7 SDLC Steps Powered by AI Tools in 2025: Reduced Costs!

A Sneak Peek Into the Past

Software development used to be a labor-intensive process, requiring significant human effort and time to bring a product to life. The complexity of building custom software often meant that developers had to master various technologies, frameworks, and systems, which naturally increased development time and costs. The effort required for tasks such as coding, testing, and deployment could lead to lengthy timelines, adding pressure on budgets and resources.

However, the advent of AI has revolutionized this process, enabling a faster, more efficient approach to software development. AI has made the workforce more versatile by automating routine tasks and providing tools that allow developers to specialize in fields beyond their core expertise. By integrating AI tools like machine learning models, code generators, and automated testing platforms, developers are now able to focus on innovation and higher-level problem-solving, rather than getting bogged down in repetitive tasks.

Today, non-technical individuals can leverage AI-powered tools to develop software and even take a basic-mid level software product to completion with minimal coding knowledge. AI has democratized software development, allowing people from various backgrounds to bring their ideas to life with ease. This shift has significantly increased the supply of available developers, as even those without technical backgrounds can now create functional applications.

For example, ten years ago, developing a basic-mid level software product could easily cost around $200,000 or more, depending on its complexity and scope. Today, with the help of AI-driven tools, the same product could be developed for a fraction of the cost—roughly $50,000—making it more accessible to small businesses and startups. The reduced costs and increased efficiency not only enhance accessibility but also foster a more innovative and dynamic software development landscape.

In this article you’ll learn how AI streamlining every step of the software development lifecycle.

1. Requirement Analysis

• Natural Language Processing (NLP) tools like spaCy and GPT-based models can analyze and extract insights from user requirements and documents, helping to understand business needs and automate the gathering of requirements.
• AI-powered survey tools such as Typeform or SurveyMonkey with AI for gathering user feedback.
• AI-powered Analytics: Tools like Google Analytics or Mixpanel can analyze user behavior to better define the software's functional requirements based on user interactions.

2. System Design

• AI-driven architecture design tools such as Archistar help automate the design of scalable and efficient software architectures by analyzing data from previous successful projects.
• AI in UX/UI design: Tools like Uizard and Figma's AI-assisted features can help automate aspects of UI design by predicting optimal layouts, color schemes, and other design elements based on user data.
• AI-based diagramming tools like Lucidchart with AI can assist in visualizing system architectures and workflows automatically based on predefined inputs.

3. Coding (Development)

• AI-powered code completion: GitHub Copilot, Tabnine, and Kite provide real-time code suggestions and auto-completions to help developers write code faster and more efficiently.
• AI-driven code generators: OpenAI Codex and DeepCode can help generate code snippets or entire functions based on a description of the functionality.
• Machine learning libraries: TensorFlow, PyTorch, and Scikit-learn can be used to develop machine learning models as part of the application itself.

4. Testing

• AI-based testing tools like Testim.io and Applitools use AI to automate the creation of test cases, execution, and visual validation, reducing manual testing efforts.
• Smart bug detection: Tools like DeepCode or Snyk use AI to identify potential security vulnerabilities and bugs in the codebase.
• AI in performance testing: Neudesic AI Testing can analyze performance metrics to predict and optimize software performance issues using AI.

5. Deployment and Implementation

• AI for continuous integration/continuous deployment (CI/CD): CircleCI and Jenkins with AI integrations optimize the deployment pipeline by predicting issues and automating deployment tasks.
• AI-based infrastructure management: IBM Watson AIOps and Azure AI use AI to monitor infrastructure, predict failures, and automate responses to system anomalies during deployment.
• Cloud AI deployment: Google Cloud AI, AWS AI, and Azure AI offer pre-built solutions that simplify the deployment of AI-powered applications into production environments.

6. Maintenance and Updates

• AI-driven monitoring tools like Dynatrace and New Relic use machine learning to automatically detect performance issues, bottlenecks, and potential failures, aiding in maintenance efforts.
• Predictive maintenance: AI tools like Uptake or Zebra Medical Vision can be used for predictive maintenance by analyzing logs, user feedback, and system data to predict future failures or needs for updates.
• AI-assisted bug fixing: Tools like IntelliJ IDEA with AI features can suggest or even automate bug fixes during maintenance.

7. Documentation

• Automated documentation tools like GitBook and MkDocs can use AI to auto-generate documentation from code comments and documentation templates.
• AI-driven content generation: OpenAI’s GPT-3 can assist in writing technical documentation, FAQs, and user guides based on the software's functionality and user feedback.
• Knowledge management platforms: Confluence with AI can help organize and recommend relevant documentation based on the team’s needs.

8. Final Evaluation and Feedback

• Sentiment analysis tools like MonkeyLearn or Lexalytics can analyze user feedback and reviews to identify areas for improvement and detect overall sentiment toward the software.
• AI-powered analytics platforms like Power BI with AI or Tableau can analyze the software’s usage and performance metrics, helping developers make data-driven decisions about future updates or modifications.
• A/B Testing with AI: Tools like Optimizely or Google Optimize can automate A/B testing processes, using AI to analyze which features or changes lead to better user engagement or performance.