Unpacking Shift-Left Testing Benefits: Key to Reducing Costs and Boosting Collaboration

Software testing typically starts once the code is fully developed or in the later stages of development. With this approach, when QA’s role begins at that time, most of the development is already done, and identifying defects at this timeframe means rework for the developers, which is quite costly in terms of time and money. It also impacts project production delivery.

Author: Gaurav Mittal

With the introduction of Shift Left Testing, there is a significant shift in incorporating testing activities earlier in the development life cycle, starting from the design phase. This initiative-taking strategy enables quicker defect detection. By identifying issues early on, Shift Left Testing notably reduces the chances of rework, enhances software quality, and improves team collaboration, resulting in more efficient project delivery.

Shift Left Diagram

This diagram illustrates the concept of shift left testing with:

1. Two curves showing testing effort distribution:
   • Blue solid line: Shift left testing approach (earlier testing)
   • Red dashed line: Traditional testing approach
2. A timeline showing the software development lifecycle phases:
   • Requirements
   • Design
   • Development
   • Testing
   • Production
3. The diagram shows how shift left testing:
   • Moves testing activities earlier in the development cycle.
   • Distributes testing effort more evenly.
   • Reduces the testing burden in later stages.
4. A legend to distinguish between the two approaches.

What is Shift-Left Testing?

Shift-left testing involves incorporating testing activities early in the software development lifecycle (SDLC). Instead of waiting for the development phase to be completed, testing is carried out in the initial design and coding stages. This early involvement enables quicker feedback from QA, allowing developers to address issues before they deploy code and invest time in rework.

Critical Benefits of Shift-Left Testing

1. Early Detection of Defects: By identifying defects earlier, we can cut the cost of defects as it’s faster to fix them. With the current traditional approach, defects are discovered late in the process and require rework, leading to delayed product releases. Shift-Left Testing catches defects during the design or coding phases and thus helps prevent these defects from being turned later into high-priority or severe defects.
2. Reduced Costs and Time: Fixing a defect in the requirements or design phase is significantly less expensive than fixing it after development or during production. By identifying issues early, Shift-Left Testing reduces the overall development cost and speeds up the release cycle.
3. Improved Collaboration Between Teams: Shift-Left Testing encourages collaboration between development and testing teams from the project’s kickoff. Testers participate in the design and coding stages, providing initial insights and ensuring robust product delivery.
4. Increased Test Coverage: Early tester involvement enables them to create comprehensive test cases and scenarios from the beginning, ensuring higher application coverage. It also allows them to capture edge cases and scenarios that might have been overlooked if QAs had not been involved upfront.
5. Supports Agile and DevOps Practices: Shift-Left Testing supports Agile and DevOps methodology, where continuous integration and delivery require rapid feedback loops. Automated testing frequently requires code changes, which can now be easily managed by Shift-Left Testing. Recent changes can be evaluated immediately. Overall, it helps reduce the risk of integrating faulty code into production.

How to Implement Shift-Left Testing

1. Incorporate Testing from the Design Phase: Engaging testers during the design phase of development can help examine the requirements and pinpoint potential issues early in the process, ensuring that test cases are coordinated with the functionality. This teamwork helps minimize ambiguity and fosters a mutual understanding of the product.
2. Continuous Integration and Continuous Delivery (CI/CD): Shift-Left Testing thrives in environments with CI/CD pipelines. Each code commits triggers automated tests built using the Shift-Left approach, ensuring the code is functional before being integrated into the main branch. This early and frequent testing helps teams detect and resolve issues faster, leading to more stable releases. This is like reducing the dependency on functional testing for later when the code is fully developed.
3. Use of Early Performance Testing: Performance testing should not be reserved for the end of the development cycle. By incorporating performance testing early on, teams can identify bottlenecks and scalability issues before the system grows too complex. This initiative-taking approach helps avoid costly redesigns and ensures the product can handle expected loads from the start.

How AI Supports Shift Left Testing?

AI-Powered Test Automation: A Game-Changer

AI-powered test automation tools, such as Tosca Vision AI, Testim, and Applitools, generate test locators directly from UI mockups, whiteboard diagrams, or design files (e.g., Sketch, Figma, or Adobe XD). These tools can analyze the mockup, automatically identify key UI components such as buttons, forms, and images, and generate stable locators for these elements.

Critical Benefits of AI-Powered Locator Generation

1. Early Test Automation with UI Mockups: One of the most powerful use cases of AI-powered test automation is the ability to create test scripts from UI mockups before the development team has completed the actual coding of the web application. This approach enables:
   • Parallel Development and Testing: Test scripts can be created and executed as soon as the UI design is finalized, allowing QA teams to start testing before the final product is ready.
   • Faster Time to Market: Since test scripts are prepared earlier, QA teams can provide rapid feedback on the application’s design, reducing the time to market.

1. Test Automation in Continuous Integration/Continuous Delivery (CI/CD): In a CI/CD pipeline, test automation is crucial for continuous testing. AI-powered tools can generate locators on the fly, making it easier to keep up with frequent code changes and UI updates. This is especially helpful in agile development environments where:

1. • Frequent UI Changes: Traditional test automation scripts may break as UI elements frequently change in an agile development environment. AI-generated locators help make test scripts more resilient to these changes.
2. • Reduced Test Maintenance: Since AI tools can adjust locators based on visual recognition, the maintenance burden of updating test scripts when minor UI changes occur is reduced.

3. Cross Platform Testing: AI-powered tools ensure that locators generated from mockups work consistently across various platforms without requiring separate scripts for each browser or device.

How Does It Work?

1. Input UI Mockup: Testers provide a mockup or design file visually representing the web page or application interface.
2. AI-Powered Analysis: The AI engine analyzes the mockup and identifies the visual elements-buttons, text fields, drop-downs, images -using image recognition and machine learning algorithms.
3. Generate Locators: The AI tool automatically generates locators (XPath, CSS selectors) for each web element based on the analysis.
4. Create Automated Test Scripts: Once the locators are generated, the AI tool can create a complete test script or allow testers to integrate the locators into their existing automation frameworks.

Challenges and Limitations of AI-Powered Locator Generation

While AI-powered locator generation offers numerous benefits, there are some challenges to consider:

• Complex UIs: It is challenging to identify web locators needed for Test Automation on dynamic user interfaces for AI tools, and refining locators mostly requires manual intervention.
• Training Data Limitations: The accuracy of AI models depends on the quality and quantity of the training data; the more training data we have, the better the model accuracy.
• False Positives/Negatives: AI tools can use incorrect locators, leading to false positives that require manual intervention to ensure accuracy

Conclusion

Shift-left testing is today’s transformative approach to improving software quality and reducing defect costs. By integrating testing activities earlier in the SDLC, teams can detect and fix defects earlier, collaborate more effectively, and deliver more reliable software to users.

The practice suits modern development methodologies like Agile and DevOps, where continuous testing and rapid feedback are essential. AI-powered test automation tools that generate web element locators from mockups are also handy in the Shift Left testing strategy.

Key Takeaways

• Early Testing Saves Time and Money: Shift-left testing helps find defects early in the development process, thus reducing rework and saving time and money.
• Better Collaboration: Shift Left Testing improves communication and teamwork between developers and testersby involving testers from the beginning.
• Supports Agile and DevOps: Shift-left testing fits well with these practices, ensuring continuous testing and faster feedback loops.
• AI-Powered Test Automation: AI tools can generate test scripts early from UI mockups, allowing parallel testing and development to speed up the release cycle.
• Challenges with AI Tools: While AI-powered tools are helpful, they struggle with complex UIs and require manual efforts to ensure accuracy.

About the Author

Gaurav Mittal is a seasoned IT Manager with 18+ years of leadership experience, adept at guiding teams in developing and deploying innovative technology solutions. Specializing in strategic IT planning, budget management, and project execution, he excels in AWS Cloud, security protocols, and container technologies. Gaurav is skilled in Java, Python, Node.js, and CI/CD pipelines, with a robust background in database management (Aurora, Redshift, DynamoDB). His achievements include substantial cost savings through innovative solutions and enhancing operational efficiency. Gaurav is recognized for his leadership, problem-solving abilities, and commitment to delivering exceptional IT services aligned with organizational goals.