The real price of bugged software
The true cost of software failure, is not restricted to loss of revenue. There are significant reputation and competitive risks associated with poor quality software. All you to have to do is look at a few examples to get an idea of the damage it can have.
For starters, let’s take distinguished payment processing firm Worldpay. They processed 36 million payments, which suffered major glitches for three weeks.
British Airways faced considerable IT failures in 2017, impacting their website, mobile apps and call centres. As a result, an estimated 70,000 flights had to be cancelled.
All of this left experts predicting that BA could face a final bill of more than £100 million in compensation costs.
In an ever-evolving digital age, the source of business strategy and technology, falls in the hands of customer experience
In an attempt to try and meet the increasing expectations of digital customers, enterprises have started to embrace the Agile and DevOps approach.
In terms of quality, stability, and release velocity, software quality and testing, are more important now than ever before, becoming almost mandatory to a customers’ digital experience.
Test and QA teams have adopted test automation to meet the mandate of speed, greater complexity, and error-free releases.
Software test automation, paved the way for a change that provided scale, efficiency and faster time-to-market for the QA process.
Ongoing integration (and continuous testing) have decreased the testing life-cycle, by adding to efficiency gains.
The agile mantra is “high quality at speed.”
Businesses continue to battle with sluggish test suites, that get setback by mounting QA backlogs. These automate large quantities of test cases, creating poor visibility and inadequate coverage. To put it simply, test automation needs to be smarter and more intuitive.
By learning from trend analysis of legacy data.
Error-patterns can then be anticipated and avoided, thus re-inventing the wheel. This is where disruptive technology trends like BOTs, artificial intelligence (AI) and machine learning (ML) come into play, altering the entire quality life-cycle as we know it.
Intelligent test automation, heralds the third wave of the test automation/DevOps journey, with a preemptive, prescriptive and predictive approach to quality.
Intelligent testing uses (AI and ML), can address pain points’ of organisations, and introduce data-driven insights, predictions and recommendations, therefore automating, optimising and improving the software development life-cycle.
This has seen a shift in descriptive (or reactive analytics), into predictive and prescriptive.
Some significant challenges that businesses face today, involve huge amounts of test data and results. This includes redundant test cases, flakiness of tests and maintenance/decision-making, not to mention an overwhelming amount of information.
Intelligent testing tools can sift through high volumes of test data, as well as analyse trends, decode patterns, and forecast future trends and outcomes. Tools analyse both structured and unstructured data, gathered from defect management tools, automating test results using this information to predict outcomes, so that actionable insights can be gathered.
Artificial intelligence & Machine Learning
Forget validity being a singular priority, automatic detection of regressions and high-risk defects in apps, are just as important now. Using this data-driven approach, software has the ability to predict failures, bottlenecks, error categories and productivity attempts across project cycles. Is this enough coverage?
Testing more than necessary? What should you prioritise and focus on? This information is exceptionally valuable when you have a large QA backlog, or are looking at the release deadline for a complex application suite. With machine learning, you can project data and make informed, proactive decisions.
These intelligent insights help decide the next course of action, improving outcomes and ultimately creating a constant feedback loop. QMetry’s Intelligent Testing tools, implement solution stacks that enable agility, efficiency and quality. Their end-to-end automation, led by AI and ML, help companies:
• Optimise test coverage, and depth
• Increase re-usability, with data-driven testing.
• Enhance quality of test suites, with higher traceability and visibility.
• Weed-out duplicates, and dead test cases.
•Predict outcomes, and prescribe actionable changes.
• Forecast accurate and insightful decisions for release-readiness, testing adequacy and risk index.
• Improve ROI, while reducing time-to-market.