Glossary Of 90 Software Engineering Terms For Recruiters And HR Managers
In today's digital age, software engineering has become an integral part of many industries and companies. For example, the U.S. Bureau of Labor Statistics has found that software developers are expected to see the third-largest increase in jobs of any occupation over the 2021-31 decade.
As a result, recruiters and HR managers are often tasked with finding and hiring skilled software engineers to fill critical roles within their companies. However, that field is complex and constantly evolving, with a wide range of technical terms and jargon that may be unfamiliar to those outside of the industry.
By familiarising themselves with software engineering concepts and terminology, recruiters and HR managers can more effectively navigate the ever-changing landscape of software engineering, properly identify and evaluate suitably skilled candidates who can drive success for the business.
Agile development: An iterative approach to software development that emphasises collaboration, flexibility, and customer satisfaction.
Algorithm: A step-by-step procedure for solving a problem or performing a task, often used in software development.
API: An acronym for Application Programming Interface, a set of protocols and tools for building software applications.
API endpoint: The specific location or URL where a software application's API can be accessed.
API gateway: A server that acts as an intermediary between a client and a collection of microservices, providing a unified API and handling tasks such as authentication and load balancing.
Artificial intelligence: The simulation of human intelligence in machines, typically involving techniques such as machine learning, natural language processing, and computer vision.
Asynchronous programming: A programming paradigm in which code execution does not happen in a linear, synchronous manner, but instead uses callbacks, promises, or other mechanisms to handle events or delays.
Automation testing: The process of using software tools to automate the execution of tests, reducing manual effort and improving efficiency.
Back-end: The part of a software application or system that handles data storage, processing, and logic behind the scenes.
Backend as a service: A cloud-based service that provides infrastructure and tools for building backend functionality, such as data storage and authentication, without requiring the development team to manage the underlying infrastructure.
Big data: Large volumes of complex data that require specialised tools and techniques for storage, processing, and analysis.
Bug: An error or defect in software code that causes it to behave unexpectedly or incorrectly.
Business logic: The code and rules that define how a software application or system operates, including data processing, calculations, and workflows.
Client-server architecture: A software architecture pattern in which a client application communicates with a central server to exchange data and perform tasks.
Cloud computing: The delivery of computing services over the internet, including storage, processing power, and software applications, typically provided by third-party providers.
Code review: The process of examining code written by other developers to identify potential issues or improvements, typically done by other members of the development team.
Continuous delivery: A software development approach in which code changes are automatically built, tested, and deployed to production, often using a pipeline or workflow tool.
Continuous integration: A software development practice where developers frequently merge their code changes into a shared repository, allowing for early detection and resolution of conflicts and errors.
Cross-platform development: The process of building software applications or systems that can run on multiple operating systems or platforms, typically using frameworks or development tools that support this capability.
Cryptography: The practice of using mathematical algorithms and protocols to secure and protect data, communications, and transactions.
Data analytics: The process of analysing and interpreting large volumes of data to identify patterns, trends, and insights, often using specialised tools and techniques.
Data structures: The way data is organised and stored in a software application or system, typically using concepts such as arrays, lists, trees, and graphs.
Database: A software application that stores and organises data, typically using a structured query language (SQL) to interact with the data.
Debugging: The process of identifying and fixing errors, bugs, or issues in software code, typically using tools or techniques such as breakpoints and logging.
Dependency injection: A programming technique in which the dependencies of a software component or module are provided externally, rather than being created within the object itself, typically to increase flexibility and maintainability.
DevOps: A software development approach that emphasises collaboration and integration between development teams (Dev) and operations teams (Ops), often using automation and continuous delivery practices.
Docker: A platform for building, shipping, and running distributed applications, typically using containers to encapsulate and isolate different components or services.
Domain-driven design: A software development approach that emphasises modelling software based on business or domain concepts, often using domain-specific languages (DSLs) or patterns.
Dynamic programming: A programming technique in which solutions to complex problems are computed by breaking them down into smaller sub-problems, often using memoisation or tabulation to avoid redundant computations.
Event-driven architecture: A software architecture pattern in which components or services communicate by sending and receiving events or messages, often using a message broker or event bus.
Exception handling: The process of handling and recovering from errors or exceptions that occur during the execution of software code, typically using try-catch blocks or similar constructs.
Framework: A set of pre-written code libraries and tools that provide a foundation for building software applications.
Front-end: The part of a software application or system that users interact with, including the user interface and user experience.
Functional programming: A programming paradigm that emphasises functions as the primary building blocks of software, often using concepts such as immutability, higher-order functions, and lambda expressions.
Git: A popular distributed version control system for managing software code and project files, typically used in software development teams.
GraphQL: A query language and runtime for APIs that provides a flexible, efficient, and type-safe way to request and deliver data, often used in web and mobile applications.
Grid computing: A type of distributed computing that involves connecting and coordinating computing resources from multiple machines or clusters to perform large-scale computations or simulations.
IDE: An acronym for Integrated Development Environment, a software application that provides a comprehensive environment for software development, including features such as code editing, debugging, and testing.
IDE plugins: Extensions or add-ons to an integrated development environment (IDE) that provide additional functionality or integration with other tools.
Infrastructure as code: The practice of managing and provisioning infrastructure resources, such as servers and networks, using code and automation tools, often using configuration management tools or infrastructure-as-code frameworks.
Internet of Things (IoT): A network of physical devices, vehicles, home appliances, and other items embedded with sensors, software, and connectivity that enables them to collect and exchange data over the internet.
Iterative development: A software development approach in which software is developed and delivered incrementally and continuously, allowing for ongoing feedback and adaptation.
Jenkins: A popular open-source automation server used for building, testing, and deploying software projects.
Kubernetes: An open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications.
Machine learning: A branch of artificial intelligence that involves building/training algorithms and models that enable computers to learn from data and improve their performance over time.
Microservices: A software architecture pattern in which a large application is broken down into smaller, independently deployable services that can be developed, deployed, and scaled separately.
MVC: An acronym for Model-View-Controller, a design pattern that separates an application into three interconnected components: the model (data), the view (user interface), and the controller (logic and input handling).
Object-oriented programming (OOP): A programming paradigm that emphasises the use of objects and classes to model real-world entities and their relationships, often using concepts such as encapsulation, inheritance, and polymorphism.
Open-source software: Software that is freely available and can be modified and redistributed by anyone.
Pair programming: A software development technique in which two developers work together at one computer, with one person writing code (driver) and the other person reviewing it in real-time (navigator).
Performance testing: The process of testing a software application or system to evaluate its performance under various conditions and loads.
Platform as a Service (PaaS): A cloud computing service model that provides a platform for building, testing, deploying, and managing software applications without the need for infrastructure management.
Polymorphism: A concept in object-oriented programming that allows objects of different classes to be treated as if they belong to the same class.
Progressive Web Apps (PWA): Web applications that use modern web capabilities to provide an app-like experience, including offline functionality, push notifications, and access to device hardware.
Python: A high-level, interpreted programming language that is widely used for web development, data analysis, scientific computing, and artificial intelligence.
Quality assurance (QA): The process of ensuring that a software product or system meets specified quality standards and requirements, often through testing and quality control measures.
Refactoring: The process of restructuring and improving existing software code without changing its external behaviour.
Regression testing: The process of testing software code to ensure that changes or updates have not introduced new bugs or issues, nor caused unintended side effects or regressions.
REST (Representational State Transfer): A software architecture style for building web services that use HTTP methods (GET, POST, PUT, DELETE) to perform CRUD (Create, Read, Update, Delete) operations on resources.
RESTful API: An API that adheres to the principles of Representational State Transfer (REST), a web services architecture that emphasises simplicity, scalability, and flexibility.
SaaS: An acronym for Software as a Service, a cloud computing service that provides software applications to users over the internet.
Scalability: The ability of a software application or system to handle increasing amounts of data, users, or traffic without significant performance degradation.
Scrum: An agile project management framework for software development that emphasises collaboration, flexibility, and iterative development.
SDLC: An acronym for Software Development Life Cycle, a process for developing software that includes planning, analysis, design, implementation, testing, and maintenance.
Security testing: The process of testing software for vulnerabilities, weaknesses, and potential security threats, often using tools and techniques such as penetration testing, vulnerability scanning, and code review.
Serverless computing: A cloud computing model in which applications are built and run on a platform that manages the underlying infrastructure, including scaling and availability.
Single-page application: A web application that loads a single HTML page and dynamically updates the content as the user interacts with it.
Software architecture: The overall structure and organisation of software systems, including components, interfaces, and relationships.
SOLID principles: A set of five design principles in software development that aim to improve code quality, maintainability, and scalability.
Sprints: Fixed periods of time, typically one to four weeks, during which a software development team works on a specific set of features or tasks.
Static analysis: The process of analysing software code without actually executing it, typically using specialised tools that can detect potential errors, security vulnerabilities, or performance issues.
Systems integration: The process of combining different software systems or components into a larger, functioning system.
Test-driven development (TDD): A software development approach in which tests are written before writing code, with the goal of ensuring that code meets specified requirements and reducing errors.
Thread: A basic unit of CPU processing that can execute one or more tasks simultaneously, often used in multi-threaded software applications.
Unit testing: The process of testing individual units or components of a software application or system to ensure they function as intended.
Usability testing: The process of testing a software application or system with real users to evaluate its usability and user experience.
User acceptance testing: The process of testing a software application or system with representative users to ensure it meets their needs and requirements.
User experience (UX) design: The process of designing and optimising the user experience of a software product or system, often through user research, prototyping, and usability testing.
User interface (UI) design: The process of creating the visual elements and controls of a software application or system that enable users to interact with and manipulate data and functionality.
User story: A brief, user-focused description of a software application's functionality or feature.
Version control: The practice of tracking and managing changes to software code and other digital assets over time, often using a version control system like Git.
Versioning: The practice of assigning unique identifiers or version numbers to different releases/versions of a software application or system, typically using a version control system.
Virtual machine (VM): A software emulation of a physical computer system that can run multiple operating systems or applications on the same hardware.
Waterfall model: A traditional software development approach that follows a linear, sequential process with distinct phases for planning, analysis, design, implementation, testing, and maintenance.
Web services: Software components that enable different applications to communicate with each other over the internet, often using APIs (Application Programming Interfaces).
Wireframe: A visual blueprint or schematic of a software application or system, typically used to plan and design the layout and functionality of a user interface.
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