Software Development Automation

Automation at this SDLC stage is very complex and challenging due to the subjective nature of requirements and the need for human input and interpretation. Still, it's possible to use tools to automate software requirements collection, analysis, and documentation.

Some of the possible tools include:

• Requirements management tools (Atlassian Jira, Trello, Asana, etc.): these tools provide a centralized repository for all requirements and allow stakeholders to collaborate on requirements in real time.
• Natural language processing tools: NLP tools can help extract requirements from natural language documents, such as user stories, emails, and chat logs. These tools use machine learning algorithms to identify key phrases and concepts in the text and convert them into structured data. However, today, we don't use these tools at ScienceSoft and don't see the NLP's wide adoption for this purpose.

Automation tools can help create and validate software design documents, ensure that they meet the necessary standards and requirements. Some of the automation opportunities at this stage include the use of:

• Design patterns (MVC, MVVM, Observer, etc.) that provide proven solutions to common architectural problems and can improve software quality and maintainability.
• Modeling tools (UML, ArchiMate, SysML, etc.) that help automate the process of creating software architecture diagrams. These tools provide a standardized notation for representing software architecture and allow stakeholders to collaborate on architecture design in real time.
• Architecture analysis tools (SonarQube, CAST, etc.) that help automate the process of analyzing software architecture for quality and compliance. These tools can identify potential issues in the architecture and provide recommendations for improvement.

The tools that help automate the process of writing code include:

• Integrated development environments (Eclipse, Visual Studio, NetBeans, etc.) provide such useful features as auto-completion, syntax highlighting, and debugging. These tools can reduce the time and effort required for coding and ensure that the code is consistent with the architecture.
• Low-code platforms (Microsoft Power Apps, OutSystems, Mendix, etc.) provide a visual, drag-and-drop interface for building applications. This allows developers to quickly create and modify application components without writing extensive lines of code. Low-code platforms also typically include pre-built templates and integrations, reducing the need for developers to start from scratch.
• Unit testing tools (JUnit, NUnit, PyUnit, TestNG, etc.) allow developers to write test cases that check the functionality of specific parts of their code and then run those tests automatically.
• Version control tools provide a centralized repository for source code, documentation, and other project assets. Version control tools automate code branching and merging, automatically track every code change, and perform code reviews.
• Code generation tools (Yeoman, Codesmith, MyGeneration, etc.) automate code generation from templates or models. There are also AI-based tools (like OpenAI) that analyze large amounts of data and learn from patterns to generate optimized code for specific tasks or applications. At ScienceSoft, we don't use these tools as we see they are yet in their infancy.

QA automation involves writing and running code-based test scripts to simulate user and software interactions. ScienceSoft's team usually automates regression and integration tests, cross-browser testing, performance testing, and security testing. For this, we use such tools as Selenium, Protractor, Appium, REST Assured, RestSharp frameworks and Apache JMeter.

Automation tools can assist in deploying software parts to various environments, such as development, testing, staging, and production.

At this stage, the most popular practices and tools include:

• Continuous integration and continuous deployment (CI/CD) tools (Jenkins, GitLab CI/CD, CircleCI, etc.) to automate the process of building, testing, and deploying applications. At ScienceSoft, we generally need 3–5 weeks to develop an efficient CI/CD process for a mid-sized software development project with several microservices, an API layer and a front-end part. The most sophisticated CI/CD process helps integrate, test and deploy new software functionality within 2–3 hours.
• Containerization tools (e.g., Docker and Kubernetes) can reduce the time and effort required to deploy an app in containers and ensure the app is always running in a consistent environment.
• Infrastructure-as-code (IaC) tools (e.g., Terraform and AWS CloudFormation) can reduce the time and effort to deploy the infrastructure resources the app requires and ensure that the infrastructure is always configured correctly.

Automation can help monitor how the app behaves after the deployment and identify issues before they become problems.

At this stage, the most popular automation practices include the use of:

• Monitoring tools (Nagios, Zabbix, Datadog, etc.) that automatically monitor network services, hosts, and devices, and alert administrators when performance issues or risks of failure arise.
• Log analysis tools (Logstash, Splunk, Graylog, Logmatic, etc.) that automatically analyze logs for issues and potential failures.
• Configuration management tools (Ansible, Chef, Puppet, etc.) that help automate the management of app configurations.
• Patch management tools (WSUS, SCCM, etc.) that help automate the process of applying patches and updates to software.