This is part of the Solutions Review’s premium content series, a collection of contributing columns written by industry experts in mature software categories. In this presentation, Digital.ai Continuous Testing Chief Technology Officer Guy Arieli presents key considerations to buyers when evaluating continuous testing tools and solutions.
Enterprise application developers are under pressure to deliver important new applications faster than ever that meet the superior demands of users for a killer user interface and user experience. Companies often rely on rapid release cycles to accelerate digital transformation initiatives and drive critical new revenue streams.
At the same time, cybercriminals are branching out far beyond just the usual phishing, ransomware, and malware attacks. Bad actors get creative when it comes to infiltrating company defenses and compromising systems and applications. According to the Verizon Data Breach Incident Report, one of the newest, most volatile threats and the leading category of hacking is “system intrusion”—that is, unauthorized activity within a digital system. Meanwhile, “bring your own device” policies have accelerated with the advent of remote work, and this increases the risks as well. For enterprise developers building mission-critical mobile and web applications to support banking or healthcare applications, failing on the security front is not an option.
The best way to improve mobile app security is to make apps as secure as possible during development. It is therefore not surprising that continuous testing solutions are becoming essential for more secure applications, leading to rapid worldwide adoption. The continuous testing market, valued at approximately US$1.5 billion in 2020, is expected to reach approximately US$3.5 billion by 2026, with Projected compound annual growth rate of 15.24% between 2021-2026.
But not all solutions are equal. Continuous testing solutions must be: automated, rapid, iterative, guaranteeable and auditable. Here are seven important considerations when evaluating a continuous/automated test tool:
Mobile testing solutions should be multi-layered with manual and automated capabilities
It is critical that any solution includes a healthy mix of automated and manual testing to ensure that all scenarios are covered. This includes manual testing, which forms the basis for a mobile app testing project because teams use the manual capabilities of beta testing as well. But developer and test teams also need automated testing that includes other layers like end-to-end testing and unit testing. Beta testing is also important as it will enable teams to get customer feedback early in the process.
Conducted testing needs to be easily scaled against industry standards and users’ expectations
An important part of testing, benchmarking helps organizations gain insight into how their products compare to both competitors and customer expectations. This information helps identify areas of improvement for the functionality of the application being tested, as well as the development and testing processes used in the SDLC. There are two main types of benchmarking testing solutions you should include: technical benchmarking, which compares product and service capabilities to those of major competitors, and competitive benchmarking, which uses customer data and feedback to measure how well a company’s products are against the competition.
Performance must be monitored: Various networks and environments must be scanned and defects must be fixed as soon as possible
In addition to testing application functionality, developers need to test how their applications perform under different conditions and, if necessary, improve user experience and usability across different devices and operating systems. The testing solution needs to integrate performance testing, which checks on the device itself (if the application is putting stress on the device, it can crash the application or damage the device itself in extreme situations) and gain insight into device performance as well as identify and remove bottlenecks in application performance and performance Network and server performance and recovery capabilities.
Continuous testing must be carried out at various stages during the SDLC
Best practice is to automate tests whenever possible during SDLC, as code quality should be checked at every stage to give developers insights into releases and release candidates ready for delivery. When tests are automated, it is easier to get feedback on risks as automated tests help the process move faster. The CI/CD process must be incorporated into the ongoing testing. Continuous integration allows developers to move projects to a shared software repository automatically and get quick feedback on their quality, adding reliability and speed to SDLC. Continuous delivery allows developers to extend their automated testing to include UI testing, load testing, integration testing, and regression testing.
Consider the different physical hardware, as the market is more fragmented than ever with thousands of devices running hundreds of different operating systems and versions
It is not possible or even effective to attempt to test every combination of hardware operating systems. However, with a hardware lab, teams can run tests on a wide range of different hardware/operating system combinations, which are pulled from monitoring market trends and user information to best satisfy their target hardware groups. Developers need to ensure that they can test their apps on an array of devices to ensure that apps work properly regardless of the operating system, especially for Android apps. Up to 90% of iOS users are using the current operating system version, while Android apps have more devices, with various options for the CPU, memory, and screen running the operating system.
AI analytics must be in place to analyze threats using data correlation and detect errors during the development process
Software is expected to be released more frequently than ever, and customers demand that these releases be high-performance and bug-free. Integrating AI analytics into the process helps detect flaws faster based on historical data insights and provides a level of analysis to ensure that only the highest quality code is sent into production. Data from AI analytics helps teams understand the impact of changes to code, enables teams to automate their testing and drive innovation – increasing visibility and enhancing the effectiveness of automated continuous testing.
It is important to be able to test the latest operating systems to mitigate device and operating system fragmentation
Developers need to have the ability to test on pre-GA operating systems, so the transition to new versions will be seamless and security issues can be detected and eliminated before the operating system is released – making apps more secure. In a continuous testing environment, activities such as security testing should take place throughout the SDLC from development to deployment. Using CT capabilities to perform security checks is a somewhat overlooked process: performing continuous penetration testing work as part of ongoing testing and can simulate a range of cyberattacks that investigate network vulnerabilities. These tests, like CT tests for code defects, help companies examine and improve the security of their systems and the response of their teams to these scenarios.
As threats mount and organizations implement more cautious security protocols, there is increasing pressure on software developers to make applications more secure during the development process.
Continuous testing and automated testing solutions can ensure that apps are more secure during the development process, long before they are released to the App Store or Google Play.