The consumer noticed flaws: No one was at fault, everyone was embarrassed
Exploring the challenges of customer-found defects and strategies to improve software quality.
Introduction
Engineering teams use a methodical and disciplined approach to software development because they are motivated to provide clients with high-quality products as soon as possible. It comprises adopting best practices, increasing automation, and standardizing processes throughout the application development and delivery lifecycle. All of this work, though, ultimately appears in vain if your clients keep finding flaws in the product that was given.
Even with such thorough internal testing and quality checks, how did the flaw go undetected? When and where was it first mentioned? What, or wLastly, what will it take to make it right? Even though the solutions to these concerns are difficult to come by, it seems imperative. Customer-found flaws (CFDs) have the potential to cast doubt on your brand's legitimacy and dedication to providing high-quality and dependable service in a timely manner.
Beyond the "Known Knowns"
A few basic questions need to be addressed when we diagnose the "what, when, and how" of faults. Are the procedures we use for quality assurance (QA) flawless? For what are we conducting tests? Are there any known variables that we are not tracking? Exist any unidentified factors in the equation that could surprise us? This is particularly nicely explained by the design thinking paradigm of knowns and unknowns.
Our QA procedures are typically created to monitor only the "Known Knowns." These are known problems that have been dealt with in the past. To guarantee that no such problems make it into the production environment, a number of tests are conducted. These include automated regression tests (ARTs), unit tests, and even thorough manual tests carried out by trained business analysts.
Optimize and Quantify: Recognize the "known" and get ready for the "unknown"
Quality and reliability engineering teams frequently overlook measurement, which is the most important factor in continuously improving quality, as they concentrate on putting best practices and standard procedures into place.
To start, you must decide the key performance indicators (KPIs) to use to gauge software quality at each stage of the development and delivery process, and then standardize them. Nevertheless, it is undeniable that evaluating quality is more complex than evaluating time-to-market or other more concrete characteristics of software delivery. Numerous variables, both objective and subjective, have a role.
Conclusion
Engineering teams can employ advanced analytics and machine learning to identify the underlying cause of "Known Unknowns" and move an increasing number of issues into the "Known Knowns" category by having more visibility into all phases of application delivery.
Ultimately, self-service analytics combined with real-time software performance visibility enables engineers to identify and address unforeseen problems early on, maintaining continuous reliability and lessening the effect of the "Unknown Unknowns."