Recent advancements in technology have boosted the feasibility and practical use of AI and ML models. We see this everywhere and software development is no exception. From code generation to understanding large code bases and data sets (e.g. traces, test results), it’s hard to miss how AI and ML are becoming more central in our day-to-day software development activities. The growing pace of development, increasing volume of code, and daunting complexity in code-related insights will only accelerate this transition. What are the unique opportunities in software testing? How can we create AI and ML solutions tailored to such opportunities? Finally, what does the future look like for software testing professionals?
Takeaways from the talk:
- What are the practical ways in which AI and ML are used today in software development and software testing.
- What are unique opportunities in software testing that are more suitable for AI and ML-based solutions?
- What kind of skills and competencies do we anticipate becoming more relevant for software testing professionals?
- What do we expect this transition to look like?