Large-scale, generative AI models have a deep understanding of language and code, opening up the possibilities to do anything from building applications to designing game-changing UI experiences. As generative AI continues to make waves, its promise to revolutionize development and testing will usher in a new era where tests become increasingly autonomous, self-sufficient, and optimized for speed-to-value.
But what benefits will generative AI bring to testing solutions already on the market? To answer this and more, here’s a rundown of how generative AI will change the DevOps landscape.
1. Test Case Generation
Creating test cases using traditional methods is generally limited to the human capacity and understanding of test requirements, resulting in unintended gaps in coverage and defect leaks.
Generative Al will tailor interactions and content by generating diverse test cases based on either recommendations or scenarios, saving time and ensuring comprehensive test coverage.
2. Test Data Generation
Generating synthetic test data with predefined templates and/or algorithms may limit the diversity of context and total volume of data.
Generative AI will improve the breadth and depth of testing with AI models capable of breaking down data silos with high-volume, diverse, and realistic test data.
3. Code Generation for Tests
Developers already have access to AI solutions for code generation today. Google Cloud’s Vertex AI, for example, offers generative AI models that allow for all types of content generation, including code.
Down the line, Generative AI will help developers and testers generate test scripts that tests code using input from specific log files, making work more efficient and productive.
4. Anomaly Detection
Escaped defects have a way of unintentionally ruining releases and reputations.
Generative Al will spot patterns and anomalies more thoroughly and consistently, helping developers and testers find more defects to ensure each release meets their company’s high standards of quality and customer expectations.
5. Risk Prioritization
As of today, AI is capable of monitoring and managing large numbers of problem areas with modern solutions.
To better address high-priority concerns and manage the influx of risks in the future, Generative AI’s enhanced pattern recognition will identify potential risks for prioritization based on severity and impact.
6. Real-time Insights
The lack of visibility and insights hinders the decision-making process—all the way from the code level to the CEO level.
Generative AI will relieve frustration and help companies make better informed, data-driven decisions by gaining high-value insights and rapid results in areas that impact both software testing and quality.
7. Security Vulnerabilities
The emphasis on security will make its way to center stage as the need for governance, the importance of assessing risks, and the shift toward DevSecOps continues.
Generative AI is well poised to help companies not only find formatting issues in their code, but also help mitigate application vulnerabilities and data privacy issues.
8. Test Maintenance
Arguably, the most taxing and frustrating work for testers involves making constant maintenance updates to tests, especially when they’re under pressure to meet time-sensitive demands.
Generative Al models have the potential to reduce maintenance loads by continuously learning and adapting to new requirements, saving untold hours of time and aggravation.
9. Test Execution
The repetitive manual process of creating tests can unknowingly make tests time-consuming and brittle. Broken syntax, random data, external dependencies, and bulky scripts are all culprits that make tests inefficient.
Generative Al models will accelerate the test execution process by quickly generating tests, test cases, and test data, making the test execution flow more efficient.
10. Scalability and Performance
Scaling tests often requires additional intervention by IT, costly resources, and untold constraints on time.
Generative Al will easily handle large volumes of data and manage complex scenarios, making them highly scalable and faster with fewer constraints on resources.
Next-level DevOps with Next-Generation AI
OpenText is pioneering this new era of possibilities where generative AI complements human creativity to become tomorrow’s solutions. We recently launched opentext.ai and are using large language models (LLMs) to predict delivery times, identify risks and gaps, and generate AI-generated test ideas that deliver high-quality software applications at unprecedented velocity and efficiency.
Sign up today to:
- Gain high-value insights instantly into projects and points of risk that impact software quality.
- Empower developers with value generating work that minimizes manual repetitive tasks.
- Release software on-time using state-of-the-art AI for ultra-efficiency.
Discover next-level DevOps using next-generation AI with OpenText DevOps Aviator.