Intelligent Analytics with AI Testing

In the time where agile development is on the forefront, maintaining high quality can be a monstrous challenge, like battling the mythical Hydra. This creature with many heads grows two new ones for every one you chop off. Similarly, with an iterative approach, small quality issues can quickly multiply if left unchecked. Time constraints may also force compromises, allowing minuscule defects to sneak into production. As a result, the quality of your product or service can fluctuate over time.

Imagine a graph where quality dips to a critical point, represented by one of Hydra’s vicious heads. This triggers a concentrated effort, slaying that head and achieving a significant improvement in quality like chopping off one of Hydra’s heads. Technical debt, like the Hydra’s multiplying heads, accumulates, leading to another crisis point. This cycle repeats until a permanent solution is found.

As the product grows in complexity, adding more features, the quality swings become more drastic. Eventually, you reach a tipping point where the only solution is to abandon the current version and create a new one entirely. This is an opportunity to reflect and identify any shortcomings in your QA policies and processes.

Ideally, you should have well-defined strategies for testing and analyzing results to control these quality fluctuations. Here are three defect-centric metrics that act as canaries in the coal mine:

• Defects Discovered in the Field: These are the defects found by users, and are the most expensive to fix. They’re like Hydra’s venomous bites — a sign of deep-rooted issues. When you see a surge of severe defects, it’s a wake-up call that your QA needs a major overhaul. This often leads to a reactive cycle of firefighting, lurching from intense focus on quality to laxity once the crisis subsides. A solid foundation of quality goals is essential for effective QA.
• Defects Found During System Testing: This metric is an excellent early warning system for lurking quality problems. It requires consistent and thorough testing of every new feature or change. An increase in high-priority defects indicates a Hydra-like problem that needs to be nipped in the bud before it spirals out of control. These bugs might point to underlying design issues that need to be addressed.
• Defect Open/Close Report: This is another valuable metric that tracks defects identified versus defects fixed. Imagine a graph with two lines: red for open defects and green for fixed defects. In the first scenario, the number of defects discovered increases but they are being addressed. Ideally, the green line would overtake the red line, but in reality there will always be some open defects at deployment. The key metric is the gap between the lines — is it shrinking or widening? A widening gap signifies a multiplying Hydra of unaddressed defects.

The basis of these defect metrics assumes comprehensive software testing is being performed, including functional testing, regression testing, and performance testing. Regression testing, in particular, is crucial for maintaining software health. Without it, you’re flying blind in terms of quality.

Taming the Hydra of Software Defects

Metrics like open/close defect reports can effectively predict the software quality of your product. Defect discovery trends over time are a good indicator, while bugs found in the field are a lagging indicator, a sign of problems that already exist. However, for these metrics to work, comprehensive software testing and analysis are essential.

Webomates offers solutions to help you slay the Hydra of software defects. Our tools and services can help you:

• Control the Frequency and Scope of Testing: Catch defects earlier by strategically targeting tests based on changes made. Webomates CQ uses a shift-left approach and continuous testing methodologies to automate testing efficiently, providing results within 15 minutes to 1 hour.
• Optimize Your Test Suite: Our AI and machine learning algorithms dynamically adjust the testing scope based on changes, identifying the most relevant tests to run. This saves time and resources.
• Identify Root Causes of Defects: The AI Test Package Analyzer helps pinpoint the origin of defects, allowing you to take corrective action and prevent similar issues in the future.
• Predict Defects Earlier: Webomates’ AI defect predictor can identify potential problems early in the development cycle, saving countless hours in debugging.
• Gain Actionable Insights: Our Intelligent Analytics platform provides valuable insights to improve your testing process. It creates a continuous feedback loop, connecting defects back to requirements.

Don’t let software defects multiply like a Hydra. Contact Webomates today to learn more and schedule a demo. You can also reach out to us at info@webomates.com