AI/ML for continuous quality
Implementing continuous quality across the development, delivery, and production life cycle involves several activities designed to ensure stable releases and enhance user satisfaction, as illustrated in Figure 8.4.
Figure 8.4 – AI/ML for continuous quality activities
Here is a list of activities essential for this approach, along with potential bottlenecks and how AI/ML can address these challenges:
- Quality metrics integration:
- Description: Embedding quality metrics into every phase of the software development life cycle to monitor and improve quality continuously.
- Bottlenecks: Manual collection and analysis of quality metrics can be time-consuming and prone to errors, potentially slowing down the development process.
- AI/ML application: AI can automate the extraction, monitoring, and analysis of quality metrics from various tools and platforms, providing real-time insights and predictions to prevent quality issues...
