Software deliveries have accelerated as technology progresses, resulting in more frequent releases. Modern app development relies more and more on methodologies like DevOps and Agile for fast and effective deliveries. Continuous testing plays a huge part in these releases, by integrating into the delivery pipelines. Teams use tools to automate and execute tests, enabling code deployment decisions to be influenced by current test reports.
This article will explore what continuous testing is, its current trends, and some challenges you are likely to encounter while implementing it.
What is Continuous Testing?
Continuous testing, or CT, is a process in which software applications are tested continuously, usually through automated testing, throughout the SDLC. The main objective is to evaluate the software quality across the entire life cycle, provide early feedback, and enable high-quality and fast deliveries. Continuous testing was initially presented as a solution to reduce the time it takes for developers to receive feedback by including both development environment-triggered tests and more traditional developer/tester trigger tests.
CT aims to offer fast and consistent feedback on the business risk in the most recent builds or release candidates. Testers can then use this data may to decide whether the program is ready to move through the delivery pipeline at any moment.
Continuous testing in DevOps streamlines automated end-to-end testing, fixing errors, and maintaining continuity. It reduces feedback cycles, prioritizes quality at every stage, and enhances test coverage. Early integration of CI/CD pipeline automation testing minimizes business risks and instills confidence in code updates.
Continuous testing is a valuable tool for QA teams to identify and fix bugs before a release, providing insights and improving testing efficiency. It helps find the best testing approach for the software development cycle and eliminates false positives and timeouts.
Continuous testing also reduces the time required for designing, developing, and delivering software updates, guiding developers in making informed decisions on when and how to release necessary changes. It focuses on improving the user experience by preventing defects from disrupting the way the users interact with the software.
Continuous Testing Trends
Some of the current trends surrounding CT these past years have been around hybrid cloud testing, AI-powered testing, as well as non-functional aspects of testing.
Hybrid Cloud Testing
Hybrid and cloud testing have become popular due to their flexibility and cost-effectiveness. Teams are increasingly searching for testing systems that can operate in both hybrid and cloud settings. For this, a versatile platform is needed to support hybrid environments effectively. Hybrid and cloud-based test environments outperform on-premise hardware, indicating they are here to stay.
AI-Augmented Testing
AI and Machine Learning (ML) have grown in popularity over the past years. Efforts to use AI and ML in testing remain limited, but interest increases and we can expect to see it more in the future. Many people believe that AI is the future of testing. AI can generate test scripts quickly, eliminating the need for human or hybrid coding, resulting in faster and more efficient testing. AI can also provide trustworthy test data, which can be a challenge for testers.
Accessibility Testing
Accessibility has become a major trend in 2024 for many organizations. This is due in part to legal requirements in various countries, as well as the fact that more accessibility equals more users interested in using your application. And, of course, if development is done with accessibility in mind, this aspect also needs to be tested. Since it’s something that can be automated, it’s a good CT candidate.
Non-functional Testing
In teams looking to shift testing left non-functional testing will continue to play a big role. Non-functional testing, including performance and load testing, is critical for organizational success. Load testing tools enable teams to do scalable performance tests on online and mobile apps, as well as APIs. You can test other non-functional aspects as part of a continuous testing strategy, such as usability, security, and visual testing.
Low-code/No-code solutions
Low-code and no-code solutions are gaining popularity fast. Many companies invest in tools that can be used even by their less technical team members. Since many testers feel that manual is the most time-consuming part of the testing process, no-code/low-code testing tools significantly speed up this process.
Challenges in Continuous Testing
Continuous Testing does not come without its challenges. Some hurdles you may have to overcome while implementing CT are:
- Lack of automation resources: A lack of resources, test environments, and test data can derail testing efforts. Acquiring effective test data can be a stressful barrier in the testing process. Setting up conventional service virtualization applications may be time-consuming, difficult, and costly. Additionally, mocks and stubs may not accurately represent real-world circumstances.
- Implementing AI: Although many people consider AI very useful, a recent Testrail testing and quality report revealed that 54% of respondents do not yet have AI technologies incorporated into their testing processes.
- Test failure analysis: Analyzing test automation results can be time-consuming. The main reasons tests fail are environment issues, test script issues, test data issues, or defects. Testers need to analyze the test reports to determine whether new defects have been found or if other issues are causing the failures, rather than assuming the failures result from actual defects.
Final Thoughts
With continuous testing, software applications are continuously tested throughout the software development life cycle (SDLC). We can expect future trends in CT like hybrid cloud testing, AI-powered testing, a focus on accessibility and non-functional testing, as well as low-code/no-code adoption. CT is a valuable tool for QA teams to identify and fix bugs before releases, improve testing efficiency, and reduce the time required for designing, developing, and delivering software updates.
