Continuous Testing Tools: 7 Considerations for Product Evaluation

Solutions Review’s Expert Insights Series is a collection of contributed articles written by industry experts in enterprise software categories.  In this submission, Digital.ai CTO of Continuous Testing Guy Arieli offers key considerations for buyers when evaluating continuous testing tools and solutions.

Enterprise app developers are under pressure to deliver critical new applications faster than ever that meet sky-high user demands for a killer UI and UX. And businesses often depend on fast release cycles to speed up digital transformation initiatives and drive critical new revenue streams.

At the same time, cybercriminals are branching out beyond just the usual phishing, ransomware, and malware attacks. Bad actors have become creative when it comes to infiltrating company defenses and compromising systems and apps. According to the Verizon Data Breach Incident report, one of the newest, most volatile threats and the leading breach category is “System Intrusion” – any unauthorized activity within a digital system. Meanwhile, “bring your own device” policies have accelerated with the rise of remote work, and this increases risks as well. For enterprise developers building mission-critical mobile and web applications to power banking services or healthcare apps, failure on the security front is not an option.

The best way to improve mobile app security is to make the apps as secure as possible during development. So it comes as no surprise that continuous testing solutions are becoming imperative to more secure applications, fueling fast adoption worldwide. The continuous testing market, valued at nearly $1.5 billion USD in 2020, is expected to reach nearly $3.5 billion by 2026, with a projected CAGR of 15.24% between 2021-2026.

But not all solutions are created equal. Continuous testing solutions need to be: automated, rapid, iterative, guarantee-able, and auditable. Here are seven important considerations when evaluating a continuous/automated testing tool:

Mobile testing solutions should be multi-layered with manual and automated capabilities

It’s crucial that any solution include a healthy mix of automated and manual tests to ensure all scenarios are covered. This includes manual testing, which forms the foundation of a mobile app testing project because teams use manual capabilities for beta testing as well. But developer and testing teams also need automated testing that encompasses other layers such as end-to-end testing, and unit testing. And beta testing is important as well because it will enable teams to get customer feedback earlier in the process.

Testing being done needs to easily be benchmarked against industry standards and user expectations

An important part of testing, benchmarking helps organizations gain insight into how their products compare to both competitors and to customer expectations. This information helps identify areas for improvement for the functionality of the app being tested, and also the development and testing processes used in the SDLC. There are two main types of benchmarking testing solutions should incorporate: Technical Benchmarking, which compares product and service capabilities to those of leading competitors, and Competitive Benchmarking, which uses customer data, and feedback to measure how well a company’s products rank against the competition.

Performance needs to be monitored: different networks and environments need to be examined and defects need to be fixed as early as possible

In addition to testing the functionality of an app, developers need to test how their apps perform under different conditions and if necessary improve UX and usability across different devices and OSes. A testing solution needs to incorporate Performance Testing, which checks the device itself (if the app puts stress on the device, that might lead to the app crashing or in extreme situations the device itself being damaged) and gain insights into device performance as well as identify and eliminate app performance bottlenecks, network performance, server performance, and recovery capabilities.

Continuous testing should be executed at different stages during the SDLC

The best practice is to automate tests whenever possible during the SDLC, as code quality needs to be validated at every stage to give developers insights into which versions and release candidates are ready for delivery. When the tests are automated, it is easier to get risk feedback as automated tests help the process move faster. The process of CI/CD should be incorporated into continuous testing. Continuous Integration allows developers to move projects into a shared repository to automatically build software and get fast feedback about its quality, adding reliability and velocity to the SDLC. Continuous Delivery enables developers to expand their automated testing to include UI testing, load testing, integration testing, and regression testing.

Take into consideration different physical devices, as the market is more fragmented than ever with thousands of devices running hundreds of different OSes and versions

It is not possible or even effective to try to test every single device OS combination. However with a lab of devices teams can run tests on a wide array of different device/OS combinations, pulled from monitoring market trends and user information to best satisfy the target device groups. Developers need to make sure they can test their apps on a matrix of devices to ensure apps function properly no matter what the OS is, especially for Android apps. As many as 90% of iOS users are on the current OS version, while Android apps have more devices, with varied CPU, memory, and screen options running the OS.

AI analytics should be present that analyze threats using data correlation and detect bugs during the development process

Software releases are expected more frequently than ever, and customers demand that these releases are highly functional and error-free. Incorporating AI analytics in the process helps discover defects faster based on historical data insights and provides a level of analysis to ensure that only the highest quality code is sent to production. Data received from AI analytics helps teams understand the impact of changes to the code, and enables teams to automate their testing and drive innovation – increasing visibility and boosting the effectiveness of automated continuous testing.

It is important to be able to test on the latest OSes to mitigate device and OS fragmentation

Developers need to have the ability to test on OSes before GA, so the shift to new versions will be seamless and security issues can be discovered and eliminated before the OS is even released – making apps more secure. In a continuous testing environment, activities like security testing need to take place throughout the SDLC from development to deployment. Utilizing the potential of CT for security checks is a somewhat overlooked process: implementing continuous penetration testing works as part of continuous testing and can simulate a range of cyber-attacks that probe network vulnerabilities. These tests, like CT does for code defects, help companies examine and improve the security of their systems and the response of their teams to these scenarios.

Final Thoughts

As threats escalate and organizations implement more cautious security protocols, there is increasing pressure on software developers to make apps more secure during the development process.

Continuous testing and automated testing solutions can ensure that apps are more secure during the development process, well before they are released in the App Store or on Google Play.