This is the second post in a four-part series from the eGuide: Embracing AI First Software Quality Platforms: Transforming the Future of Software Testing
Download the full eGuide here.
Introduction
The landscape of software quality assurance is undergoing a seismic shift with the advent of AI First Software Quality Platforms. Just as predictive maintenance has revolutionized auto repairs by identifying potential issues before they become problems, AI is poised to transform software testing by enabling proactive defect remediation. However, achieving this transformation hinges on trust—trust in the AI systems and trust in the process of change. As organizations navigate this new terrain, they must adopt an iterative, agile approach to fully leverage AI’s capabilities while minimizing risk. This article explores the necessary steps and strategies to successfully integrate AI into end-to-end functional testing.
Map Your Evolution Based on Trust
The role of a tester might not change, but the scope of responsibilities will be dramatically impacted by AI. A good analogy for this is auto repairs. In the not-so-distant past, the primary cause for an auto repair was an actual breakdown. AI and the availability of sensors have introduced predictive maintenance. Predictive analysis can highlight potential failures, offering drivers proactive maintenance and a reduction in unscheduled breakdowns.
The idea of proactive defect remediation in software is powerful. However, convincing developers to proactively fix a defect is roughly the same task as believing the mechanic that you should proactively fix your transmission. It comes down to trust and the ability to back up recommendations with solid evidence.
An AI First testing platform opens the opportunity for transformative change. The most difficult barrier to realizing the benefits of this transformation is that the ultimate end-state of using an AI-Native testing platform is radically different from what most organizations are doing today.
A pattern for iterative change must be adopted which allows the organization to evolve without exposing the business to undue risk. In short, organizations need to apply an agile iterative model to the process of adopting AI.
What is Required to Leverage AI in Testing
Effectively leveraging AI and generative AI in end-to-end functional testing requires a multifaceted approach. This includes developing an application map to visualize its states, actions, and flows, specifying the expected outcomes at each step, choosing diverse test data, writing clear test cases, and tuning AI models to the application. Furthermore, employing a code generator trained on the UX library will automate test script creation, reducing manual effort. Together, these elements create a robust testing framework that ensures applications function as intended.
To leverage AI and generative AI in end-to-end functional testing requires the following:
- Application Map (web/mobile) of All States, Actions, Flows: An application map is a visual representation of the various states, actions, and flows within an application. It helps to understand how the application behaves in different scenarios and can be used to identify potential testing areas. In the case of web or mobile applications, this would include different screens or pages, user interactions, and transitions.
- Expected Outcomes at Each Step or Test: This refers to the anticipated results or behavior of the application when specific actions or inputs are provided. For example, if a user clicks a “Submit” button on a web form, the expected outcome might be that the form is submitted successfully and a confirmation message is displayed. Defining these expected outcomes helps in evaluating whether the application is functioning correctly.
- Test Data to Achieve Expected Outcomes: This data should be carefully selected to cover various scenarios and edge cases, ensuring that the application behaves as expected in different situations. For instance, when testing a web form, test data could include valid inputs, invalid inputs, and boundary inputs.
- Test Cases Written in English (or Other Native Language): Writing test cases in English or another native language ensures that they are easily understandable and accessible to a wider audience, including non-technical stakeholders.
- AI Model Tuned to the Application Under Test: AI models can be used to automate certain aspects of testing, such as generating test cases or predicting potential issues. To leverage AI in end-to-end functional testing, the AI model must be trained on data specific to the application being tested. This ensures that the model understands the application’s behavior and can provide accurate predictions.
- Code Generator Trained on the Required UX Library: In the context of end-to-end functional testing, a code generator trained on the required user experience (UX) library can automatically create test scripts or test cases based on the application’s UI components. This helps in reducing manual effort and ensuring consistency in testing.
Conclusion
As we stand at the cusp of a new era in software testing, embracing AI First Software Quality Platforms offers unprecedented opportunities for improvement. However, the path to success is not solely paved with advanced technology; it requires building trust, adopting iterative changes, and realigning processes to focus on business risk. By systematically implementing AI-driven testing strategies and fostering a culture of proactive defect remediation, organizations can not only enhance the efficiency and effectiveness of their testing processes but also ensure their software aligns with and supports overarching business goals. The future of software testing is here, and it is defined by the strategic and thoughtful integration of AI.
Second post of a four-part series. Download the full eGuide here.
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