Types of software include system software, programming software, application software and embedded software:

• System software provides core functions such as operating systems, disk management, utilities, hardware management and other operational necessities.
  
  
• Programming software gives programmers tools such as text editors, compilers, linkers, debuggers and other tools to create code.
  
  
• Application software (applications or apps), such as office productivity suites, data management software, media players and security programs help users complete specific tasks. Applications also refer to web and mobile applications such as those used to shop on retail websites or interact with content on social media sites.  
  
  
• Embedded software is used to control devices not typically considered computers including telecommunications networks, cars, industrial robots and more. These devices and their software, can be connected as part of the Internet of Things (IoT).

Software can be designed as custom software or commercial software. Custom software development is the process of designing, creating, deploying and maintaining software for a specific set of users, functions or organizations.

In contrast, commercial off-the-shelf software (COTS) is designed for a broad set of requirements, enabling it to be packaged and commercially marketed and distributed.

Programmers, software engineers and software developers primarily conduct software development. These roles interact, overlap and have similar requirements, such as writing code and testing software. The dynamics between them vary greatly across development departments and organizations.  
 

Programmers (coders)
 

Programmers, or coders, write source code to program computers for specific tasks such as merging databases, processing online orders, routing communications, conducting searches or displaying text and graphics. They also debug and test software to make sure the software does not contain errors. 

Programmers typically interpret instructions from software developers and engineers and use programming languages such as C++, Java™, JavaScript and Python to implement them.
 

Software engineers 
 

Software engineers design, develop, test and maintain software applications. As a managerial role, software engineers engage in problem solving with project managers, product managers and other team members to account for real-world scenarios and business goals. Software engineers consider full systems when developing software, making sure that operating systems meet software requirements and that various pieces of software can interact with each other. 

Beyond the building of new software, engineers monitor, test and optimize applications after they are deployed. Software engineers oversee the creation and deployment of patches, updates and new features. 
 

Software developers 
 

Like software engineers, software developers design, develop and test software. Unlike engineers, they usually have a specific, project-based focus. 

A developer might be assigned to fix an identified error, work with a team of developers on a software update or to develop a specific aspect of a new piece of software. Software developers require many of the same skills as engineers but are not often assigned to manage full systems. 

The software development life cycle (SDLC) is a step-by-step process that development teams use to create high-quality, cost-effective and secure software. The steps of the SDLC are:

• Planning
• Analysis
• Design
• Implementation
• Testing
• Deployment
• Maintenance

These steps are often interconnected and might be completed sequentially or in parallel depending on the development model an organization uses, the software project and the enterprise. Project managers tailor a development team’s workflows based on the resources available and the project goals.

The SDLC includes the following tasks, though the tasks might be placed in different phases of the SDLC depending on how an organization operates.

Requirements management

The first step of planning and analysis is to understand what user needs the software should be designed to meet and how the software contributes to business goals. During requirements management, analysis or requirements gathering, stakeholders share research and institutional knowledge such as performance and customer data, insights from past developments, enterprise compliance and cybersecurity requirements and the IT resources available.

This process enables project managers and development teams to understand the scope of the project, the technical specifications and how tasks and workflows are organized.
 

Developing a design
 

After establishing project requirements, engineers, developers and other stakeholders explore the technical requirements and mock up potential application designs. Developers also establish which application programming interfaces (APIs) will connect the application with other applications, systems and user interfaces. Sometimes existing APIs can be used, other times new APIs are needed.
 

Building a model

In this step, teams build an initial model of the software to conduct preliminary testing and discover any obvious bugs. DevOps teams can use modeling language such as SysML or UML to conduct early validation, prototyping and simulation of the design.

Constructing code
 

Using the knowledge gained by modeling, software development teams begin to write the code that turns the designs into a functioning product. Traditionally writing code is a manual process, but organizations are increasingly using artificial intelligence (AI) to help generate code and speed the development process.
 

Testing
 

Quality assurance (QA) is run to test the software design. The tests look for flaws in the code and potential sources of errors and security vulnerabilities. DevOps teams use automated testing to continuously test new code throughout the development process.
 

Deploying

A software integration, deployment or release means that the software is made available to users. Deployment involves setting up database and server configurations, procuring necessary cloud computing resources and monitoring the production environment. Development teams often use infrastructure as code (IaC) solutions to automate the provisioning of resources. Such automations help simplify scaling and reduce costs.

Often organizations use preliminary releases, such as beta tests, before releasing a new product to the public. These tests release the product to a selected group of users for testing and feedback and enable teams to identify and address unforeseen issues with the software before a public release.
 

Optimization
 

After deployment, DevOps teams continue to monitor and test the performance of the software and perform maintenance and optimization whenever possible. Through a process called continuous deployment, DevOps teams can automate the deployment of updates and patches without causing service disruptions.
 

Documentation
 

Keeping a detailed accounting of the software development process helps developers and users troubleshoot and use applications. It also helps maintain the software and develop testing protocols.

Software development models are the approach or technique that teams take to software development. They dictate the project workflow, how tasks and processes are completed and checked, how teams communicate and more.

When selecting a model for development, project managers consider the scope of the project, the complexity of the technical requirements, the resources available, the size and experience of the team, the deadline for release and the budget.

Common software development models include:
 

Waterfall
 

Waterfall is a traditional software development model that sets a series of cascading linear steps from planning and requirements gathering through deployment and maintenance. Waterfall models are less flexible than agile methodologies. Development can be delayed if a step is not completed and it is often costly and time-consuming to revert to previous steps if an issue is discovered. This process can be valuable for simple software with few variables.

V-shaped
 

This model creates a V-shaped framework with one leg of the “V” following the steps of the SDLC and the other leg dedicated to testing. Like the waterfall approach, V-shaped models follow a linear series of steps.

The main difference is that V-shaped development has associated testing built into each step that must be completed for development to proceed. Robust software testing can help identify issues in code early but has some of the same shortcomings of the waterfall effect—it is less flexible and can be difficult to revert to a previous step.
 

Iterative

The iterative model focuses on repeated cycles of development, with each cycle addressing a specific of requirements and functions. Each cycle or iteration of development adds and refines functions and is informed by previous cycles. The principles of the iterative model, mainly the cyclical nature of working, can be applied to other forms of development.
 

Agile
 

This iterative approach to software development breaks larger projects into smaller “sprints” or consumable functions and delivers rapidly on those functions through incremental development. A constant feedback loop helps find and fix defects and enables teams to move more fluidly through the software development process.
 

DevOps
 

The DevOps approach is a further development of the agile model. DevOps combines the work of development and IT operations teams and uses automation to optimize the delivery of high-quality software. DevOps increases visibility across teams and prioritizes collaboration and input from all stakeholders throughout the software development lifecycle.

It also uses automation to test, monitor and deploy new products and updates. DevOps engineers take an iterative approach, meaning software is continuously tested and optimized to improve performance.
 

Rapid application development (RAD)
 

This process is a type of agile development that places less emphasis on the planning stage and focus on an adaptive process influenced by specific development conditions. RAD prioritizes receiving real-world user feedback and making updates to software after deployment rather than trying to plan for all possible scenarios.

Spiral

A spiral model combines elements of both waterfall and iterative approaches. Like the waterfall model, a spiral development model delineates a clear series of steps. But it also breaks down the process into a series of loops or “phases” that give development teams more flexibility to analyze, test and modify software throughout the process.

The visual representation of these models takes the form of a spiral, with the beginning planning and requirements gathering step as the center point. Each loop or phase represents the entire software delivery cycle. At the start of each new phase, teams can modify requirements, review testing and adjust any code as needed. The spiral model offers risk-management benefits and is ideal for large, complex projects.
 

Lean
 

A type of agile development, lean development takes principles and practices from the manufacturing world and applies them to software development. The goal of lean development is to reduce waste at every step of the SDLC. To do this, lean models set a high standard for quality assurance at every step of development, prioritize faster feedback loops, remove bureaucratic processes for decision making and delay the implementation of decisions until accurate data is available.

While traditional agile development is largely focused on the optimization of software, lean development is also concerned with the optimization of development processes to achieve this goal.
 

Big bang
 

Unlike all other development models, big band development does not begin with a robust planning phase. It is based on time, effort and resources—meaning work begins when the time, personnel and funding are available. Developers create software by incorporating requirements as they filter in throughout the process.

Big bang development can be a quick process, but due to the limited planning phase, it risks the creation of software that does not meet user needs. Because of this, the big bang model is best suited for small projects that can be updated quickly.

Using software development to differentiate from competition and gain competitive advantage requires proficiency with the techniques and technologies that can accelerate software deployment, quality and efficacy.

There are different types of software development, geared toward different parts of the tech stack or different deployment environments. These types include:
 

Cloud-native development

Cloud-native development is an approach to building and deploying applications in cloud environments. A cloud-native application consists of discrete, reusable components known as microservices. These microservices act as building blocks used to compile larger applications and are often packaged in containers.

Cloud-native development and practices like DevOps and continuous integration work together because of a shared emphasis on agility and scalability. Cloud-native applications enable organizations to take advantage of cloud computing benefits such as automated provisioning through infrastructure as code (IaC) and more efficient resource use.
 

Low-code development
 

Low-code is a visual approach to software development that enables faster delivery of applications through minimal hand-coding. Low-code software development platforms offer visual features that enable users with limited technical experience to create applications and make a contribution to software development.

Experienced developers also benefit from low-code development by using built-in application programming interfaces (APIs) and prebuilt code components. These tools promote faster software development and can eliminate some of the bottlenecks that occur, such as when project managers or business analysts with minimal coding experience are involved in the development process.

Front-end development
 

Front-end development is the development of the user-facing aspect of software. It includes designing layouts and interactive elements and plays a large role in the user experience. Poor front-end development resulting in a frustrating user experience can doom software, even if it’s technically functional.
 

Back-end development
 

Back-end development is concerned with the aspects that the user doesn’t see, such as building the server-side logic and infrastructure that software needs to function. Back-end developers write the code that determines how software accesses, manages and manipulates data; defines and maintains databases to make sure they work with the front end; sets up and manage APIs and more.
 

Full-stack development
 

A full-stack developer is involved in both front and back-end development and is responsible for the entire development process. Full-stack development can be a useful in bridging any divide between the technical aspects of running and maintaining software and the user experience, creating a more holistic approach to development.

Artificial intelligence (AI) tools play an increasingly important role in software development. AI is used to generate new code, review and test existing code and applications, help teams continuously deploy new features and more. AI solutions are not a subsitute for human development teams. Rather, these tools are used to enhance the development process, creating more productive teams and stronger software. 

Code generation

Generative AI can create code snippets and full functions based on natural language prompts or code context. Using large language model (LLM) technologies, natural language processing (NLP) and deep learning algorithms, technical professionals train generative AI models on massive datasets of existing source code. Through this training, AI models begin to develop a set of parameters—an understanding of coding language, patterns in data and the relationship between different pieces of code. An AI-powered code generator can help developers in several ways, including:

Autocompletion

When a developer is writing code, generative AI tools can analyze the written code and its context and suggest the next line of code. If appropriate, the developer can accept this suggestion. The most obvious benefit is that this helps save the developer some time. This can also be a useful tool for developers working in coding languages they are not the most experienced in or haven’t worked with in a while.

Writing original code

Developers can directly prompt AI tools with specific plain language prompts. These prompts include specifications such as programming language, syntax and what the developer wants the code to do. Generative AI tools can then produce a snippet of code or an entire function; developers then review the code making edits when needed. These corrections help to further train the model.

Translating code and application modernization

Generative AI tools can translate code from one programming language to another, saving developers time and reducing the risk of manual errors. This is helpful when modernizing applications, for example, translating COBOL to Java.

AI-powered code generation can also help automate the repetitive coding involved when migrating traditional infrastructure or software to the cloud.

Testing

Developers can prompt generative AI tools to build and perform tests on existing pieces of code. AI tools can create tests that cover more scenarios more quickly than human developers. AI-powered monitoring tools can also provide a real-time understanding of software performance and predict future errors.

Also, through their ability to analyze large datasets, AI tools can uncover patterns and anomalies in data which can be used to find potential issues. When AI tools uncover issues, whether through testing or monitoring, they can automate the remediation of errors and bugs. AI helps developers proactively address issues with code and performance and maintain the smooth operation of software.

Deployment

Generative AI helps DevOps teams optimize the continuous integration/continuous delivery pipeline (CI/CD). The CI/CD pipeline enables frequent merges of code changes into a central repository and accelerates the delivery of regular code updates. CI/CD helps development teams continuously perform quality assurance and maintain code quality and AI is used to improve all aspects of this process.

Developers can use AI tools to help manage changes in code made throughout the software development lifecycle and make sure that those changes are implemented correctly. AI tools can be used to continue monitoring software performance after deployment and suggest areas for code improvement. In addition, AI tools help developers deploy new features by seamlessly integrating new code into production environments without disrupting service. They can also automatically update documentation after changes have been made to software.