composition framework: A pre-built library or tool that can be used to combine multiple APIs to create a new, more complex API.
API composition service: A cloud-based service offered by various vendors that provides API composition functionality.
API composition tool: A software tool that can be used to combine multiple APIs to create a new, more complex API.
API composition: The process of combining multiple APIs to create a new, more complex API.
API consumption: The practice of consuming or using an API to access data or services from another system or service.
API contract testing: The process of testing an API by comparing the expected behavior with the actual behavior, based on the API contract or specification.
API contract: A document that describes the expected behavior of an API, including the endpoints, parameters, responses, and error codes.
API deployment automation: The process of automating the deployment of an API using tools or scripts.
API deployment containerization: The process of packaging an API and its dependencies into a container, such as a Docker container, to facilitate deployment.
API deployment environment: The environment in which an API is deployed, such as development, staging, or production.
API deployment management: The process of managing the deployment of an API, including versioning, rollback, and scaling.
API deployment monitoring: The process of monitoring the deployment of an API, including health, performance, and availability.
API deployment orchestration: The process of managing and coordinating the deployment of multiple APIs and their dependencies.
API deployment pipeline: The set of steps and processes that are followed to deploy an API, such as building, testing, and releasing.
API deployment scaling: The process of adjusting the resources and infrastructure of an API to handle an increasing number of requests and users.
API deployment security: The process of securing the deployment of an API, including encryption, authentication, and access controls.
API deployment: The process of making an API available to users and clients.
API design documentation: A document that describes the design of an API, including the endpoints, parameters, responses, and error codes.
API design governance: The process of creating and enforcing policies and procedures for the design, development and maintenance of APIs in an organization.
API design guidelines: set of recommendations and best practices for designing and building APIs, such as RESTful API design principles, GraphQL and gRPC.
API design iteration: The process of refining and improving an API design by testing it and incorporating feedback.
API design mockup: A representation of an API that can be used for testing and evaluation purposes, but it is not functional.
API design patterns: A set of commonly used solutions and approaches for designing and building an API, such as REST and GraphQL.
API design principles: A set of guidelines and best practices for designing and building an API, such as simplicity, consistency, and discoverability.
API design prototyping: The process of creating a prototype of an API for testing and evaluation purposes.
API design review: The process of reviewing and evaluating the design of an API, in order to identify and mitigate potential issues or weaknesses.
API design testing: The process of testing an API design by creating prototypes or mockups of the API and evaluating them with users or stakeholders.
API design: The process of creating and defining the structure, behavior, and interface of an API.
API design: The process of designing the functionalities and interfaces of an API, to ensure it is easy to use, understand, and consume.
API developer community: A group of developers who use or contribute to a specific API or platform.
API developer portal: A web-based platform that provides information, documentation, and tools for developers to consume an API, including examples, tutorials, and SDKs.
API developer relations: The process of building and maintaining relationships with developers who use or contribute to a specific API or platform.
API development environment: The environment in which an API is developed, such as a local development environment, a staging environment, and a production environment.
API development framework: A pre-built library or tool that can be used to develop an API, such as Express.js, Flask, and Ruby on Rails.
API development frameworks: pre-built libraries and tools that can be used to simplify and speed up the process of building APIs, such as Express.js, Flask, and Django.
API development kit (SDK): A set of tools and libraries that can be used to develop an API, such as the AWS SDK, the Azure SDK, and the Google Cloud SDK.
API development language: The programming language used to develop an API, such as JavaScript, Python, and Ruby.
API development life cycle (SDLC): The process of developing an API, including the phases of requirement gathering, design, development, testing, deployment, and maintenance.
API development process: The methodology used to develop an API, such as Agile, Scrum, or Waterfall.
API development standard: A set of guidelines and best practices for developing an API, such as RESTful API design principles, JSON API specification and OpenAPI specification.
API development team: The group of individuals responsible for developing an API, including developers, designers, project managers, and QA engineers.
API development: The practice of creating, designing and maintaining Application Programming Interfaces (APIs) that allow different systems and services to communicate with each other.
API discovery platforms: Tools that provide a centralized place for discovering existing APIs, such as RapidAPI, ProgrammableWeb, and APIs.guru.
API discovery: The practice of finding and discovering existing APIs that can be reused or integrated into a new application or system.
API discovery: The process of finding and identifying available APIs in a specific domain or industry.
API documentation generation tools: tools that can be used to automatically generate documentation for APIs, such as Swagger, Postman, and Apigee.
API documentation generation: The practice of automatically generating documentation for an API using information from the API’s code and annotations.
API documentation generator: A tool that can automatically generate API documentation from code and annotations.
API documentation generator: A tool that can automatically generate API documentation from code or other source files.
API documentation portal: A web-based portal that provides API documentation and resources for developers to learn and use an API.
API documentation standard: A set of guidelines and best practices for creating and maintaining API documentation.
API documentation standards: A set of guidelines for creating and formatting API documentation, such as OpenAPI Specification (OAS) and RAML.
API documentation tool: A software tool that can be used to create and maintain documentation that describes the functionality and usage of an API, such as Swagger, Postman and Readme.io.
API documentation: Technical documentation that describes how to use and interact with an API, including information on endpoints, parameters, and responses.
API documentation: The practice of creating documentation that describes the functionalities and usage of an API.
API documentation: The process of creating and maintaining documentation that describes the functionality and usage of an API, including endpoints, parameters, responses, and error codes.
API economy: The ecosystem of businesses and organizations that create and consume APIs to share data and services, and create new opportunities for innovation and value creation.
API endpoint caching: The process of storing frequently-used API endpoint responses in a cache to reduce the number of requests made to the endpoint and improve performance.
API endpoint documentation: The process of creating and maintaining documentation that describes the functionality and usage of an API endpoint, including the parameters, responses, and error codes.
API endpoint load balancing: The process of distributing incoming requests to multiple instances of an API endpoint to improve performance and availability.
API endpoint rate limiting: The process of limiting the number of requests that can be made to an API endpoint over a specific period of time.
API endpoint security: The process of securing the access to an API endpoint, including authentication, access controls, and rate limiting.
API endpoint testing: The process of testing an API endpoint to ensure that it behaves as expected and meets the requirements.
API endpoint throttling: The process of controlling the rate at which an API endpoint can be accessed, usually to protect the availability and performance of the endpoint.
API endpoint versioning: The process of creating multiple versions of an API endpoint to ensure backward compatibility, and to allow for new features and functionality to be added without breaking existing clients.
API endpoint: A specific URL or entry point for accessing an API.
API error handling: The process of handling and returning errors that occur when interacting with an API.
API exception handling: The process of handling and returning exceptions that occur when interacting with an API.
API federation framework: A pre-built library or tool that can be used to connect multiple APIs together and make them available as a single API.
API federation service: A cloud-based service offered by various vendors that provides API federation functionality.
API federation tool: A software tool that can be used to connect multiple APIs together and make them available as a single API.
API federation: The process of connecting multiple APIs together and making them available as a single API.
API fuzz testing: The process of testing an API with a large number of random input data in order to identify unexpected behavior or crashes.
API gateway caching: The practice of caching API responses at the gateway level in order to improve API performance.
API gateway caching: The process of storing frequently-used API gateway responses in a cache to reduce the number of requests made to the gateway and improve performance.
API gateway documentation: The process of creating and maintaining documentation that describes the functionality and usage of an API gateway, including the parameters, responses, and error codes.
API gateway pattern: A common architectural pattern used to provide a single entry point for different services and APIs, and handle tasks such as security, rate limiting, and request/response transformation.
API gateway pattern: A design pattern that involves using an API gateway to provide a single entry point for multiple microservices.
API gateway rate limiting: The process of limiting the number of requests that can be made to an API gateway over a specific period of time.
API gateway security: The process of securing the access to an API gateway, including authentication, access controls, and rate limiting.
API gateway service: A cloud-based service offered by various vendors that provides API gateway functionality, such as AWS API Gateway, Azure API Management, and Google Cloud Endpoints.
API gateway service: A cloud-based service that provides an API gateway functionality, such as AWS API Gateway, Azure API Management, and Google Cloud Endpoints.
API gateway testing: The process of testing an API gateway to ensure that it behaves as expected and meets the requirements.
API gateway throttling: The process of controlling the rate at which an API gateway can be accessed, usually to protect the availability and performance of the gateway.
API gateway versioning: The process of creating multiple versions of an API gateway to ensure backward compatibility, and to allow for new features and functionality to be added without breaking existing clients.
API gateway: A reverse proxy that sits in front of your backend services and APIs, and acts as a single entry point for external consumers. It can handle tasks such as authentication, rate limiting, caching, and request/response transformation, among others.
API gateway: A server or service that acts as an intermediary between an API and its clients, handling tasks such as authentication, rate limiting, and request/response handling.
API gateway: A server or service that acts as an intermediary between an API and its clients, providing functionality such as authentication, rate limiting, caching, and transformation.
API Gateway: A service that acts as an intermediary between an application and a set of microservices, handling tasks such as routing, authentication, and rate limiting.
API governance: The practice of creating and enforcing policies and procedures for the management, usage, and security of APIs in an organization.
API input sanitization: The process of removing any potentially harmful or unwanted data from the input passed to an API.
API input validation: The process of validating the input passed to an API to ensure it conforms to the expected format and values.
API key management: The process of creating, distributing, and revoking API keys and tokens.
API key revocation: The process of invalidating or removing API keys and tokens that are no longer needed or have been compromised.
API key rotation: The process of periodically replacing API keys and tokens to enhance security.
API key: A unique identifier that is used to authenticate and authorize access to an API.
API lifecycle management platform: A platform that provides tools and functionality for managing the entire lifecycle of an API.
API lifecycle management policy: A set of rules and guidelines for managing the entire lifecycle of an API.
API lifecycle management tools: Software that provides functionalities for managing the different stages of an API’s lifecycle, including design, development, testing, deployment, and retirement, such as Mulesoft Anypoint Platform, Red Hat 3scale API Management, and IBM API Connect.
API lifecycle management: The practice of managing the different stages of an API’s lifecycle, including design, development, testing, deployment, and retirement.
API lifecycle management: The process of managing the entire lifecycle of an API, including design, development, testing, deployment, and maintenance.
API load testing: The process of testing an API’s performance and stability under high load conditions.
API logging service: A cloud-based service offered by various vendors that provides API logging functionality, such as AWS CloudWatch Logs, Azure Log Analytics, and Google Stackdriver Logging.
API logging tool: A software tool that can be used to record information about the usage and performance of an API for later analysis and troubleshooting, such as Elasticsearch, Logstash and Kibana.
API logging: The process of recording information about the usage and performance of an API for later analysis and troubleshooting.
API Management as a Service (APIaaS): a cloud-based service that provides API management functionalities and infrastructure, such as AWS API Gateway, Azure API Management, and Google Cloud Endpoints.
API management as a service: A cloud-based service offered by various vendors that provides API management functionality, such as AWS API Gateway, Azure API Management, and Google Cloud Endpoints.
API Management Platform as a Service (API PaaS): a cloud-based service that provides a platform for managing and optimizing APIs, including authentication, authorization, monitoring, analytics and more.
API management platform: A software or service that provides functionalities for managing and optimizing APIs, including authentication, authorization, monitoring, and analytics.
API management platform: A software platform that provides tools and functionality for managing the operations and usage of an API, such as security, monitoring, analytics, and scaling.
API management platforms: Tools that provide a centralized place for managing and optimizing APIs, including authentication, authorization, monitoring, and analytics.
API management policy: A set of rules and guidelines for managing and optimizing APIs in an organization.
API management service: A cloud-based service offered by various vendors that provides API management functionality, such as AWS API Gateway, Azure API Management, and Google Cloud Endpoints.
API management tools: tools and software that provide functionalities for creating, publishing, testing and managing APIs, such as Swagger, Postman, and Apigee.
API management: The practice of managing and optimizing the performance, security, and usage of an API.
API management: The process of overseeing and controlling the operations and usage of an API, including security, monitoring, analytics, and scaling.
API marketplace: A platform where different APIs can be discovered, subscribed to and used by developers or businesses.
API mock: A simulation of an API that can be used for testing and development purposes.
API mocking framework: A pre-built library or tool that can be used to create a simulated or “mocked” version of an API.
API mocking server: A server that simulates the behavior of an API for testing or development purposes.
API mocking tool: A software or tool that can be used to create a simulated or “mocked” version of an API.
API mocking tools: tools that can be used to simulate the behavior of an API for testing and development purposes, such as WireMock, Nginx, and Hoverfly.
API mocking: The process of creating a simulated or “mocked” version of an API for testing or development purposes.
API monetization: The practice of generating revenue from an API by charging for its usage or by selling access to its data and services.
API monetization: The process of generating revenue from an API by charging for its usage, access or other value-added services.
API monitoring service: A cloud-based service offered by various vendors that provides API monitoring functionality, such as AWS CloudWatch, Azure Monitor, and Google Stackdriver.
API monitoring tool: A software tool that can be used to track the performance and availability of an API in real-time, such as New Relic, AppDynamics and Prometheus.
API monitoring: The process of tracking the performance and availability of an API in real-time.
API negative testing: The process of testing an API by providing invalid or unexpected input in order to evaluate its error handling and input validation capabilities.
API orchestration: The process of managing and coordinating the interactions between different APIs, including routing requests and handling responses.
API output validation: The process of validating the output returned from an API to ensure it conforms to the expected format and values.
API parameter validation: The process of validating the parameters passed to an API to ensure they conform to the expected format and values.
API penetration testing: The process of attempting to exploit vulnerabilities in an API in order to gain unauthorized access or perform malicious actions.
API performance testing frameworks: pre-built libraries and tools that can be used to automate the process of testing the performance of APIs, such as Apache JMeter, LoadRunner, and Gatling.
API performance testing: The practice of measuring the performance of an API under various loads and conditions.
API performance testing: The process of testing an API’s performance in terms of response time, throughput, and resource usage.
API portal: A web-based platform that provides information, documentation, and tools for developers to consume an API.
API portal: A web-based portal that provides documentation, tutorials, and other resources for developers to learn and use an API.
API product analytics: The process of collecting, analyzing and interpreting data about the usage and performance of an API product.
API product feedback: The process of gathering and analyzing feedback from users of an API product.
API product management: The process of planning, developing, and managing an API as a product, including its features, pricing, and roadmap.
API product marketing: The process of promoting an API product to potential users, including positioning, messaging, and content creation.
API product monetization: The process of generating revenue from an API product by charging for its usage, access or other value-added services.
API product roadmap: A document that outlines the future development plans for an API product, including upcoming features, enhancements, and milestones.
API product strategy: The approach used to plan, develop and manage an API as a product, including its target audience, value proposition, and pricing model.
API proxy: A server that sits in front of an API and acts as a reverse proxy, handling tasks such as authentication, rate limiting, caching, and request/response transformation.
API rate limiting: The practice of limiting the number of API requests that a particular user or system can make within a certain time period.
API reliability testing: The process of testing an API’s ability to function correctly and consistently over time.
API request/response handling: The process of handling requests and returning responses from an API.
API sandbox testing: The practice of testing an API in a simulated environment, without affecting the production environment.
API sandbox: A simulated environment that can be used for testing and development purposes, without affecting the production environment.
API scalability testing: The process of testing an API’s ability to handle an increasing number of requests and users.
API scaling: The process of adjusting the resources and infrastructure of an API to handle an increasing number of requests and users.
API security audit: The process of evaluating the security of an API, identifying vulnerabilities and recommending remediation.
API security audit: The process of reviewing and evaluating the security of an API, in order to identify and mitigate vulnerabilities and weaknesses.
API security best practices: A set of guidelines and recommendations for securing APIs, such as using secure protocols, implementing authentication and authorization, and performing regular security testing.
API security certificate: A digital certificate that is used to secure an API, such as an SSL/TLS certificate.
API security certification program: A program that assesses and certifies the security of APIs, such as PCI DSS for payment processing APIs and HIPAA for healthcare APIs.
API security certification: A formal certification that an API meets a set of security standards.
API security certification: The process of certifying that an API meets a specific security standard or set of guidelines.
API security compliance: The process of ensuring that an API adheres to a set of security regulations and standards.
API security compliance: The process of ensuring that an API meets a specific set of security regulations or standards, such as HIPAA for healthcare APIs and GDPR for personal data APIs.
API security firewall: A security system that monitors and controls inbound and outbound traffic to and from an API.
API security gateway: A system that acts as an intermediary between an API and its clients and provides security functionality, such as authentication and access controls.
API security incident management: The process of managing and coordinating the response to a security incident on an API, including communication and reporting to stakeholders.
API security incident response plan: A plan that outlines the steps to be taken in the event of a security incident on an API, including communication and reporting to stakeholders.
API security incident response: The process of identifying, assessing, and responding to a security incident on an API, including steps such as containment, eradication, and recovery.
API security incident: A security breach or attack that has occurred on an API, resulting in unauthorized access, data loss, or other negative consequences.
API security key: A secret value that is used to encrypt and decrypt data sent to and from an API.
API security monitoring: The process of monitoring the security of an API and identifying potential vulnerabilities or threats.
API security monitoring: The process of tracking and analyzing the security of an API in real-time, in order to identify and respond to potential threats and incidents.
API security policy: A set of rules and guidelines for securing APIs in an organization.
API security protocol: A protocol that is used to secure an API, such as HTTPS, TLS, and SSL.
API security protocol: A set of rules that are used to secure an API, such as OAuth, JWT, and SAML.
API security scanning automation framework: A pre-built library or tool that can be used to automate the process of scanning APIs for vulnerabilities and security issues, such as OWASP ZAP, Nessus, and Acunetix.
API security scanning automation frameworks: pre-built libraries and tools that can be used to automate the process of scanning APIs for vulnerabilities and security issues, such as OWASP ZAP, Nessus, and Acunetix.
API security scanning tool: A software or tool that can be used to automatically scan an API for vulnerabilities and security issues, such as OWASP ZAP, Nessus, and Acunetix.
API security scanning tools: tools that automatically scan APIs for vulnerabilities and security issues, such as OWASP ZAP, Nessus, and Acunetix.
API security scanning: The process of automatically scanning an API for vulnerabilities and security issues.
API security standard compliance validation: The process of testing and validating that an API meets a specific security standard or set of guidelines.
API security standard compliance: The process of ensuring that an API meets a specific security standard or set of guidelines.
API security standard: A set of guidelines and best practices for securing an API, such as OAuth, OpenID Connect, and JWT.
API security standard: A set of guidelines and best practices for securing an API, such as OWASP API Security Top 10 and NIST SP 800-63.
API security standard: A set of guidelines and best practices for securing APIs, such as OWASP API Security, OAuth, and OpenID Connect.
API security standards: set of guidelines and best practices for securing APIs, such as OWASP API Security, OAuth, and OpenID Connect.
API security testing framework: A pre-built library or tool that can be used to automate the process of testing the security of APIs, such as OWASP ZAP, Nessus, and Acunetix.
API security testing frameworks: pre-built libraries and tools that can be used to automate the process of testing the security of APIs, such as OWASP ZAP, Nessus, and Acunetix.
API security testing tool: A software or tool that can be used to test the security of an API, such as Burp Suite, OWASP ZAP, and Fiddler.
API security testing tools: Tools and software that are used to test and identify vulnerabilities in APIs, such as Burp Suite, OWASP ZAP, and Fiddler.
API security testing: The practice of identifying and mitigating vulnerabilities in an API.
API security testing: The process of identifying and mitigating vulnerabilities and security issues in an API.
API security testing: The process of testing an API for security vulnerabilities, such as penetration testing and vulnerability scanning.
API security testing: The process of testing an API’s security by attempting to exploit vulnerabilities and identifying potential vulnerabilities.
API security threat: A potential security risk or attack that can be launched against an API, such as SQL injection, cross-site scripting (XSS), and man-in-the-middle (MitM) attacks.
API security token: A string of characters that is passed with an API request and used for authentication and authorization.
API security vulnerability remediation: The process of identifying and fixing vulnerabilities and security issues on an API.
API security vulnerability scanner: A software or tool that can be used to automatically scan an API for vulnerabilities and security issues.
API security vulnerability: A weakness or flaw in an API that can be exploited by malicious actors to gain unauthorized access or perform malicious actions.
API security: The practice of securing an API from various security threats such as hacking, malware, and data breaches.
API security: The process of protecting an API from unauthorized access, malicious attacks, and data breaches.
API security: The process of securing an API against potential threats and vulnerabilities, such as authentication, access controls, encryption, and rate limiting.
API simulation framework: A pre-built library or tool that can be used to create a simulated version of an API.
API simulation tool: A software or tool that can be used to create a simulated version of an API.
API simulation: The process of creating a simulated version of an API for testing or development purposes.
API specification: A document that describes the structure and format of an API, including the endpoints, parameters, responses, and error codes.
API stress testing: The process of testing an API’s performance and stability under extreme load conditions.
API testing automation frameworks: pre-built libraries and tools that can be used to automate the process of testing APIs, such as Selenium, Appium, and Cucumber.
API testing automation: The practice of automating the process of testing an API using tools such as Postman and SoapUI.
API testing automation: The process of automating the testing of an API using a testing framework or tool.
API testing automation: The process of automating the testing of an API using tools or scripts.
API testing continuous integration: The process of continuously integrating and testing new changes to an API as part of a CI/CD pipeline.
API testing coverage: The extent to which an API has been tested, measured in terms of the percentage of code or functionality that has been tested.
API testing coverage: The percentage of the functionality and features of an API that have been tested.
API testing data: The data used to test an API, including input and output data, and test cases.
API testing environment: The simulated or real-world environment in which an API is tested.
API testing framework: A pre-built library or tool that can be used to automate the process of testing APIs, such as Postman, SoapUI and Jmeter.
API testing framework: A pre-built library or tool that can be used to test an API.
API testing frameworks: pre-built libraries and tools that can be used to automate the process of testing APIs, such as JUnit, NUnit, and TestNG.
API testing load: The amount of traffic and requests sent to an API during testing, used to evaluate its performance under high load conditions.
API testing load: The process of testing an API under a heavy load to ensure that it can handle a large number of requests and users.
API testing performance: The process of measuring and evaluating the performance of an API, such as response time and throughput.
API testing report: A document that summarizes the results of an API testing, including the test cases performed, the test results, and any identified issues or bugs.
API testing results: The outcome of an API testing, including the number of test cases passed, failed, or skipped, and any identified errors or bugs.
API testing security: The process of testing the security of an API by attempting to exploit vulnerabilities and identifying potential vulnerabilities.
API testing strategy: The approach used to test an API, including the types of tests to be performed, the test environment, and the test data.
API testing strategy: The approach used to test an API, such as unit testing, integration testing, and end-to-end testing.
API testing tool: A software or tool that can be used to test the functionality, performance, and security of an API, such as Postman, SoapUI and Jmeter.
API testing tool: A software tool that can be used to test an API, such as Postman, SoapUI, and Jmeter.
API testing tools: tools and software that can be used to test the functionality and performance of APIs, such as Postman, SoapUI, and Apache JMeter.
API testing: The practice of testing the functionalities and performance of an API, to ensure it meets the requirements and specifications.
API testing: The process of evaluating the functionality, performance, and security of an API by sending requests and evaluating the responses.
API testing: The process of testing an API to ensure that it behaves as expected and meets the requirements.
API throttling: The practice of limiting the number of API requests that can be made within a certain time period in order to prevent overuse and abuse of the API.
API token: A digital token that is used to authenticate and authorize access to an API.
API transformation service: A cloud-based service offered by various vendors that provides API transformation functionality.
API transformation tool: A software tool that can be used to convert an API from one format or protocol to another.
API transformation: The process of converting an API from one format or protocol to another.
API usage policy: A set of rules and guidelines for how APIs can be used and consumed in an organization.
API versioning client compatibility: The level of compatibility between different versions of an API and the client applications that consume it.
API versioning client migration: The process of migrating client applications to a new version of an API.
API versioning client support: The process of ensuring that client applications that consume an API can work with different versions of the API.
API versioning deployment: The process of deploying a new version of an API to the production environment.
API versioning documentation: The documentation that describes the different versions of an API, including the changes and new features in each version.
API versioning management: The process of managing and maintaining different versions of an API, including updating the documentation, and deprecating or retiring older versions.
API versioning management: The process of managing multiple versions of an API and ensuring compatibility between different versions.
API versioning rollout: The process of gradually rolling out a new version of an API to users, in order to test and validate it before making it available to all users.
API versioning strategy: The approach used to manage multiple versions of an API, such as using a version number in the endpoint URL or headers.
API versioning strategy: The approach used to version an API, such as using a specific format for the version number, or using a different URL for each version.
API versioning testing: The process of testing an API version to ensure it is compatible with previous versions and that new features work as expected.
API versioning: The practice of creating multiple versions of an API to ensure backward compatibility, and to allow for new features and functionality to be added without breaking existing clients.
API versioning: The practice of creating multiple versions of an API to support backward compatibility and allow for changes and evolution over time.
API versioning: The process of maintaining and updating multiple versions of an API to ensure backwards compatibility and support for new features.
API virtualization: The practice of simulating the behavior of an API for testing and development purposes, by using tools such as WireMock, Nginx and Hoverfly.
App Store Optimization (ASO): The practice of optimizing a mobile application to improve its visibility and ranking in app store search results.
App store testing: A type of testing that focuses on ensuring the application meets the guidelines and requirements of the app store it is intended to be published in.
Application development: the process of designing, creating, testing, and maintaining software applications
Application monitoring: The practice of tracking and analyzing the performance, usage, and errors of an application in real-time.
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, including techniques such as machine learning, natural language processing, and computer vision.
Asynchronous programming: a programming paradigm where the program continues to execute independent of the completion of a specific task or operation, allowing multiple operations to be performed in parallel.
Automation: the use of software or technology to perform tasks or processes that would otherwise be done manually.
Backend development: the development of the server-side of a web application, including the database and API
Back-end development: The practice of building the server-side part of a website or web application, including the database, API, and server-side logic.
Backup and recovery: the process of creating and maintaining copies of a software application or system’s data and configurations, to be able to recover from unexpected failures or disasters.
Behavior-driven development (BDD): a software development methodology that emphasizes writing tests, that describe the desired behavior of the system, before writing the actual
Beta testing: a method of testing in which a software application or system is made available to a select group of users, typically outside of the organization, to gather feedback and identify any remaining issues before release.
Big Data: A term used to describe large and complex data sets that traditional data processing tools and techniques are unable to handle efficiently.
Big Data: the large and complex sets of data that traditional data processing and storage methods are not able to handle, but can be analyzed and mined for insights using distributed computing and specialized technologies such as Hadoop and Spark.
Blockchain: A decentralized and distributed digital ledger that records transactions across multiple computers, providing integrity and immutability.
BlockChain: A decentralized and distributed ledger technology that allows for secure and transparent record-keeping and transactions.
Blue-green deployment: the process of deploying a new version of a software application or system to a parallel environment, before switching traffic to the new version, to ensure a smooth and seamless deployment.
Branding: The practice of creating and managing a brand, including its name, logo, voice, and values, to differentiate it from competitors and create a strong and consistent image.
Bullet Point List all Application Development Terminology and Related Definitions.
Business Intelligence (BI): The practice of using data, technology, and tools to analyze and visualize business information and make data-driven decisions.
Business Intelligence (BI): the process of collecting, storing, and analyzing business data to support decision-making and strategic planning.
Business Process Management (BPM): the process of designing, automating, and continuously improving business processes to increase efficiency and reduce costs.
Canary deployment: the process of deploying a new version of a software application or system to a small subset of users, before rolling it out to the entire user base, to ensure it works as expected and identify any issues early.
Canary testing: a method of testing in which a new version of a software application or system is deployed to a small subset of users, and their behavior is monitored to ensure it does not cause any issues before it’s deployed to the entire user base.
Chatbots: computer programs designed to simulate conversation with human users, through platforms such as text messages, voice commands, or web chat interfaces.
Cloud computing: the delivery of computing services, including servers, storage, and applications, over the internet
Cloud Computing: The practice of delivering computing services and resources, such as servers, storage, and software, over the internet, on-demand and on a pay-as-you-go basis.
Cloud Native: a term used to describe the approach of building and running applications in the cloud, using technologies and architectures that are optimized for the cloud environment, such as containers, microservices, and serverless computing.
Cloud-based deployment: the practice of deploying software applications and systems to cloud-based environments, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
Cloud-based development: the practice of using cloud-based services and infrastructure to develop, test, and deploy software applications and systems.
Cloud-native development: the practice of designing and developing software applications and systems to take advantage of the unique characteristics of cloud computing environments, such as scalability, availability, and elasticity.
Code analysis: the process of automatically analyzing the code to find errors, bugs, vulnerabilities or to measure code quality.
Code generation: the process of automatically creating code, either by using a code generator tool or by using AI/ML algorithms.
Code instrumentation: the process of adding additional code to an application to enable monitoring and profiling of the application’s performance, behavior and the environment it runs on.
Code management platforms: web-based tools that provide a centralized place for storing, managing and versioning the source code of a software application or system, such as GitHub, GitLab, and Bitbucket.
Code obfuscation: The practice of making the code harder to understand and reverse engineer by using techniques such as renaming variables and functions, and using code transformations.
Code optimization: The practice of improving the performance and efficiency of the code by reducing its complexity, removing unnecessary operations and using efficient algorithms.
Code optimization: the process of improving the performance and efficiency of the code written for a software application or system, without changing its functionality.
Code quality: the degree of excellence of the code written for a software application or system, including factors such as readability, maintainability, and performance.
Code refactoring: the process of modifying existing code to improve its structure or organization, without changing its external behavior
Code review: the process of reviewing the code written by other developers, to ensure it meets the standards and is free from errors or vulnerabilities.
Code signing: The practice of using digital signature to verify the authenticity and integrity of an application.
Code style: the conventions and guidelines used to format and organize the code written for a software application or system, to ensure consistency and readability.
code, to ensure that the code meets the requirements and behaves as expected.
Compliance testing: the process of testing a software application or system to ensure it complies with relevant regulations and standards, such as HIPAA or PCI-DSS.
Computer Vision (CV): A subset of AI that deals with the interpretation and understanding of visual data from the real world, such as image and video analysis.
Computer Vision: a branch of AI that enables computers to interpret and understand visual information from the world, such as images and videos.
Concurrency: the ability of a software application or system to perform multiple tasks or operations at the same time, by using techniques such as multithreading or multiprocessing.
Configuration management: the process of tracking and controlling the changes made to the configuration of a software application or system, including versions, dependencies, and settings.
Container orchestration: the practice of automating the management, scaling, and coordination of containers, typically using tools like Kubernetes, Docker Compose, or Mesosphere.
Containerization: the practice of packaging an application and its dependencies into a container, which can be easily deployed and run on different environments
Containerization: the practice of packaging an application and its dependencies into a container, which can be run consistently across different environments.
Content Management System (CMS): A software system that allows users to create, manage, and publish digital content, such as text, images, and videos, without the need for technical expertise.
Continuous delivery (CD): the practice of automatically building, testing, and deploying software changes, as soon as they are committed to version control, and making them available to users.
Continuous deployment (CD): the practice of automatically building, testing, and deploying software changes, as soon as they are committed to version control, and making them available to users without human intervention.
Continuous deployment: a software development practice in which code changes are automatically built, tested, and deployed to production
Continuous integration (CI): the practice of automatically building, testing, and deploying software changes, as soon as they are committed to version control.
Continuous integration: a software development practice in which developers regularly merge their code changes into a central repository
Continuous testing: the process of automating the testing of software applications and systems as part of the development process, to ensure that code changes do not introduce new bugs or break existing functionality.
Cross-platform app development: The practice of building mobile applications that can run on multiple platforms using a single codebase, using tools such as React Native, Xamarin, and Flutter.
CSS: A stylesheet language used to define the layout and design of a web page.
Customer Relationship Management (CRM): A type of software that manages and automates a company’s interactions with customers, including sales, marketing, and customer service.
Data mining: The process of discovering patterns and insights from large data sets using techniques from statistics, machine learning, and database systems.
Data modeling: the process of creating a conceptual representation of data, including entities and relationships, to be used in a software application
Data visualization: The practice of representing data in a visual format, such as charts, graphs, maps, and dashboards, to make it easier to understand and interpret.
Data warehousing: The practice of collecting, storing, and managing large amounts of data from various sources for the purpose of reporting and analysis.
Debugging: the process of finding and resolving errors or bugs in a software application
Deep Learning (DL): A subset of ML that utilizes neural networks with multiple layers to learn and extract features and patterns from data.
Deep Learning: a subset of machine learning that uses neural networks, which are modeled after the human brain, to perform tasks such as image recognition and natural language processing.
Dependency management: The practice of managing the external libraries and modules that an application relies on.
Deployment: the process of making a software application or system available to end-users, either by installing it on a local machine or by making it available over the internet.
Design patterns: a reusable solution to common software design problems, such as the observer pattern or the singleton pattern.
Design patterns: reusable solutions to common software design problems, such as the observer pattern or the singleton pattern.
DevOps: a set of practices that combines software development and IT operations to improve the speed, quality, and reliability of software releases
DevOps: the practice of bringing development and operations teams together, to collaborate, automate, and optimize the software development and delivery process.
Disaster recovery: the process of planning and implementing measures to protect a software application or system and its data from disasters, and to be able to recover quickly in case of an incident.
Encapsulation: a feature of OOP that allows an object to hide its internal state and behavior, and expose only a public interface.
Enterprise Resource Planning (ERP): A type of software that manages and integrates a company’s core business processes, such as finance, human resources, and operations.
Error handling: the process of managing and responding to errors or exceptions that occur in a software application or system.
Error tracking: The practice of automatically collecting and analyzing error reports from an application in order to identify and fix bugs.
Event sourcing: A technique for storing the state of an application by recording the sequence of events that led to its current state.
Event-driven architecture: a software architecture pattern that allows systems to respond to certain events in real-time, as they occur, rather than on a pre-determined schedule.
Event-driven programming: a programming paradigm where the program responds to specific events or triggers, rather than executing a specific sequence of instructions.
Exception handling: the process of dealing with unexpected or exceptional events or conditions in a software application or system, typically through the use of try-catch statements or error handling functions.
Extensibility: the ability of a software application or system to be easily modified or extended to add new features or capabilities.
Feature flags: a technique that allows teams to enable or disable certain features of a software application or system, without having to deploy new code or perform a full release.
Flexibility: the ability of a software application or system to adapt to changing requirements and environments.
Frontend development: the development of the client-side of a web application, including the user interface and user experience
Front-end development: The practice of building the user-facing part of a website or web application, including the layout, design, and interactivity.
Full-stack development: the development of both the frontend and backend of a web application
Full-stack development: The practice of building both the front-end and back-end of a website or web application.
Functional programming: a programming paradigm that emphasizes the use of functions to manipulate data, rather than objects
Functional programming: a programming paradigm where the program is composed of a series of pure functions, that do not cause any side-effects and always return the same output for the same input.
Functional testing: a method of testing that checks if a software application or system is performing its intended functions correctly.
Functional testing: A type of testing that focuses on testing the functionality and features of the application, to ensure they meet the requirements and specifications.
Game development: the development of video games, including both the creation of the game itself and the development of the tools and technologies used to create it
Garbage collection: a technique used in some programming languages, where the system automatically frees up memory that is no longer being used by the application, to prevent memory leaks.
Git: a widely-used version control system that allows for distributed development, branching, merging, and tracking of changes.
High availability: the process of designing and configuring a software application or system to be able to continue operating, even in case of failures or outages.
HTML: A markup language used to structure and format the content of a web page.
Human-computer interaction (HCI): the study of how people interact with computers and how to design technology that is easy to use and meets the needs of the users.
Human-Computer Interaction (HCI): The study of how people interact with computers and how to design technology that is easy to use and meets the user’s needs and expectations.
Hybrid Mobile Application: A mobile application that is built using web technologies and can run on multiple platforms.
In-app purchases: A feature that allows users to purchase digital goods or services from within a mobile application.
Information Architecture (IA): The practice of organizing and structuring information, such as content and navigation, to make it easy to find and understand.
Inheritance: a feature of OOP that allows an object to inherit properties and methods from another object, called the parent or superclass.
Integration testing: a method of testing in which different parts of a software application or system are combined and tested to ensure they work together correctly.
Integration testing: A type of testing that focuses on testing the interactions and interfaces between different components or systems, to ensure they work together as expected.
Internationalization (i18n): the process of designing software applications and systems to be able to support multiple languages and cultures.
Internet of Things (IoT): the interconnectivity of everyday devices, such as appliances and vehicles, through the internet, allowing them to send and receive data.
Internet of Things (IoT): The network of physical objects embedded with sensors, software, and network connectivity, which enables them to collect and exchange data.
Interoperability: the ability of a software application or system to work with other software applications or systems, to exchange data or perform a common function.
Java: A programming language used for developing Android applications.
JavaScript: A programming language commonly used for front-end web development, to create interactive and dynamic user interfaces.
Kanban: a methodology for managing and improving workflows, that emphasizes visualizing work, limiting work-in-progress, and making process policies explicit.
Kotlin: A programming language developed by JetBrains, which is officially supported by Google for developing Android applications.
Kubernetes : An open-source container orchestration system for automating the deployment, scaling, and management of containerized applications.
Lean development: a software development methodology that emphasizes eliminating waste, optimizing flow, and delivering value to customers, based on the principles of Lean manufacturing.
Load testing: the process of testing a software application or system to determine how well it performs under a specific load, such as a large number of concurrent users or requests.
Localization (L10n): the process of adapting software applications and systems to specific languages and cultures.
Log analysis: The practice of collecting and analyzing log files from an application in order to troubleshoot issues and monitor performance.
Logging: the process of recording and storing the events and messages generated by a software application or system, to be able to diagnose and troubleshoot issues.
Low-code development: a method of developing software applications using visual drag-and-drop interfaces, rather than writing code, to increase development speed and reduce the need for specialized technical skills.
Machine Learning (ML): A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
Machine Learning: a subset of AI that enables computers to learn from data, without being explicitly programmed.
Maintainability: the ability of a software application or system to be easily maintained and updated over time, to fix bugs, add new features, or improve performance.
Maintenance: the ongoing process of updating, modifying, and maintaining a software application or system after it has been released.
Marketing Automation: The practice of using software and technology to automate and optimize marketing tasks, such as email campaigns, lead generation, and analytics.
Memory management: the process of allocating and managing the memory used by a software application or system, to ensure that it does not run out of memory and cause errors.
Mercurial: a distributed version control system that allows for collaboration, branching, merging, and tracking of changes.
Metrics: the data points and measurements used to monitor and evaluate the performance, availability, and health of a software application or system.
Microservices: a software architecture in which a large application is broken down into small, independent services that can be developed, deployed, and scaled independently
Microservices: a software architecture pattern that structures an application as a collection of small, loosely-coupled services, that can be developed, deployed, and scaled independently.
Mobile advertising: The practice of promoting mobile applications through online advertising platforms such as Google Adwords and Facebook Ads.
Mobile analytics: The practice of collecting, analyzing, and interpreting data from mobile applications, to understand user behavior and improve the app’s performance.
Mobile app testing: The practice of testing a mobile application to ensure its functionality, usability, and performance on various devices and platforms.
Mobile Backend as a Service (MBaaS): A cloud-based service that provides a backend infrastructure for mobile applications, including server-side logic, storage, and push notifications.
Mobile development: the development of software applications for mobile devices such as smartphones and tablets
Mobile development: The practice of creating, designing, and maintaining mobile applications for various platforms such as iOS, Android, and Windows.
Mobile security: The practice of protecting mobile applications and devices from various security threats such as hacking, malware, and data breaches.
Modularity: the ability of a software application or system to be broken down into smaller, independent units or modules, which can be developed, tested, and maintained separately.
Monitoring: the process of observing and measuring the performance, availability, and health of a software application or system, to detect and diagnose issues.
Multivariate testing: a method of testing in which multiple variables of a software application or system are changed and tested simultaneously to determine which combination results in the best performance.
Native app development: The practice of building mobile applications that are specifically designed and developed for a particular platform, using the platform’s native programming languages and SDKs.
Natural Language Processing (NLP): a branch of AI that enables computers to understand, interpret, and generate human language, such as speech and text.
Natural Language Processing (NLP): A subset of AI that deals with the interaction between computers and humans using natural language, such as speech recognition, text analysis and generation.
NoSQL: A type of database that does not use a fixed schema and is optimized for handling large amounts of unstructured or semi-structured data.
NoSQL: a type of database that does not use a fixed schema and is optimized for storing and querying large amounts of unstructured data
Object-C: A programming language used for developing iOS applications.
Object-oriented programming (OOP): a programming paradigm that emphasizes the use of objects, which have properties and methods, to represent and manipulate data.
Object-oriented programming (OOP): a programming paradigm that uses objects, which have properties and methods, to represent and manipulate data
Observability: the ability to understand the internal states and behavior of a software application or system, by collecting and analyzing monitoring data.
Pair programming: a technique in which two developers work together at one computer, with one typing and the other reviewing and suggesting changes in real-time, to improve quality, collaboration, and knowledge sharing.
Parallelism: the ability of a software application or system to perform multiple tasks or operations at the same time, by using multiple cores or processors.
Penetration testing: A type of security testing that simulates a real-world attack on an application or system in order to identify vulnerabilities and weaknesses.
Penetration testing: the process of simulating an attack on a software application or system to identify vulnerabilities and assess its security.
Performance engineering: a method of testing that focuses on how well a software application or system performs under different conditions, such as varying loads, network conditions, and hardware configurations.
Performance monitoring: the process of constantly monitoring the performance of a software application or system to ensure that it is operating within acceptable parameters and quickly identify and resolve issues.
Performance optimization: the process of improving the speed and efficiency of a software application
Performance testing: A type of testing that focuses on measuring the performance and scalability of the application, under various loads and conditions.
Performance testing: the process of testing a software application or system to ensure it can handle the expected workload and meet performance requirements.
Polymorphism: a feature of OOP that allows an object to take on different forms or behaviors, depending on the context in which it is used.
Portability: the ability of a software application or system to run on different platforms, devices, or environments.
Profiling: the process of measuring and analyzing the performance of a software application or system, to identify and optimize bottlenecks or slow areas.
Progressive Web Application (PWA): A web application that uses modern web technologies to provide a native-like experience, such as offline support and push notifications.
Prototyping: the process of creating a preliminary model or sample of a software application or system, to test and gather feedback on its functionality, usability, and design.
Push notifications: A feature that allows mobile applications to send messages or alerts to users even when the application is not running.
Quality assurance (QA): the process of testing and evaluating a software application or system to ensure it meets the required quality standards.
Rapid application development (RAD): a software development methodology that emphasizes rapid prototyping, user involvement, and iterative development.
Refactoring: the process of improving the design and structure of existing code, without changing its functionality, to make it more maintainable, readable, and efficient.
Regression testing: a method of testing in which previously working functionality is retested after changes have been made to ensure that the changes have not introduced any new bugs or broken existing functionality.
Reinforcement Learning: a subset of machine learning that focuses on training models to make decisions by maximizing a reward function.
Release management: the process of planning, scheduling, and coordinating the release of a software application or system, including testing, deployment, and rollback.
Responsive design: A design approach that ensures that a website or application adapts to different screen sizes and devices, to provide an optimal user experience.
Responsive design: a web design approach that enables a website to adapt to the device and screen size of the user.
Reusability: the ability of a software application or system to be easily reused in other projects or contexts.
Robotics: The branch of engineering and computer science that deals with the design, construction, and operation of robots.
Robotics: the branch of engineering that deals with the design, construction, operation, and use of robots.
Rollback: the process of undoing a deployment and restoring a previous version of a software application or system in case of issues or errors.
Rolling deployment: the process of deploying a new version of a software application or system to a small subset of servers or instances at a time, before rolling it out to the entire fleet, to minimize disruption and ensure high availability during the deployment.
Ron Legarski Application Manager: General Electrician and Telecommunications Advisor.
Root cause analysis: the process of identifying the underlying cause of an issue or problem in a software application or system, to be able to take appropriate actions to fix it.
Root cause analysis: the process of identifying the underlying cause of an issue or problem in a software application or system.
Scalability: the ability of a software application or system to handle an increasing amount of load or traffic, either by adding more resources or by distributing the load across multiple machines.
Scrum: an Agile framework for managing and completing complex projects
Scrum: an Agile framework for managing and completing complex projects, that emphasizes teamwork, accountability, and iterative progress.
Search Engine Optimization (SEO): The practice of optimizing a website or application to improve its visibility and ranking in search engine results pages.
Security testing: The practice of testing an application or system to identify and mitigate vulnerabilities and weaknesses.
Security testing: the process of testing a software application or system to identify and mitigate security risks and vulnerabilities.
Security: the practices and measures taken to protect a software application and its data from unauthorized access or malicious attacks
Serverless computing: a method of building and running applications and services without having to manage and provision the underlying servers.
Serverless computing: the practice of building and running applications and services without the need to provision or manage servers, using cloud-provided services such as AWS Lambda, Azure Functions, or Google Cloud Functions.
Single Page Application (SPA): A web application that loads a single HTML page and dynamically updates the content as the user interacts with the application.
Smoke testing: a method of testing that checks if the basic functionality of a software application or system is working correctly, before performing more in-depth testing.
Social Media Optimization (SMO): The practice of optimizing a website or application to improve its visibility and engagement on social media platforms.
Software architecture: the high-level structure of a software system, including the components and their relationships
Source code management: the practice of managing and organizing the source code of a software application or system, including version control, branching, merging, and collaboration.
SQL: a programming language used for managing relational databases
SQL: A type of database that uses a structured query language to interact with the data and is optimized for handling structured and relational data.
Stress testing: the process of testing a software application or system to determine how well it performs under extreme loads, such as a large number of concurrent users or requests beyond its expected capacity.
Supply Chain Management (SCM): The practice of managing and optimizing the flow of goods, services, and information from suppliers to customers.
SVN: a version control system that uses a centralized repository to store different versions of the code, and allows for collaboration, branching, and merging of changes.
Swift: A programming language developed by Apple for developing iOS and macOS applications.
Synchronous programming: a programming paradigm where the program waits for a specific task or operation to complete before continuing execution, allowing only one operation to be performed at a time.
System testing: a method of testing that checks if the entire software application or system is working correctly.
Test-driven development (TDD): a software development methodology that emphasizes writing automated tests, before writing the actual code, to ensure that the code meets the requirements and is of high quality.
Test-driven development (TDD): a software development process in which tests are written before any code, to ensure that the code meets the requirements
Tracing: the process of tracking and recording the execution of a software application or system, to identify and diagnose issues, such as performance bottlenecks or errors.
Unit testing: a method of testing individual units or components of a software application to ensure they function as intended.
Unit testing: A type of testing that focuses on individual units or components of the code, to ensure they work as expected.
User acceptance testing: A type of testing that focuses on testing the application with real users, to gather feedback and ensure it meets their needs and expectations.
User experience (UX) design: the process of designing software applications and systems to be easy to use and meet the needs of the users.
User Experience (UX): The overall experience of a user when interacting with a product, service, or system, including aspects such as usability, accessibility, and emotional design.
User interface (UI) design: the process of designing the visual layout and interactions of a software application or system.
User Interface (UI): The part of a product, service, or system that a user interacts with, including the layout, design, and controls.
Version control: the management of changes to a software application over time, using tools such as Git or SVN
Version control: the practice of tracking and managing changes to the codebase, by storing different versions of the code and allowing for collaboration, rollbacks, and merging of changes.
Virtual Reality (VR) and Augmented Reality (AR): technologies that allow users to experience an immersive, computer-generated environment, either in place of or in addition to the real world.
Virtualization: the practice of creating a virtual version of a resource, such as a server, storage, or network, that can be accessed and managed remotely.
Waterfall model: a software development methodology that emphasizes a linear and sequential approach, with distinct phases for requirements, design, implementation, testing, and maintenance.
Web development: The practice of creating, designing and maintaining websites and web applications.