This has gained popularity in the last 12 months and is now increasingly known as “shift-deep testing.”
Some traditional techniques such as root cause analysis, the five whys, and Failure Mode Effect Analysis (FMEA) have now been subsumed under this paradigm. At its core, shift deep testing comprises the following key tenets.
1. Accurate and continuous data capture (log, wire, test results, screenshots, etc.) during the software test process.
2. Organizing and structuring captured data for effective analysis.
3. Extracting insights from this data utilizing manual and/or AI/ML-driven algorithms to further optimize, refine, and improve the test life cycle.
The application of shift deep techniques can not only help significantly improve overall software delivery but are a great differentiator in industry use cases such as Predictive maintenance, equipment reliability, and total cost ownership (TCO) optimization.
With that, let us dive deeper into the key tenets of shift deep testing.
