If you are in the IT profession, you know that metrics are extremely important in helping to make decisions. This is also especially true for Quality Engineering teams. 10-15 years ago, testing was primarily conducted by software quality analysts and test cases were executed manually. Most software testing teams were small, and they would run a limited number of test cases to ensure things worked. Using this approach, it was relatively easy to know if there software was ready for production, and that QA manager could pull the team into a room and determine if the software was ready to be deployed. Those times have drastically changed.
Here are a few reasons why software testing has evolved:
- There is a lot of software testing tools that enable status reporting
- Automation and Performance testing tools are widely utilized
- Applications are more complex and tightly integrated using interfaces across multiple technologies
- There is tremendous pressure to deploy products quickly to market
- Testing applications earlier in the lifecycle ( shift left)
- Distributed teams
There is a need based upon this evolution to have software testing metrics in order to make better decisions. This data needs to be consistently captured and analyzed. It is important to create predictive analytics so that you will be able to determine the current state of the quality engineering effort and accurately predict what would happen in production.
Quality is required.
Speed is required.
Resources and time is limited.
Decisions must be made.
Software must be deployed to production.
In order for these things to happen data analytics must be performed. A base set of data is needed. Some of those data elements include:
Planned/Executed test cases
Manual vs Automated tests
Root Cause Analysis
Once this data has been identified, it needs to be captured and segregated. When that information is gathered, you will be able to start and see trends. If you are testing a certain application, you will be able to predict how long it will take to perform testing, how many defects you plan to identify, and most likely how many defects will make it do production. Predictive analytics will evolve over a period of years. Many companies have started using AI/Machine Learning in helping perform this analysis.
This is also a continuous process. It is something that is not done once and completed. Additional metrics and more information will be needed. Those metrics will have to be captured and predictive analytics models will need to be created or modified.
Digital transformation requires that quality engineering teams transform how testing is planned, executed, and measured. The key to digital transformation is a focus on the customer. This requires that the quality engineering teams truly understand the business, and more importantly can accurately predict customer behavior. Issues such as usability, compatibility, performance, and security are extremely crucial. Provided these issues are tested, and the results are acceptable, this will create a really positive customer experience. For example, if a mobile application is slow, the customer is not going to have patience and will quit using it.
Predictive analytics can be used for defects. Here is some helpful information that will improve quality:
- Type of defect
- What phase was the defect identified
- What is the root cause of the issue
- What changes need to be made so that defect will not make it into production
- Is the defect reproducible?
Once this is understood, changes can be made to prevent similar issues from occurring. Using these predictive analytics, overall quality will greatly improve and speed to market will accelerate. It is important to have the right amount of data so that predictive decisions can be made.