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Continuous testing is the process of executing automated tests as part of the software delivery pipeline to obtain immediate feedback on the business risks associated with a software release candidate.
In the 2010s, software has become a key business differentiator. As a result, organizations now expect software development teams to deliver more, and more innovative, software within shorter delivery cycles. To meet these demands, teams have turned to lean approaches, such as Agile, DevOps, and Continuous Delivery, to try to speed up the SDLC. After accelerating other aspects of the delivery pipeline, teams typically find that their testing process is preventing them from achieving the expected benefits of their SDLC acceleration initiative. Testing and the overall quality process remain problematic for several key reasons.
- Traditional testing processes are too slow. Iteration length has changed from months to weeks or days with the rising popularity of Agile, DevOps, and Continuous Delivery. Traditional methods of testing, which rely heavily on manual testing and automated GUI tests that require frequent updating, cannot keep pace. At this point, organizations tend to recognize the need to extend their test automation efforts.
- Even after more automation is added to the existing test process, managers still lack adequate insight into the level of risk associated with an application at any given point in time. Understanding these risks is critical for making the rapid go/no go decisions involved in Continuous Delivery processes. If tests are developed without an understanding of what the business considers to be an acceptable level of risk, it is possible to have a release candidate that passes all the available tests, but which the business leaders would not consider to be ready for release. For the test results to accurately indicate whether each release candidate meets business expectations, the approach to designing tests must be based on the business's tolerance for risks related to security, performance, reliability, and compliance. In addition to having unit tests that check code at a very granular bottom-up level, there is a need for a broader suite of tests to provide a top-down assessment of the release candidate's business risk.
- Even if testing is automated and tests effectively measure the level of business risk, teams without a coordinated end-to-end quality process tend to have trouble satisfying the business expectations within today's compressed delivery cycles. Trying to remove risks at the end of each iteration has been shown to be significantly slower and more resource-intensive than building quality into the product through defect prevention strategies such as development testing.
Organizations adopt Continuous Testing because they recognize that these problems are preventing them from delivering quality software at the desired speed. They recognize the growing importance of software as well as the rising cost of software failure, and they are no longer willing to make a tradeoff between time, scope, and quality.
Goals and benefits
The goal of continuous testing is to provide fast and continuous feedback regarding the level of business risk in the latest build or release candidate. This information can then be used to determine if the software is ready to progress through the delivery pipeline at any given time.
Since testing begins early and is executed continuously, application risks are exposed soon after they are introduced. Development teams can then prevent those problems from progressing to the next stage of the SDLC. This reduces the time and effort that need to be spent finding and fixing defects. As a result, it is possible to increase the speed and frequency at which quality software (software that meets expectations for an acceptable level of risk) is delivered, as well as decrease technical debt.
Moreover, when software quality efforts and testing are aligned with business expectations, test execution produces a prioritized list of actionable tasks (rather than a potentially overwhelming number of findings that require manual review). This helps teams focus their efforts on the quality tasks that will have the greatest impact, based on their organization's goals and priorities.
Additionally, when teams are continuously executing a broad set of continuous tests throughout the SDLC, they amass metrics regarding the quality of the process as well as the state of the software. The resulting metrics can be used to re-examine and optimize the process itself, including the effectiveness of those tests. This information can be used to establish a feedback loop that helps teams incrementally improve the process. Frequent measurement, tight feedback loops, and continuous improvement are key principles of DevOps.
Scope of testing
For testing functional requirements (functional testing), Continuous Testing often involves unit tests, API testing, integration testing, and system testing. For testing non-functional requirements (non-functional testing - to determine if the application meets expectations around performance, security, compliance, etc.), it involves practices such as static code analysis, security testing, performance testing, etc. Tests should be designed to provide the earliest possible detection (or prevention) of the risks that are most critical for the business or organization that is releasing the software.
Teams often find that in order to ensure that test suite can run continuously and effectively assesses the level of risk, it's necessary to shift focus from GUI testing to API testing because 1) APIs (the "transaction layer") are considered the most stable interface to the system under test, and 2) GUI tests require considerable rework to keep pace with the frequent changes typical of accelerated release processes; tests at the API layer are less brittle and easier to maintain.
Tests are executed during or alongside continuous integration—at least daily. For teams practicing continuous delivery, tests are commonly executed many times a day, every time that the application is updated in to the version control system.
Ideally, all tests are executed across all non-production test environments. To ensure accuracy and consistency, testing should be performed in the most complete, production-like environment possible. Strategies for increasing test environment stability include virtualization software (for dependencies your organization can control and image) service virtualization (for dependencies beyond your scope of control or unsuitable for imaging), and test data management.
- Testing should be a collaboration of Development, QA, and Operations—aligned with the priorities of the line of business—within a coordinated, end-to-end quality process.
- Tests should be logically-componentized, incremental, and repeatable; results must be deterministic and meaningful.
- All tests need to be run at some point in the build pipeline, but not all tests need be run all the time.
- Eliminate test data and environment constraints so that tests can run constantly and consistently in production-like environments.
- To minimize false positives, minimize test maintenance, and more effectively validate use cases across modern systems with multitier architectures, teams should emphasize API testing over GUI testing.
Since modern applications are highly distributed, test suites that exercise them typically require access to a dependencies that are not readily available for testing (e.g., third-party services, mainframes that are available for testing only in limited capacity or at inconvenient times, etc.) Moreover, with the growing adoption of Agile and parallel development processes, it is common for end-to-end functional tests to require access to dependencies that are still evolving or not yet implemented. This problem can be addressed by using service virtualization to simulate the application under test's (AUT's) interactions with the missing or unavailable dependencies. It can also be used to ensure that data, performance, and behavior is consistent across the various test runs.
One reason teams avoid continuous testing is that their infrastructure is not scalable enough to continuously execute the test suite. This problem can be addressed by focusing the tests on the business's priorities, splitting the test base, and parallelizing the testing with application release automation tools.
Continuous Testing vs automated testing
The goal of Continuous Testing is to apply "extreme automation" to a stable, production-like test environments. Automation is essential for Continuous Testing. But automated testing is not the same as Continuous Testing.
Automated testing involves automated, CI-driven execution of whatever set of tests the team has accumulated. Moving from automated testing to continuous testing involves executing a set of tests that is specifically designed to assess the business risks associated with a release candidate, and to regularly execute these tests in the context of stable, production-like test environments. Some differences between automated and continuous testing:
- With automated testing, a test failure may indicate anything from a critical issue to a violation of a trivial naming standard. With continuous testing, a test failure always indicates a critical business risk.
- With continuous testing, a test failure is addressed via a clear workflow for prioritizing defects vs. business risks and addressing the most critical ones first.
- With continuous testing, each time a risk is identified, there is a process for exposing all similar defects that might already have been introduced, as well as preventing this same problem from recurring in the future.
Since the 1990s, Continuous test-driven development has been used to provide give programmers rapid feedback on whether the code they added a) functioned properly and b) unintentionally changed or broke existing functionality. This testing, which was a key component of Extreme Programming, involves automatically executing unit tests (and sometimes acceptance tests or smoke tests) as part of the automated build, often many times a day. These tests are written prior to implementation; passing tests indicate that implementation is successful.
Continuous Testing tools
Gartner evaluated 9 tools that met their criteria for enterprise-grade test automation tools. The evaluation involved inquiries with Gartner clients, surveys of tool users, vendor responses to Gartner questions, vendor product demonstrations. Gartner required tools to support native Windows desktop application testing and Android or iOS testing support as well as support 3 of the following: responsive web applications, mobile applications, package applications, API/web services. The results of the 2016 Magic Quadrant research are:
- Leaders: Hewlett Packard Enterprise, Tricentis, IBM
- Challengers: Microsoft, Worksoft
- Visionaries: TestPlant, Micro Focus, SmartBear Software
- Niche players: Ranorex
Forrester Research evaluated 11 tools that met their criteria for enterprise-grade test functional automation tools. Forrester determined 33 criteria based on past research, user needs, and expert interviews, then evaluated products versus that criteria based on vendor responses to Forrester questions, vendor product demonstrations, and customer interviews. Forrester required tools to have cross-browser, mobile, UI, and API testing capabilities. The results of the 2016 Forrester wave are:
- Leaders: Parasoft, IBM, Tricentis, Hewlett Packard Enterprise
- Strong performers: Microsoft, Micro Focus, SmartBear Software, TestPlant
- Contenders: Conformiq, Original Software, LogiGear
- Automated testing
- Continuous delivery
- Continuous integration
- Release management
- Service virtualization
- Software testing
- Ariola, Wayne; Dunlop, Cynthia (2014). Continuous Testing. CreateSpace. ISBN 978-1494859756.
- Gruver, Gary; Mouser, Tommy (2015). Leading the Transformation: Applying Agile and DevOps Principles at Scale. IT Revolution Press. ISBN 978-1942788010.
- Whittaker, James; Arbon, Jason; Carollo, Jeff (2012). How Google Tests Software. Addison-Wesley Professional. ISBN 978-0321803023.
- Part of the Pipeline: Why Continuous Testing Is Essential, by Adam Auerbach, TechWell Insights August 2015
- The Relationship between Risk and Continuous Testing: An Interview with Wayne Ariola, by Cameron Philipp-Edmonds, Stickyminds December 2015
- DevOps: Are You Pushing Bugs to Clients Faster, by Wayne Ariola and Cynthia Dunlop, PNSQC October 2015
- DevOps and QA: What’s the real cost of quality?, by Ericka Chickowski, DevOps.com June 2015
- The Importance of Shifting Right in DevOps, by Bob Aiello, CM Crossroads December 2014
- Kinks persist in Continuous Workflows, by Lisa Morgan, SD Times September 2014
- Continuous Testing: Think Different, by Ian Davis, Visual Studio Magazine September 2011
- Testing in a Continuous Delivery World, by Rob Marvin, SD Times June 2014
- Shift Left and Put Quality First, by Adam Auerbach, TechWell Insights October 2014
- The Forrester Wave™ Evaluation Of Functional Test Automation (FTA) Is Out And It's All About Going Beyond GUI Testing, by Diego Lo Giudice, Forrester Research April 23, 2015
- Continuous Development Brings Changes for Software Testers, by Amy Reichert, SearchSoftwareQuality September 2014
- Zeichick’s Take: Forget 'Continuous Integration'—the Buzzword is now 'Continuous Testing', by Alan Zeichick, SD Times February 2014
- Buy the Wrong Software? A Fix Can Cost $700,000 A Conversation with voke’s Theresa Lanowitz, by Dom Nicastro , CMS Wire October 2014
- Jones, Capers; Bonsignour, Olivier (2011). The Economics of Software Quality. Addison-Wesley Professional. ISBN 978-0132582209.
- Kolawa, Adam; Huizinga, Dorota (2007). Automated Defect Prevention: Best Practices in Software Management. Wiley-IEEE Computer Society Press. p. 73. ISBN 0-470-04212-5.
- Theresa Lanowitz Talks Extreme Test Automation at STAREAST 2014, by Beth Romanik, TechWell Insights May 2014
- Guest View: What’s keeping you from Continuous?, by Noel Wurst, SD Times November 2015
- Manage the Business Risks of Application Development with Continuous Testing, by Wayne Ariola, CM Crossroads September 2014
- The Power of Continuous Performance Testing, by Don Prather, Stickyminds August 2015
- Practices for DevOps and Continuous Delivery, by Ben Linders, InfoQ July 2015
- Produce Better Software by Using a Layered Testing Strategy, by Sean Kenefick, Gartner January 7, 2014
- Cohn, Mike (2009). Succeeding with Agile: Software Development Using Scrum. Addison-Wesley Professional. p. 312. ISBN 978-0321579362.
- Experiences from Continuous Testing at Siemens Healthcare, by Ben Linders, InfoQ February 2015
- DevOps- Not a Market, but a Tool-Centric Philosophy That Supports a Continuous Delivery Value Chain, by Laurie F. Wurster, Ronni J. Colville, Jim Duggan, Gartner February, 2015
- Keep your Software Healthy During Agile Development, by Adrian Bridgwater, ComputerWeekly November 2013
- Extreme automation, meet the pre-production life cycle, by Alexandra Weber Morales, SD Times January 2014
- Continuous Integration (original version), by Martin Fowler, DevOps.com September 2000
- Magic Quadrant for Software Test Automation, Gartner, November 16, 2016
- The Forrester Wave™: Modern Application Functional Test Automation Tools, Q4 2016, Gartner, December 5, 2016.