In a conventional software development project, testing happened right before the software was released into production. This meant that if defects or usability concerns were discovered, the launch would be postponed until those issues were resolved.
In this approach, testing became a significant bottleneck, making it difficult for projects to be completed by the set deadlines. Because development and quality assurance (QA) were different entities, projects could not adapt to shifting requirements and expectations, resulting in unfavorable business results.
One way to avoid this is by implementing shift-left testing. If you want to improve your software development process, keep reading! In this article, you'll learn about the fundamentals of shift left testing: shift left’s meaning, what a shift left strategy is, and how to use it in your own company.
What Is the shift-left Approach?
Shift Left is a movement that advocates changing the way we handle software quality improvement. In the past, we relied on a waterfall approach in which developers and quality assurance specialists had separate roles and responsibilities.
Shifting left refers to bringing the testing stage of the software development life cycle (SDLC) earlier into the process. By doing so, teams become more collaborative and share information as quickly and frequently as possible.
What Does It Mean To shift-left in QA Testing?
Shifting left in testing and QA refers to the earlier integration of testing operations with information gathering, design, and development requirements. Shift left takes care of issues when they arise and ensures fewer flaws later in the SDLC.
Why Adopt a Shift-Left Testing Strategy?
When testing is scheduled at the end of the development process, any issues that do appear are usually more challenging to resolve. Because all the code has been written, the only way to fix the problem is to overhaul the software and rewrite the code completely. As a result, costs rise, and the time to market lengthens significantly.
The sooner bugs are fixed, the better. Shift-left testing involves a process that allows developers to find bugs quickly and frequently. The easiest bugs to correct are those found when the developers write or review code, since these code blocks are small and easy to manage.
After being combined with the primary development branch, the scope of the code increases, as does the amount of time it takes to hunt for errors. In this approach, quality assurance engineers will act more like specialists, providing their expertise and guidance from the start of the SDLC to completion.
Benefits of a Shift-Left Testing Strategy
By implementing the left-shift testing approach, organizations can place quality as a top priority from the outset of a project and work to uncover flaws as early as possible in the life cycle. The following are some of the significant advantages of shifting left.
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Improves Efficiency and Quality
It is essential for delivering high-quality software quickly. To reduce rework and disruptions, developers move testing to the beginning of the process, or more rightly put, to the left to minimize extended test cycles, avoid flaws, and manage risks early in the SDLC rather than later.
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Reduces Development and Testing Costs
If each build is tested, issues become easier to identify and may be fixed to lower costs. Doing quality assurance as we move along the process also means avoiding high costs and extra work of going back and redoing everything.
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Gain a Competitive Advantage
Shifting left helps you gain an edge over your competition, reducing the time to market. Using incremental innovation, development teams can move quickly, establish, and maintain their credibility. The challenge of faster progress without losing quality is addressed by shifting left.
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Automation
Shifting left allows you to automate testing more effectively. In addition to minimizing human error, test automation also increases coverage, reduces production problems, and frees up testers' time to work on more exciting and satisfying projects.
How To Plan a Shift-Left Testing Strategy
Here are some of the things you can do to shift your software testing to the left:
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Demand Planning
Demand planning is an essential aspect of the shift left strategy since it serves as a springboard for test lifecycle tasks. By collaborating with management and operational stakeholders, testers can obtain a better understanding of future demand. You can plan and confirm the budget, resource allocation, and testing strategies beforehand with this insight.
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Static Testing
Static testing is done in the early stages of a project and comprises requirements and design validation. Using static testing, you can uncover problems early in the project's life cycle before they become too costly to fix.
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Unified Testing Strategy
With a unified test strategy, you can evaluate constraints on automation, stubs, environments, and test data, guaranteeing that the respective teams can meet the requirements. In general, this is a high-level approach for end-to-end testing, from unit tests to user acceptance tests (UAT) to operational readiness tests (ORT) and post-deployment tests (PDT). This strategy will cover all QA responsibilities and steps.
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Risk-Based Analysis
Using risk-based analysis, determines the consequences and probability of failure for every test case. Functional, non-functional, and regression testing can be done using this method.
Is the Shift Left Testing Strategy Always Appropriate?
In a real-life setting, a shift-left testing strategy may not always be successful in delivering optimal results or improving functionality. In such cases, a shift-right testing approach might help improve the customer experience by allowing for test automation and ensuring improved test coverage.
With the shift-right approach, you will test a fully developed and functional application to check its performance and usability characteristics. Reviews and input from specific users also aid in improving the software's quality.
How Can Organizations Implement the Strategy?
It can be implemented in your company by following these steps.
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Decide on Coding Standards
An excellent place to start is by establishing code standards for your development team. All developers must have the same understanding of the project. It allows them to check code more quickly and ensures that the code is of greater quality. Since these coding standards prevent incorrect or unsafe code, they should reduce the frequency of errors.
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Implement Testing Early in the SDLC Process
Figure out which tools can be helpful for your codebase as a group. Aside from that, the group should consider how testing will be included in the SDLC early on. One strategy is adopting the agile method, which works with tiny code increments known as sprints.
However, many organizations are unable to make the transition to an agile approach. As a result, the project team may agree on building unit tests for every new feature. This gives them confidence when developing business logic.
At a later stage, integration tests should be written for these different bits of code to ensure that they work together seamlessly.
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Embrace Test Automation
Since it requires regular testing, the project team should adopt test automation tools. Conducting tests for every code increment should also be automated, in addition to releasing new builds. This will relieve the load on QA responsibilities and bring faster feedback on the code's reliability.
Changes Observed After Implementing This Strategy
Here are a few things to expect if your company decides to use shift-left testing.
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Greater Customer Involvement
According to many agile methods, involving the customer in the software development process is critical to its success. Shifting left while involving customers in your testing process is advantageous for you.
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Less Time Required for Testing
In traditional systems, testers must wait until developers have completed establishing software features before testing. By shifting left, testing is prioritized earlier in the development process as well as more frequently. As a result, there will be less time spent waiting for testers.
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Adherence to the Testing Pyramid
When deciding which automated software tests to undertake, teams and organizations can use the test automation pyramid. According to the pyramid, unit tests are advised since they are easy to execute, cost less to set up, and require less effort on the developer's part.
If your company uses shift-left testing, then developers will almost certainly create unit tests before or after generating production code. As a result, you will also be following the recommendations of the testing pyramid.
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Increases Coverage
It has the added benefit of increasing test coverage. If you have more people creating tests more regularly and starting earlier, you'll have a more significant percentage of your software evaluated by tests.
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Involves a More Diverse Team for Running Performance Tests
It's becoming more normal for everyone in a software company to carry out testing these days. This is not only beneficial but also necessary for genuine shift-left testing. It's impossible to make testing a comprehensive, ongoing activity without enlisting all available resources. Fortunately, there are tools available today that allow even non-programmers to create extensive tests in previously unimaginable ways.
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Prevents Unnecessary Maintenance Tests
One of the most challenging aspects of test automation is tedious test maintenance. When your test suite is full of flimsy tests, even the tiniest modification to the codebase can cause a test to fail. As a result, managing the tests becomes a big chore, jeopardizing the entire test automation effort. When you use shift-left testing, you must pay special attention to test maintenance issues since you will have more tests of various kinds.
Advancing Shift-Left Test Automation with Generative AI: A Technical Exploration
The quest for efficiency and precision in the testing phase remains an ever-present challenge, and generative AI is revolutionizing the concept of Shift-Left testing.
Shift-left testing is a software development strategy that integrates early testing into the software development lifecycle. It focuses on detecting and addressing bugs and issues as early as possible to reduce costs and improve software quality. Integrating generative AI technologies into shift-left testing can enhance its precision and efficiency.
How Shift-Left Test Automation with Generative AI Works
Consider a complex SaaS-based CRM application with various modules — ranging from contact management to sales forecasting. Ensuring comprehensive test coverage in this complex ecosystem is extremely time-intensive. Creating test cases to anticipate the multitude of user scenarios and potential edge cases is also inherently prone to human error.
The advent of Generative AI has streamlined the shift-left process, offering speed, efficiency, and scale throughout numerous use cases.
Automated Test Case Generation
Using transformer-based architectures like GPT-4, AI models can process the CRM's functional specifications and autonomously generate test cases.
For instance, a human tester might rely on explicitly stated requirements to script various scenarios in a traditional manual testing scenario. Generative AI, in contrast, can interpret functional specifications and autonomously craft test cases, significantly reducing time and effort.
If a human is testing the 'create new contact' feature, this human tester might focus on typical scenarios involving valid or invalid data inputs. The AI goes several steps further:
- Character Set Handling: The AI can be directed to populate contact form fields with Unicode characters to test how the application handles different character sets, which is critical for internationalization.
- Security Vulnerabilities: It can be instructed to submit oversized data payloads to probe the system for potential buffer overflow vulnerabilities. This ensures that the application remains resilient against security threats.
- Concurrency and Performance Testing: The AI can rapidly and repeatedly submit data, assessing the application's response under heavy loads. This not only helps detect race conditions but also evaluates the application's resilience against Distributed Denial of Service (DDoS) attacks.
The impact of these AI-generated tests is transformative. They not only expedite development cycles — but also enhance overall product reliability.
Predictive Bug Discovery
In the CRM’s complex web of data, the potential for lurking bugs or inefficiencies poses a significant threat to the application's performance and user experience. The predictive prowess of generative AI transcends conventional bug detection methodologies, enabling the CRM's development team to proactively identify and address potential pitfalls before they culminate into significant issues.
Consider a scenario where the visualization module relies on multiple nested loops navigating through extensive data arrays. While human testers might neglect these loops, the AI, through its intricate analysis, can predict potential performance bottlenecks that could hamper the application's efficiency.
Plus, the AI can autonomously generate and execute test scenarios to further validate these predictions. One such scenario might involve simulating a substantial load on the system, mimicking thousands of concurrent users accessing the visualization feature simultaneously. This stress test not only validates the AI's predictions but also reveals how the suspected bottlenecks might impact the application's responsiveness. The technology unveils whether the bottlenecks result in noticeable lags — or, worse, system crashes — under heavy usage.
Real-Time Feedback Loops with CI/CD Integration
The fusion of generative AI with Continuous Integration and Continuous Deployment (CI/CD) pipelines introduces a dynamic mechanism for real-time feedback during the software development process.
Imagine the task of integrating the CRM with a marketing automation tool. AI works seamlessly within the CI/CD workflow and swiftly adapts to the changes, generating pertinent test scenarios based on the freshly committed code.
If a developer introduces a code segment that fails to validate an external API's payload before processing, the AI instantly springs into action. It crafts a specific test scenario, simulating malicious payloads to probe the system's security. This instantaneous feedback loop acts as an early warning system, swiftly flagging potential vulnerabilities long before the code progresses to later stages of development.
Infrastructure Implications
Implementing AI within the development infrastructure requires robust computational capabilities. High-performance GPUs or TPUs are indispensable for ensuring the efficient operation of advanced models like GPT-4.
Additionally, existing testing frameworks and CI/CD pipelines may require middleware integrations or API hooks to seamlessly communicate with and leverage the capabilities of these AI models.
Benefits of Using Generative AI for Shift-Left Testing
AI-powered shift-left testing offers a range of benefits that enhance the software development process, improve product quality, and streamline the project life cycle. These benefits are often driven by the emphasis on early testing, collaboration, automation, and a proactive approach to quality assurance.
- Improved Software Quality: Early testing helps identify and address defects and issues earlier, producing a more reliable and robust product.
- Accelerated Development Cycles: AI speeds up the development process by identifying and rectifying defects early, enabling a quicker software release and faster time-to-market. Organizations can also deliver updates and new features to customers more frequently.
- Early Bug Identification and Risk Management: Early detection reduces the effort required to fix bugs, compared to identifying issues later in the development process or production — and helps prevent major issues from occurring later in the project.
- Optimized Resource Utilization: Resources, including testing teams and infrastructure, are better utilized as they are integrated into the development process, preventing bottlenecks and optimizing resource allocation. Plus, identifying and addressing issues early in the development cycle is more cost-effective than doing so in later stages.
- Data-Driven Decision-Making: Organizations can make more informed decisions by analyzing metrics and data collected from early testing efforts. This data-driven approach supports continuous improvement.
Moving Forward with AI in Shift-Left Testing
The impact of generative AI within shift-left testing is profound. Through automated test case generation, predictive bug discovery, and real-time feedback loops within CI/CD pipelines, this technology not only expedites development cycles — but significantly elevates the overall quality and reliability of software products. Its ability to simulate diverse scenarios, assess security vulnerabilities, and predict performance bottlenecks redefines the proactive approach to quality assurance.
As we embrace the transformative potential of AI in the context of shift-left testing, it's vital to recognize the delicate balance between its advanced capabilities and the need for vigilant oversight.
Biases or limitations within the training data could impair the integrity of the testing outcomes. Embracing generative AI not only fosters innovation — but also fosters a culture of continuous improvement and adaptability.
Conclusion
In summary, the synergy of shift-left testing and generative AI offers a powerful avenue for achieving heightened efficiency and software quality. Every team member's shared responsibility in the shift-left approach is pivotal.
QASource is poised to support organizations with a comprehensive range of quality assurance testing services, covering automation, automation testing services, manual testing, mobile QA, API testing, and more.
Generative AI's impact is profound, automating test case generation, predicting bugs, and enhancing CI/CD pipelines. Yet, careful oversight is crucial to mitigate biases and ensure testing integrity. Embracing generative AI not only fuels innovation but fosters a culture of continuous improvement, driving excellence in software development.
To learn more about how we can help solve your testing challenges, contact us today!!
