Continuous Testing Success With Automation and Effective Frameworks
An introduction to DZone's 2020 Trend Report, The Rise of Continuous Testing: Identifying Capabilities and Challenges in the SDLC
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Continuous Testing, a term that started to increase in popularity in mid-2019, has made its way into many of today’s CI/CD processes used in the SDLC, but what exactly does the phrase mean? Continuous testing (CT) refers to the idea of automated testing of software as it passes through various stages in the software delivery pipeline. It is a technique employed to provide insight into risks associated with new and upcoming software release candidates before they are released to production.
The continuous testing ideology stands in stark contrast to the old thought process of performing most of the application testing after deployment.
CT often involves many forms of testing different layers in the application — everything from unit, integration, API, performance, systems, security, and functional (UI/UX) testing. Integrating automated testing into the lifecycle of software development:
- Helps assess risk to the business.
- Delivers feedback used in every stage of the delivery pipeline.
- Reduces the length of the feedback loop between development and deployment of software.
A shorter time identifying risky and detrimental defects in software helps ensure a quality product and contributes to higher client satisfaction with the deliverable.
Continuous testing goes hand in hand with CI/CD processes to improve the time between product enhancements and feature updates. CT exposes development defects much sooner in the SDLC, which results in a faster time to patch issues before they make their way into production environments. This efficiency factor leads to more frequent and more manageable deployments, with companies releasing new build deployments on a much shorter cadence — anywhere from once every two weeks to as many as multiple times per day.
Other benefits stemming from continuous testing include assessing the end-user experience, reducing the number of false positives, improved code quality, and emphasizing mitigations to business risk. Continuous testing is a relatively new concept and one that is largely a cultural shift for all parties involved in the development process. CT can be difficult to implement correctly without the right tools, knowledge, and sufficient resources.
Nevertheless, continuous testing is an important paradigm shift and will continue to prove its worth as it evolves through AI and machine learning to further improve testing processes and feedback handoff at each stage in the delivery pipeline.
To understand more about these shifts, DZone conducted original research and a survey among members of the DZone Community about their insights into and experience with continuous testing. The following conclusions from our research explore CT’s impact on culture, the benefits and challenges of adoption, and how AI and machine learning are predicted to transform CT capabilities over the next six to 12 months. You will also find new articles from contributors, whom shared their experiential knowledge about continuous testing frameworks and test automation throughout the SDLC.
Read our 2020 Continuous Testing Trend Report to learn more!
Opinions expressed by DZone contributors are their own.