What kind of QA person are you? Reactive or Proactive? Let’s take a quiz to find out; please answer YES/NO to each of the questions below –
- Do you start QA cycle only when the code is available in QA?
- Do you review your code after every stage of the SDLC?
- Do you log bugs in every stage before moving the code to the next stage?
- Do you create the QA environment as conveyed by the Developers/ Product management?
- Do you only update your regression suite when a high priority bug is caught is production?
If you have answered at least three YES to the above questions, you follow REACTIVE strategy model i.e. working after a stage is complete to identify issues/defects. However, if you answered NO to most of the questions above, you follow a PROACTIVE QA strategy where you act before/in parallel to a stage of SDLC.
Nevertheless, to meet the current challenges of the software development, achieve the quality standards you need to take a step further and prep your model to be predictive enough. How to do that? CloudQA will help you with that –
What is Predictive Analysis in QA?
Just take a real-life scenario, if on every Friday you visit a website for movie reviews and then accordingly book a ticket which has a good review. If such data is read and the algorithms identify a pattern, next time as soon as you land on the movie review website, you could have an option to compare and book the ticket in one single go.
Based on user’s behavior a prediction is made, and an action is suggested. This is predictive analysis.
How often have you heard QA/Dev team saying – the issue is not reproducible in our environment? Predictive Analytics would help in bridging the gap of user’s environment and QA environment.
Applying the concept into QA world is simple. The traditional model to go through the high-level requirement prepared by Business Analysts, churn out test cases and develop automated test cases and release is hashed out. The Predictive QA model follows the reverse pattern; they work on the test cases that are designed from the customer’s perspective. QA tests these cases on customer-focused QA environment; the real-life scenarios are mimicked to gain more visibility. In fact, QA is now plugged into the real-time environment analyzing and scrutinizing data, structuring it, identifying patterns and real-time logging defects.
Just for example a banking website, if the data is collected and an algorithm is reading and trying to identify some pattern it could help them in detecting Fraud or theft or unauthorized access that was not detected by firewalls/anti-virus. For in that matter a health software that is based on patient’s symptoms, history, and current condition provides the diagnosis needed for the patient.
How is Predictive Analytics Advantageous to QA?
Predictive analytics help firms in following ways
Formulate strategy in which customer is the king
When predictive analytics provides facts with the support of data, that could be used to offer services/products that are more customer focussed. Just for example if a QA person can find a pattern on why the Mobile App if getting uninstalled within five minutes of its installation, it would help the firm in redesigning their product strategy which would be more customer focussed.
Understands Customers and their emotions
A QA person could help in identifying the high rated functionalities and low rated functionalities with the help of predictive analytics. The same could be conveyed to development and support team, and it could contribute to expanding the high rated functionality with more exclusive features.
Prioritize Your Testing
Gathering, structuring the data could help QA team in the scheduling of testing in prod environment. While the usual norm is to schedule it off-hours, the data could point to an exact time when testing could start, with prioritizing the high priority test cases and stopping it when users land in.
Enhance Test efficiency
When comparing test efficiency based on product management inputs and real-time user inputs, the former would surely win. As QA team is assuring what customer needs is what is served to them. Just for example analyzing build system data could reveal the size of the build, the time and dependent variables, that could help in reducing the dependency and making the build more stable. Or the QA team could also review the old test execution logs and analyze the reasons for over-runs.
Delightful customer experience
How often have you raised a request to Google to resolve an issue or LinkedIn for a faulty error message? Were you happy when they responded the issue is resolved? Well, I was and to know someone is listening to your issues is a delightful experience for the customer. And with Predictive Analytics it is indeed a possibility.
Saves Time and Money
QA is all about saving money and time! With increased efficiency, quick defect detection, knowing your customer we at one place our enhancing our Time-to-market and saving money by reducing cost. Just for example of analyzing the past production defects, one could build a relationship with how and what kind of bugs get introduced? Are they because of new technology or new functionality? Analytics could also help in providing an insight to project release schedule, were they on time or was there a lag? And what were the probable reasons for the delay? And then building a strategy to avoid them.
What do you think about Predictive Analytics? Have you used it? Do let us know in our comments section.
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