Can Continuous Testing Resolve Software Failures?

Skim through history and witness how more than three billion people got affected by software failures in one way or another in 2017. It wasn’t just the IT industry, entertainment, media, news, financial, and even healthcare were on top of the hit list. The effect extended across ‘best case’ scenarios comprising of user frustration, poor usage rates, and major customer loss, to worst-case scenarios covering grave security breaches, traumatized brand reputations, heavy revenue loss, costly legal complications, and crashing stock prices.

Tricentis prepared a detailed report that clearly mentions the scope of these software failures in its Annual Software Fail Watch report, identifying 606 software failures that affected nearly half of the world’s population (3.7 billion people) and 314 companies. It’s the magnitude of these figures and the loss that these incidents made headlines.

Why do these software failures happen anyway?

To answer that in on sentence – it’s this endless stream of new digital offerings from newcomers, and discount offerings or upgrades in digital products from third-party resources that are bringing harm to companies’ competitive position and market shares every day.

The chance to launch a new functionality in the market ahead of the competition, such as Bluetooth connected devices, Apple Pay, and facial recognition, can frequently be seen as a difference between winning and losing. Nonetheless, in an attempt to accomplish customer demand, many companies are powerless in ensuring the provision of optimal performance, functionality, as well as anticipated quality.

Many enterprises have still not progressed in their software testing processes as they should have. A failure in putting the latest best practices in action, an absence of capabilities using the latest technologies and tools, and complications in putting together the right team and the much-needed skill sets are all together hindrances. Moreover, the faster pace that is driven by DevOps and Agile methodologies only tends to expose those inadequacies and limitations further.

In different surveys conducted in the near past, participants have stated that at different instances their teams are facing challenges due to the increasing complexity of software testing and the mounting pressure to guarantee smooth and seamless processes and integrations.

Looking at these surveys closely, one can easily derive that employees have felt a high degree of difficulty and complications have risen due to the technology disruption. Additionally, they feel that they don’t have enough time to dedicate to testing of new apps and services, which is why they are being pushed into production without going through proper QA procedures.

To cater to this situation, enterprises and organizations can either outsource their QA work to a top-notch software testing company in the market or adopt automation testing to accelerate test cycle time. Since, test automation is significant for evolving their DevOps and Agile goals, a majority of enterprises adopted automation in only 25% of their total testing assignments. In the latest World Quality Report, the average level of automation was identified to be only 16%.

‘No move’ is not the right move!

Without continuous improvement in software delivery, digital projects will fail to accomplish consumer expectations. And when it does, it also crashes the market share, revenue, as well as customer loyalty. Remember the Equifax incident?

Here’s the thing: you need to carry on updating your software testing skills similar to enhancing your software development skills. For delivering better products, you should be able to test better. Most of us are completely unaware of the versions and updates of our most-used apps that hit the market on weekly basis. In fact, many of these apps auto-update daily without us being aware of it.

QA organizations need to keep pace by continuously refining their processes for faster cycle time besides improved operational competence. Just by speeding up and automating your regression cycles is not sufficient to produce the desired outcome.

Continuous improvement calls for continuous testing

Continuous testing involves automating the testing of software on a wider level to fast-track the feedback process on possible defects and any business risks accompanying any given release. This calls for a re-evaluation of the traditional test automation process and that involves addressing the entire testing process to successfully accommodate the faster pace and increased complications due to current application development and delivery.

Fundamentally, continuous testing demands a complete test process optimization for enhanced test automation. This can only be laid down by making some necessary changes in the test and QA process, including:

• using RPA to remove duplicates and improve the effectiveness of existing test cases
• putting strict automation guidelines in place with a self-healing bot to authenticate adherence
• leveraging data to line up test execution in terms of risk, objective, and pass/fail expectation
• using test data management and service virtualization for executing effective end-to-end tests in representative user and operating ecosystems.
• arranging testing with quality indicators and risk analysis factors into release decisions.

Several businesses emphasize on automating just one or perhaps two aspects of the testing ecosystem. There are a number of tools in the market to accomplish this goal. But these tools offer limited features on their own.

Continuous testing involves robust integration and bringing together different stages of QA testing process beginning with the requirements and expectations as well as repeating with new insights, analytics, and user feedback.

Owing to this broader, more cohesive approach, continuous testing facilitates organizations in comprehending where they can and should and shouldn’t automate for optimum results.

Here comes AI

Keep this in mind that continuous testing is not possible without a high percentage of your tests being automated.

Artificial intelligence (AI), together with Machine Learning and BOTs, has surfaced as a major powerful force in the current digital setting. AI-powered test automation solutions, or intelligent testing, can help speed up test automation in several ways, including:

• • optimizing test case management
  • using adaptive learning to detect high-risk defects, bugs, and vulnerabilities
  • automating test result analysis to identify, classify, and prioritize defect configurations for regression engineers.

By incorporating AI in test automation efforts, enterprises can escalate their capability to leverage continuous testing for overcoming time to market challenges and also accomplish the rigorous quality standards necessary to achieve anticipated business outcomes.

Through continuous testing, businesses can align their systems, processes, QA engineering, and test automation needs to fast-tracking cycle time and provides real business outcomes, including sophisticated quality products and services, improved customer experience, competitive differentiation, and operational efficiency.