PEARL VIII : Agile Test Automation : Application of agile development principles to Test Automation

PEARL VIII :  Agile Test Automation : Application of agile development principles to Test Automation

Introduction

Today, more and more software development companies are shifting to an agile development approach from traditional waterfall development to keep pace with the current trends.To meet the demands of agile environment, several companies have adopted test automation which resolves some of the issues faced in manual testing and delivers faster results.

Even though test automation provides assistance in many ways, there are several challenges in test automation which can turn out to be a nightmare if it is adopted without appropriate brainstorming and analysis. Here’s a complete list of approaches to make test automation work successfully in agile environment.

The Development methodology agile team follows when creating automated tests is very similar to the process they follow for creating the software being tested. It involves a fair bit of design, coding and testing of its own to get it working correctly. So just like the application itself, automated tests are best developed incrementally – adding new tests and features to the automation framework over several different sprints. It’s important not to aim for the perfect “it-can-do-everything” test framework right at the start, as it would never materialize. Balance the cost vs. ROI well and come up with a bare minimum working solution at the start.

As more companies move from a traditional waterfall software development approach to an agile Methodology, suppliers are offering more test automation tools and services. Automated tests can provide faster feedback than a manual test, reducing rework and long feedback cycles.

Manual testing, particularly manual exploratory testing, is still important. However,  Agile teams typically find that the fast feedback afforded by automated regression is  a key to detecting problems quickly, thus reducing risk and rework.

Research firm Forrester expects testing-as-a-service (TaaS) to emerge as a managed test service from an increase in test automation, as large suppliers such as HP seek to support customers with test automation tools and outsourcing options, including security testing and services for SAP. Better test automation is intrinsic to agile development

Working tests, just like working software, are useful; they build confidence and get everyone excited about the progress being made. Successes, even small ones, make it easier to bring everyone on-board – especially when the test automation solution team have created actually runs and proves to be of real value to the team.

Agile Test team can’t set up automated testing tools without team members who can write code.  Automating tests is software development. It must be done with the same care and thought that goes into writing production code.This poses a significant hurdle for testers who lack coding skills. That challenge is best met by mastering those skills, not by shying away from projects that demand them

Better software is the result of running the right tests and continually re-evaluating which tests are the right ones,

Automated unit tests check the behavior of individual functions/methods and object interactions. They’re to run often, and provide feedback in minutes. Automated acceptance tests usually check the behavior of the system end-to-end. (Although,  sometimes they bypass the GUI, checking the underlying business logic.) They’re  typically run on checked in code on an ongoing basis, providing feedback in an hour  or so. Agile projects favor automated tests because of the rapid feedback they provide.

Agile Testing Quadrant

TheAgileTestingQuadrants.png

In the agile testing quadrant depicted above, The order in which quadrants numbered  has no relationship to when the different types of testing are done. Most projects would start with Q2 tests, because those are where you get the examples that turn into specifications and tests that drive coding, along with prototypes and the like. The quadrants are a taxonomy to help teams plan their testing and make sure they have all the resources they need to accomplish it.The timing of the various types of tests depends on the risks of each project, the customers’ goals for the product, whether the team is working with legacy code or on a greenfield project, and when resources are available to do the testing.

The lower left quadrant represents test-driven development, which is a core agile development practice. The quadrants on the left include tests that support the team as it develops the product.The testing done in Quadrants 1 and 2 are more requirements specification and design aids than what we typically think of as testing.

Unit tests verify functionality of a small subset of the system, such as an object or method. Component tests verify the behavior of a larger part of the system, such as a group of classes that provide some service [Meszaros, 2007]. Both types of tests are usually automated with a member of the xUnit family of test automation tools. We refer to these tests as programmer tests, developerfacing tests, or technology-facing tests. They enable the programmers to measure what Kent Beck has called the internal quality of their code [Beck, 1999].
A major purpose of Quadrant 1 tests is test-driven development (TDD) or test-driven design. The process of writing tests first helps programmers design their code well. These tests let the programmers confidently write code to deliver a story’s features without worrying about making unintended changes to the system. They can verify that their design and architecture decisions are appropriate. Unit and component tests are automated and written in the same programming language as the application. A business expert probably couldn’t understand them by reading them directly, but these tests aren’t intended for customer use. In fact, internal quality isn’t negotiated with the customer; it’s defined by the programmers. Programmer tests are normally part of an automated process that runs with every code check-in, giving the team instant, continual feedback about their internal quality

The tests in Quadrant 2 also support the work of the development team, but at a higher level. These business-facing tests, also called customer-facing tests and customer tests, define external quality and the features that the customers want.

They describe the details of each story. Business-facing tests run at a functional level, each one verifying a business satisfaction condition. They’re written in a way business experts can easily understand using the business domain language. In fact, the business experts use these tests to define the external quality of the product and usually help to write them.

One of the most important purposes of tests in these two quadrants (Q1 and Q2) is to provide information quickly and enable fast troubleshooting. They must be run frequently in order to give the team early feedback in case any behavior changes unexpectedly. All of these tests should be run as part of an automated continuous integration, build, and test process.

Technology-facing tests in Quadrant 4 are intended to critique product characteristics such as performance, robustness, and security. The types of tests that fall into the fourth quadrant are just as critical to agile development as to any type of software development. These tests are technology-facing. Creating and running these tests might require the use of specialized tools and additional expertise.

Quadrant 4 Automation tools

Native database tools

  •  SQL, data import tools
  • Shell scripting 
  • Monitoring tools examples
    • jConsole
      • Application bottlenecks, memory leaks
    • jProfiler
      • Database and bean usage
  • Commercial load test tools
    • Loadrunner
    • Silk Performer
  • Open source test tools
    • jMeter
    • The Grinder
    • jUnitPerf
  • Performance test providers
  • Multiple sites

The short iterations of agile development give your team a chance to learn and experiment with the different testing quadrants

Test Automation Backlog

Maintain a test automation backlog for the project that contains all needed automation tasks and identified improvements. Then target a few items from the backlog every sprint, in no time team will start to see the new regression test suite taking shape. Occasionally, stories from the test automation backlog may require dedicated developer time to implement and consequently some buy-in from the product owner in order to proceed. However, it should not be difficult to convince the product owner about the value of such stories if everyone on team is committed to quality. A test automation backlog could contain a prioritized list of items such as:

  • Parameterize the test environment for test execution.
  • Integrate with Continuous Integration.
  • Enhance reporting mechanism.
  • Provide an option to attach error logs in notification emails.
  • Collect performance metrics for workflow scenarios.
  • Add tests to check for concurrent execution of critical test cases.

Agile test automation pyramid

TA-pyramid 2 Figure 1

In the traditional Test Automation Pyramid view, most if not all of the effort was in developing UI-centric functional tests that explored the application via the GUI. There might be some lower-level tests and a few unit tests, but teams mostly staid at the upper tier.

The agile test automation pyramid is a strategy that attempts to alter the Test automation pyramid and provide the Agile Test automation pyramid depicted in the figure above.

Agile testing relies more on automation. It requires a much greater contribution from developers. And it has a different basic philosophy – to prevent bugs.

The first change is taking a whole-team view. Instead of the testers being responsible for testing AND writing all of the test automation, it becomes a whole-team responsibility. The developers take most of the ownership for unit-level automation, but testers can operate here as well.

At the base of the test automation pyramid is unit testing. Unit testing should be the foundation of a solid test automation strategy and as such represents the largest part of the pyramid.(50 % to 60 % – Unit Tests)

The upper tier focuses on limited GUI-based automation tests. Usually, these are longer running, core customer usage workflows that are best implemented at this level.

Automated user interface testing (0% to 10%) is placed at the top of the test automation pyramid because we want to do as little of it as possible.

The testers typically operate on these, but there are very few tests within this tier. And remember that the developers can operate here as well. The two layers are met by middle-tier automation. This is often the domain of the open source ATDD/BDD tools, such as: FitNesse, Cucumber, JBehave, Robot Framework, and others. The middle tier is the Acceptance Test tier / API Layer tests are performed.

One key thing to note is that traditional automation was often a one-tool operation, with Mercury/HP commercial tooling (Quick Test Professional or QTP) leading that space.  The agile approach is tool agnostic, but also aggregates tools that are appropriate for each layer. Therefor no “one size fits all” thinking is allowed. For example, these are common used tools at each tier:

  1. UI tier:  Selenium, Watir, or traditional QTP
  2. Middle tier:  FitNesse, Robot Framework, and Cucumber
  3. Unit tier:  xUnit family variants for example JUnit or NUnit for Java and .Net respectively

The other consideration is that there are extensions to many of these. For example, both Robot Framework and Cucumber have Selenium plug-ins so that they can ‘drive’ the UI as well as the middle tier. This implies that the middle tier tooling, and automated tests for that matter, can extend or blend into the lower and upper tiers.

Rather than investing in extensive, heavyweight step-by-step manual test scripts in  Word or a test management tool, we capture expectations in a format supported by  automated test frameworks like FIT/Fitnesse. The test could be executed manually,  but more importantly that same test artifact becomes an automated test when the  programmers write a fixture to connect the test to the software under test.

Test-driven development (TDD)

One key benefit of agile software development is the ability to make changes to the code quickly and easily. As the team embraces changing requirements as a part of a living, breathing system, they need to know that their changes will not cause a domino effect of broken code. Ideally, the design of the system will be so simple and well isolated that any possible effects of a change will be readily apparent. In the real world, this is not always the case, especially when working with legacy systems. In this case, something must be implemented to ensure that if a change affects other areas of the system, there will be an immediate red-flag, rather than having to chase down the issues later.

After experimenting with different ways to enable this flexibility, agile teams hit upon the practice of Test Driven Development, which involves producing automated unit tests for production code before you write that production code. Instead of writing tests afterward (or, more typically, never writing those tests), you always begin with a unit test. For every small chunk of functionality in production code, you first build and run a small, focused test that specifies and validates what the code will do. This test might not even compile, at first, because all of the classes and methods it requires may not yet exist. Nevertheless, it functions as an executable specification. You then get it to compile with minimal production code, so that you can run it and watch it fail. You then produce exactly as much code as will enable that test to pass. Sometimes you expect it to fail, and it passes, which is also useful information.

This technique feels odd, at first, to quite a few programmers who try it. It’s a bit like rock climbers inching up a rock wall, placing anchors in the wall as they go. Why go to all this trouble? Surely it slows you down considerably. The answer is that it only makes sense if you end up relying heavily and repeatedly on those anchors (unit tests) later. Those who practice Test Driven Development regularly claim that the benefits of those unit tests more than pay back the effort required to write them.

For Test-First work, you will typically use one of the xUnit family of automated unit test frameworks (JUnit for Java, NUnit for C#, etc). These frameworks make it quite straightforward to create, run, organize, and manage large suites of unit tests. Test Driven Development has grown to be such a useful tool that it is well integrated into most of the major IDEs, including Visual Studio and Eclipse.

Test-driven development (TDD) is an advanced technique of using automated unit tests to drive the design of software and force decoupling of dependencies. The result of using this practice is a comprehensive suite of unit tests that can be run at any time to provide feedback that the software is still working. This technique is heavily emphasized by those using Agile development methodologies

The motto of test-driven development is “Red, Green, Refactor.”

  • Red: Create a test and make it fail.
  • Green: Make the test pass by any means necessary.
  • Refactor: Change the code to remove duplication in your project and to improve the design while ensuring that all tests still pass.

The Red/Green/Refactor cycle is repeated very quickly for each new unit of code.

Follow these steps (slight variations exist among TDD practitioners):

  1. Understand the requirements of the story, work item, or feature that you are working on.
  2. Red: Create a test and make it fail.
    1. Imagine how the new code should be called and write the test as if the code already existed. You will not get IntelliSense because the new method does not yet exist.
    2. Create the new production code stub. Write just enough code so that it compiles.
    3. Run the test. It should fail. This is a calibration measure to ensure that your test is calling the correct code and that the code is not working by accident. This is a meaningful failure, and you expect it to fail.
  3. Green: Make the test pass by any means necessary.
    1. Write the production code to make the test pass. Keep it simple.
    2. Some advocate the hard-coding of the expected return value first to verify that the test correctly detects success. This varies from practitioner to practitioner.
    3. If you’ve written the code so that the test passes as intended, you are finished. You do not have to write more code speculatively. The test is the objective definition of “done.” The phrase “You Ain’t Gonna Need It” (YAGNI) is often used to veto unnecessary work. If new functionality is still needed, then another test is needed. Make this one test pass and continue.
    4. When the test passes, you might want to run all tests up to this point to build confidence that everything else is still working.
  4. Refactor: Change the code to remove duplication in your project and to improve the design while ensuring that all tests still pass.
    1. Remove duplication caused by the addition of the new functionality.
    2. Make design changes to improve the overall solution.
    3. After each refactoring, rerun all the tests to ensure that they all still pass.
  5. Repeat the cycle. Each cycle should be very short, and a typical hour should contain many Red/Green/Refactor cycles.

Characteristics of a Good Unit Test

A good unit test has the following characteristics.

  • Runs fast, runs fast, runs fast. If the tests are slow, they will not be run often.
  • Separates or simulates environmental dependencies such as databases, file systems, networks, queues, and so on. Tests that exercise these will not run fast, and a failure does not give meaningful feedback about what the problem actually is.
  • Is very limited in scope. If the test fails, it’s obvious where to look for the problem. Use few Assert calls so that the offending code is obvious. It’s important to only test one thing in a single test.
  • Runs and passes in isolation. If the tests require special environmental setup or fail unexpectedly, then they are not good unit tests. Change them for simplicity and reliability. Tests should run and pass on any machine. The “works on my box” excuse doesn’t work.
  • Often uses stubs and mock objects. If the code being tested typically calls out to a database or file system, these dependencies must be simulated, or mocked. These dependencies will ordinarily be abstracted away by using interfaces.
  • Clearly reveals its intention. Another developer can look at the test and understand what is expected of the production code.

Rich feature sets provided by the automation framework would essentially reduce the effort put in by the development teams in the TDD workflow. Here are some of the aspects of automation frameworks that can pay rich dividends: Ease of tracking: Unit tests would be stored in a central repository (part of the automation framework) with all the development team members submitting their unit tests to it. Tests would be stored in hierarchical folder structure based on the product features and its components. With this, viewing and tracking of unit tests within and across teams would be smoother. Traceability of unit tests: Automation frameworks can ensure that each product requirement has a unit test associated with it. This ensures that all requirements are developed as part of the TDD process, thus avoiding development slippages.

Improving the development and review process: Automation infrastructure can facilitate tracking of all requirements by associating them with a developer and reviewer(s). This would ensure that development and review processes are organized. Unit test execution: A good automation framework ensures quick running of automated unit tests. The tests could be executed selectively for a component, set of features or the product itself. Reporting of test execution results: Results of the automated unit test for a component/ feature would be sent to the respective developer; this ensures quick reporting and cuts short the response time in refactoring unit tests from the developer. Automation infrastructure components: Automation frameworks could facilitate:

  • Cross-platform testing
  • Compatibility testing

xUnit frameworks

Developers may use computer-assisted testing frameworks, such as xUnit, to create and automatically run the test cases. Xunit frameworks provide assertion-style test validation capabilities and result reporting. These capabilities are critical for automation as they move the burden of execution validation from an independent post-processing activity to one that is included in the test execution. The execution framework provided by these test frameworks allows for the automatic execution of all system test cases or various subsets along with other features.

Fakes, mocks and integration tests

Unit tests are so named because they each test one unit of code. A complex module may have a thousand unit tests and a simple module may have only ten. The tests used for TDD should never cross process boundaries in a program, let alone network connections. Doing so introduces delays that make tests run slowly and discourage developers from running the whole suite. Introducing dependencies on external modules or data also turns unit tests into integration tests. If one module misbehaves in a chain of interrelated modules, it is not so immediately clear where to look for the cause of the failure.

When code under development relies on a database, a web service, or any other external process or service, enforcing a unit-testable separation is also an opportunity and a driving force to design more modular, more testable and more reusable code. Two steps are necessary:

  1. Whenever external access is needed in the final design, an interface should be defined that describes the access available.
  2. The interface should be implemented in two ways, one of which really accesses the external process, and the other of which is a fake or mock. Fake objects need do little more than add a message such as “Person object saved” to a trace log, against which a test assertion can be run to verify correct behaviour. Mock objects differ in that they themselves contain test assertions that can make the test fail, for example, if the person’s name and other data are not as expected.

Fake and mock object methods that return data, ostensibly from a data store or user, can help the test process by always returning the same, realistic data that tests can rely upon. They can also be set into predefined fault modes so that error-handling routines can be developed and reliably tested. In a fault mode, a method may return an invalid, incomplete or null response, or may throw an exception. Fake services other than data stores may also be useful in TDD: A fake encryption service may not, in fact, encrypt the data passed; a fake random number service may always return 1. Fake or mock implementations are examples of dependency injection.

A Test Double is a test-specific capability that substitutes for a system capability, typically a class or function, that the UUT depends on. There are two times at which test doubles can be introduced into a system: link and execution. Link time substitution is when the test double is compiled into the load module, which is executed to validate testing. This approach is typically used when running in an environment other than the target environment that requires doubles for the hardware level code for compilation. The alternative to linker substitution is run-time substitution in which the real functionality is replaced during the execution of a test cases. This substitution is typically done through the reassignment of known function pointers or object replacement.

Test doubles are of a number of different types and varying complexities:

  • Dummy – A dummy is the simplest form of a test double. It facilitates linker time substitution by providing a default return value where required.
  • Stub – A stub adds simplistic logic to a dummy, providing different outputs.
  • Spy – A spy captures and makes available parameter and state information, publishing accessors to test code for private information allowing for more advanced state validation.
  • Mock – A mock is specified by an individual test case to validate test-specific behavior, checking parameter values and call sequencing.
  • Simulator – A simulator is a comprehensive component providing a higher-fidelity approximation of the target capability (the thing being doubled). A simulator typically requires significant additional development effort.

A corollary of such dependency injection is that the actual database or other external-access code is never tested by the TDD process itself. To avoid errors that may arise from this, other tests are needed that instantiate the test-driven code with the “real” implementations of the interfaces discussed above. These are integration tests and are quite separate from the TDD unit tests. There are fewer of them, and they must be run less often than the unit tests. They can nonetheless be implemented using the same testing framework, such as xUnit.

Integration tests that alter any persistent store or database should always be designed carefully with consideration of the initial and final state of the files or database, even if any test fails. This is often achieved using some combination of the following techniques:

  • The TearDown method, which is integral to many test frameworks.
  • try…catch…finally exception handling structures where available.
  • Database transactions where a transaction atomically includes perhaps a write, a read and a matching delete operation.
  • Taking a “snapshot” of the database before running any tests and rolling back to the snapshot after each test run. This may be automated using a framework such as Ant or NAnt or a continuous integration system such as CruiseControl.
  • Initialising the database to a clean state before tests, rather than cleaning up after them. This may be relevant where cleaning up may make it difficult to diagnose test failures by deleting the final state of the database before detailed diagnosis can be performed.

Continuous Integration

CI is a software development practice, where Team members integrate their work frequently, usually with each check-in to the version control system . An automated server builds the system, runs all the unit tests and reports “green” or “red” to indicate if the build is stable/broken.  Building and Testing are done on a dedicated machine to eliminate “it works on my machine” dependencies.

Over the past three years, Ancestry.com, the world’s largest online family history resource, underwent a significant transformation in technology and infrastructure. Starting with the adoption of Agile development practices, the company evolved to a Continuous Delivery model that enables code release whenever the business requires it. Transitioning from large, weekly or bi-weekly software rollouts to smaller, incremental updates has allowed Ancestry.com to increase responsiveness and deliver new features to customers more quickly.

Continuous Integration features

  • Very High ROI. One of the first engineering practices an Agile teams should adopt.
  • Many open source and commercial tools available
  • Keep builds and tests fast (move slow running tests to a nightly build)
  • Adopt a “Stop the Line” mentality (all work stops until a broken build is fixed)
  • Test in a clone of production (Every environmental difference results in risk)

Continuous integration (CI) is the practice, in software engineering, of merging all developer working copies with a shared mainline several times a day. It was first named and proposed as part of extreme programming (XP). Its main aim is to prevent integration problems, referred to as “integration hell” in early descriptions of XP. CI can be seen as an intensification of practices of periodic integration advocated by earlier published methods of incremental and iterative software development, such as the Booch method. CI isn’t universally accepted as an improvement over frequent integration, so it is important to distinguish between the two as there is disagreement about the virtues of each.

CI was originally intended to be used in combination with automated unit tests written through the practices of test-driven development. Initially this was conceived of as running all unit tests and verifying they all passed before committing to the mainline. This helps avoid one developer’s work in progress breaking another developer’s copy. If necessary, partially complete features can be disabled before committing using feature toggles.

Later elaborations of the concept introduced build servers, which automatically run the unit tests periodically or even after every commit and report the results to the developers. The use of build servers (not necessarily running unit tests) had already been practised by some teams outside the XP community. Nowadays, many organisations have adopted CI without adopting all of XP.

In addition to automated unit tests, organisations using CI typically use a build server to implement continuous processes of applying quality control in general — small pieces of effort, applied frequently. In addition to running the unit and integration tests, such processes run additional static and dynamic tests, measure and profile performance, extract and format documentation from the source code and facilitate manual QA processes. This continuous application of quality control aims to improve the quality of software, and to reduce the time taken to deliver it, by replacing the traditional practice of applying quality control after completing all development. This is very similar to the original idea of integrating more frequently to make integration easier, only applied to QA processes.

In the same vein the practice of continuous delivery further extends CI by making sure the software checked in on the mainline is always in a state that can be deployed to users and makes the actual deployment process very rapid.

Twist at AutoTrader

Testing was growing unwieldy for AutoTrader.co.uk, the UK’s #1 automotive website, with multiple tools, lack of  automation, and increasing test maintenance. Enter Twist—the test automation platform that reduced testing effort  by 95%, achieved 89% test coverage, and minimized testing time by 75%. Furthermore, Twist removed the biggest  pain-point, by drastically reducing the overhead of test maintenance. With Twist, the team could add real value and  test new functionality at a rapid pace

Sustaining quality for a dynamic and complex application can end up in a vicious cycle of modifying tests, testing
new functionality, and optimizing existing tests. AutoTrader.co.uk is the UK’s most popular motoring website, and
the fourth busiest search engine, getting over 10 million unique visitors every month. With such high traffic
volumes, optimal test coverage is not just nice-to-have, but a critical necessity. However, testing such a complex,
and ever changing application was a maintenance-heavy, lengthy, and expensive exercise that was further
complicated by difficult testing tools. Auto Trader liked Twist’s support for writing test specifications in plain English,
automating robust regression tests, and integrating with continuous integration (CI) and source control products,
and decided to give it a try. Since 2008, Twist has dramatically reduced AutoTrader.co.uk’s testing effort and
increased test coverage. It has also improved collaboration between business analysts (BAs) and quality analysts
(QAs) on tests, by simplifying technically complex tests to readable English constructs. Twist’s highly refactored,
modular test suite now exhaustively tests the site in 12 minutes, and can easily keep pace with application changes.
Additionally, Twist’s usability makes it highly popular across the teams.

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