’Test Automation’ is a Historical Accident and an Oxymoron. Time to Move On to Something Better!

The phrase “Test Automation” is a historical accident, and not a victimless one either.

“Automation” started around the 1950’s with industrial automation; building stuff faster, better, and cheaper. It continues to grow today, with more industrial robots every year.

DevOps is growing too, with automation moving software through the development process to ship, faster, better and cheaper. DevOps is true to the meaning of “automation,” because the focus is on the end product: shipped software.

“Test” is the traditional word used for measuring software quality and finding issues (bugs). When people started driving the system under test (SUT) automatically to help with this, “test automation” was an obvious way to describe it, but it was and is a poor fit: unlike with industrial automation or DevOps, given that the software product is an SUT (or, at least it’s being driven with fake, test users and fake, test data) the end product has zero value; nobody cares. Instead of the end product of the SUT, people instead focused on the pass or fail result of running a bit of “test automation.”

“Test automation” encourages the perception that what people in the manual testing role, what all people involved in software development do at least part of the time, can be automated away. Wrong! People are smart, observant, flexible and perceptive. Automated measurements of software quality can be very powerful for the business (as I describe below) but they can’t do what people can do.

It got worse with another historical “oops:” In 1979, Glenford Myers wrote what turned out to be a highly influential book on software “test,” and in this book he insisted, repeatedly and emphatically, that the whole point of “test” in general is to find “bugs.” This reinforced the perception that for “test automation,” if it passed, nothing else matters; it didn’t find a bug, so we don’t care about any other details. Oops… although in 1979, this was a fairly good approximation, the conceptual mistake remained and gradually became much more significant through now, 37 years later, and beyond.

In 2016, we have software doing some critically important things, starting with online banking through web portals, through self-driving cars and on passenger airplanes. Software flaws could ruin a person financially, or kill her, and the magnitude of software’s impact on our lives grows every year.

We can no longer afford to be distracted by the misleading “test automation” or the idea that test is only about finding bugs. We must pay attention to how we drive the product, and how it responds. We must know immediately, and in detail, of flaws and even when some part of the product is not measurable due to some other failure. We can’t afford to wait for some person in quality assurance (QA) to debug through a problem, especially not at nighttime or across a geographically dispersed team. We can’t afford to continue dropping important, actionable quality information, functional and performance information, on the floor, and hope that if it was important and actionable, somebody on the team might dig it out later.

For software that matters, we must record that information, store it and act on it if appropriate, with automation in real time and, also, make it relevant and queryable to anybody on the team who cares to look to get information on functional and performance quality, including near-term events and long-term trends.

Replace “test automation” with quality automation. “Test automation” only really works for the QA team, and not very well either. Quality automation avoids the misconceptions of “test automation” and, by focusing and using what automation does and does well, delivers transparency and business value across the larger software team.

Log statements in the automation do add some value, but any structure in the actions or log statements is lost because it becomes a list of loosely-formatted statements, lossy of information, not very queryable, not friendly to automated processes after the measurements are completed.

BDD and Gherkin offer a way of logging business-facing steps with check runs, but this requires a tool to be installed and distributed and interpreted keywords to be implemented. Information on technology-facing steps is lost. Implementation of keywords tends to drift when reuse is required (I know, been there and done that) and this information is lost, too, to the relative obscurity of the keyword implementation source code, and never to be exposed outside QA.

MetaAutomation shows a better way, starting with the Atomic Check pattern: don’t interpret or implement keywords; instead, drive the product with self-documenting steps. Put the steps in a natural hierarchy, like top-down modelling in business process modelling, and have them document themselves in this hierarchy.

Now, the business-facing steps and every technology-facing step that drives the product is self-documenting, in compact and highly queryable valid XML, in a hierarchy that reflects the process of driving the SUT for every check. Every step records how many milliseconds it took to complete that step, at every node in the hierarchy.

No interpreter needed! No keywords to implement, no Gherkin language to adapt and learn! No 3^rd-party tools to install, deploy, or update!

This is just the start for quality automation, though: the detailed self-documentation of the checks supports the Smart Retry pattern. This is the answer to working with checks that can fail intermittently due to a variety of reasons: now that a check result documents itself in how the product was driven, in the context of driving the product (and potentially, with additional instrumentation data placed in the structured data result of the check) root cause is now nicely recorded. An implementation of Smart Retry can now answer these questions, and take action, in real time:

·         Was the failure due to an external dependency?

·         Should I retry the check, or not?

·         Did I just reproduce the failure?

All the data is recorded. Flaky tests are a thing of the past; no need to mark tests as “untrustworthy,” no need to interrupt an engineer’s workflow with an impromptu debug session.

Atomic Check also ensures that the data is there so Automated Triage will work. Emails send to long DLs to beg a group of busy engineers “will somebody please follow up on this? There might be a problem here” are a thing of the past. Email folders filled with what is commonly viewed as annoying SPAM, are no more.

http://MetaAutomation.net has more information and two working samples to illustrate:

The first is easy to set up, run, modify, and reuse, and will run across processes on one machine. For example, one check can run across any number of processes.

The second is more work to set up, but will run checks across any number of machines or VMs. Running a single check across multiple machines is how one does end-to-end checks for the Internet of Things.

Both samples use a free version of Visual Studio 2015, and come with instructions.

Recognize the “oops!!” of “test automation” and “test is just about finding bugs.” Grieve for a moment – we’re all entitled to that! - then move on to something much better.

Quality automation is inevitable. MetaAutomation describes a language-independent and platform-independent way to get there. Why a pattern language of six patterns? Because that’s by far the best way I can describe it.

Vastly greater productivity, transparency, and team happiness await.

Post #6 of 6: For Effective Quality Automation, Know the Limits

One of the very basic values of MetaAutomation is to know what the manual testing role is good at, and is, in fact, indispensable.

By “manual testing role” I mean any person on the team who ever does anything with the software system under test (SUT) and is in a position to notice something awry, some issue where the software behavior (or even non-functional quality issue) does not meet the requirements or somebody’s expectations, and can characterize and record the issue (i.e., “bug” it) for the team so the issue can be considered for a potential action item to fix it. So, this does not necessarily require a person who is full-time committed to a test role or a manual test role.

People are smart. People are clever, innovative, flexible and observant. People notice stuff and can communicate it to other people (or, record it for their own records). Quality automation, or the automation that makes and communicates quality measurements, is very good and powerful at measuring and reporting on performance and steps in driving the product, and regressing functional requirements, but there’s a lot of stuff that it can’t see at all because it’s difficult, risky, or impossible to measure many things about the SUT.

For example, a web page layout: is the page attractive, readable, and usable? Quality automation won’t tell you. One needs the manual test role to follow up. Fortunately, you’re doing this anyway, even if nobody on the team thinks of him or herself as a “tester,” assuming somebody on the team checks over the product before it goes live.

Poor understanding of the boundary between what manual test is better suited for, and what quality automation should be written to do, is expensive in terms of product cost and risk.

For example, I’ve seen too much put inappropriately on the manual testing role; on a credit union web app, giving manual testers ownership to verify correct bank balances is very expensive and risky because that aspect of the product is very high priority but it might not get verified reliably by manual testers for any number of reasons, and in any case, it’s slow to do that manually. The result is extra cost and risk.

I’ve also seen too much put on quality automation: trying to verify many low-priority and relatively superficial aspects with quality automation can be tricky to write and maintain and flaky to run. For example, is a control on the screen the correct color? Unless there’s a high-priority functional requirement there, it’s better to skip that with quality automation. Checking such properties with quality automation can make checks that are too complicated, slow or flaky, or too many checks run to verify low-priority aspects of product quality.

Quality automation and manual testing have (or should have) a relationship: quality automation checks these things, and manual testing verifies the rest, notices odd stuff, and does exploratory testing. The relationship depends on knowing where the team has decided the boundaries and limits are; if the manual testing role doesn’t know what has already been checked, for a given product version and build, there will be missed stuff and duplicated work.

Keep checks simple and well-documented so what is verified is clearly understood. The Atomic Check pattern of MetaAutomation describes how to make checks “atomic,” indivisible really, so that they can’t be simplified by breaking up the check into smaller checks.

Documentation is good but can be expensive and risks falling out of date due to minor changes. Atomic Check describes the ideal solution here: self-documentation of the checks! Even better, naturally hierarchical check steps enable self-documentation of the business-facing logic of the check, easily displayed, and the atomic technology-facing check steps at the same time.

How does that work?

Download and run one or both samples on http://MetaAutomation.net to see this in action. Step through the code, make changes, and even implement your own atomic checks with hierarchical self-documenting steps.

If that is too much change for your team right now, here’s a take-away you can use immediately: knowing the boundary between quality automation and manual test reduces risk and effort, and ensures that no aspect of product quality is unintentionally missed.

This page is #6 in a series of six, with the original post here.

Post #5 of 6: Good Code Practices for Quality Automation

If MetaAutomation is too much change for right now…

Start with making your code as good as it can be.

I quote Elisabeth Hendrickson in the book on MetaAutomation:

It is tempting for organizations to treat infrastructure code as somehow inferior to production code. 'It's just scripts,' they say. 'We don't need to put seasoned engineers on it.' However what I've seen is that the infrastructure code -- that includes build and CI scripts as well as tests and test frameworks -- is the foundation on which the rest of the code is built. If the infrastructure code is not treated with the same care as production code, then everything is built on a shaky foundation.

Teams should follow the team’s adopted practices for product code where it makes sense, but also consider making some changes for quality automation code:

·       Some types of security and performance considerations for product code might be unnecessary in quality automation code, since the latter will never ship outside the team’s control.

·       Consider using some structures to help with quality automation-specific code, e.g., to make the actions of driving the product self-documenting in detail. The sample projects on http://MetaAutomation.net show many details on this.

Here too, there are more details in the book on MetaAutomation.

If you have a choice, use a compiled language. The idea that an interpreted language like Python can make QA engineers more productive is an illusion, because not having a compiler on your side can result in runtime failures that a compiler would have caught, causing significant time and cost in following up. An interpreted language lets one write code faster, but that’s not the whole value story. A compiled language like C# will tell you about all sorts of problems immediately, and many others almost immediately and certainly before committing code to a repository. This saves a lot of time.

As a counter-example, I’ve also used Ruby to do quality automation, and among many other shortcomings of this language, it effectively hid problems from me until much later than writing the code, causing significant cost and frustration.

Consider using the same language as the product code, so less information gets lost at the boundary between the product and quality automation code driving the product, the dev role can contribute as needed to quality automation, and the QA role can know more of the product and even contribute to the product as needed.

Personal anecdote: I’ve added XSL code to a product, mainly written in Java, to make an important product web site vastly more testable using both Java and XSL in the quality automation code. This kind of thing is much easier if the languages are common between quality automation code and product code.

Careful to avoid copy-and-paste code; this causes maintenance cost, because the code might have to be updated in multiple places (since the same code is copied multiple places) and if the coder misses one or more of the copies, more wasted time and cost result.

For checks that use a GUI or a web browser, always synchronize to objects or events if possible, rather than sleep. This will help performance and reliability of the checks.

A few more minor points:

·         Maximize code reusability and reuse.

·         Make symbol names descriptive, so code becomes self-documenting.

·         Comment code but only if needed, and always at one level of abstraction above the code itself.

·         Always do code reviews! It’s an opportunity to learn from each other and raise code quality and uniformity across the team.

This page is #5 in a series of six, with the original post here.

Post #4 of 6: Make Your Checks Simple

If MetaAutomation is too much change for right now…

Make your checks simple, to improve scalability of your check runs with more resources and to improve the business value of your quality data. (OK, that’s “improve the quality of your quality data” and yes I know that might sound silly.)

There is a pattern called “Chained Tests” that occurs in the wild, and described here: http://xunitpatterns.com/Chained%20Tests.html but it’s actually a very bad idea. I’d call it an antipattern. The linked page goes into the negatives a bit, but I will add a more modern reason to NEVER chain your tests: they won’t scale. Chained tests must be run in a sequence, so it doesn’t matter how much computing resources you allocate, the check (aka “test”) run won’t go any faster.

As an extension of the “scalability” reasoning, make your checks simple. Each check should have no more than one target verification or verification cluster, and NOT have superfluous verifications that can either slow the check down and/or make it less robust and more fragile than it would otherwise be.

Here is a color version of one of the diagrams illustrating the Atomic Check pattern in the book on MetaAutomation:

Another way to simplify the checks, and make them faster, is to use the Precondition Pool pattern of MetaAutomation.

This page is #4 in a series of six, with the original post here.

Post #3 of 6: Make your check fails self-documenting

If MetaAutomation is too much change for right now…

Verify that you’re doing all you can to minimize debugging costs, in case of check failure.

1.       Add preliminary verifications wherever null-reference exceptions occur

A null-reference exception (or null-pointer exception for a native language) will lose information: which symbol was null? There is often more than one possibility on a given line number. If there’s an ambiguity, it might require a debug session to find out, and in some cases, the line number might have changed for the version of the code you can access versus the one that was running and threw the exception.

Labeled preliminary verifications, e.g., asserting non-null “<some API> returned null>” will find the condition sooner and show it with much better specificity than if there were no such verification. With complex code this can make the difference between a clearly understood root cause of check failure, and a very confused one.

If there isn’t a preliminary verification for where the null could potentially happen, and the check failure can’t be reproduced easily, a debug session can be either very expensive or even useless, meaning that the information of the failure is lost forever.

2.       Verify that asserts or verifications are self-documenting

Labels for thrown exceptions add value in two ways: First, they describe what happened, to make resolution of a check failure easier, and second, they ensure that there is no ambiguity of where or under what circumstances the failure occurred. Using the built-in exceptions can make sense for produce code where the standard for performance is high, but for quality automation code, use an assert or a custom exception to make the failure explicitly self-describing.

3.       Add more log statements around points of failure

Consider the difference in cost between adding a few more log statements around parts of checks that are likely to fail, and reproducing and debugging through the failure, maybe more than once, especially if the failure is hard to reproduce.

Adding log statements is much cheaper and lower-risk.

4.       Make all checks fail on an exception, even negative cases

For negative checks that are expected to fail, enclose the expected failure in code that verifies the expected failure condition, then throw another exception in case of failure to fail the check.

Exceptions are intended to be used in exceptional circumstances. Overuse of exceptions, or too much code that is expected to be bypassed in case of an exception, reduces reusable code in the quality automation infrastructure because a new pattern would be required for coding the negative check method signatures, and makes any cleanup more complex.

The Atomic Check pattern of MetaAutomation shows how to do all this with much more structure, transparency, and detailed hierarchical self-documenting check code.

This page is #3 in a series of six, with the original post here.

Post #2 of 6: Basic Check Design

If MetaAutomation is too much change for right now…

Here are some easy steps to take:

1.       Make your checks independent of each other.

This is how checks can be fast and scalable: they must be independent of each other. Even the simplicity of the checks depends on them being independent of each other, and in remember in case of a failure, the value of what a check can find can depend on simplicity, because with long or complex checks, things can get muddy and any business value can be clouded and even lost because it becomes too much work to debug through the check to try to get the actionable information out, surface it and describe it for the benefit of the larger team.

This is a core principle of the Atomic Check pattern of MetaAutomation: all checks are independent of each other. This is how checks can scale across resources. If the “Chained Tests” antipattern is still used, checks can’t scale at all because they still have to happen in a time sequence, and any failure might depend on an operation earlier in the sequence, so the value of any issues found depends first on a lot of work to reproduce that sequence of operations.

2.       Prioritize your checks based on business requirements (or even functional requirements)

It seems obvious, doesn’t it? Prioritize… work on the more important parts of your product first.

If your team doesn’t have business requirements, you probably don’t know what you’re building, and the risk is that you’re building the wrong thing for your customers. See the excellent book by Robin Goldsmith on this topic, for example.

At least, however you’re defining your requirements (e.g., with user stories), prioritize them and prioritize your checks based on requirements.

This is part of the Atomic Check pattern as well.

3.       Ensure that your automated checks are completely repeatable. If needed, record the parameters used so you can repeat them as needed.

Whether or not you’ve ever had a failure in any one check, you need to be able to repeat the check exactly.

Otherwise, you can’t be sure that quality is always getting better because what aspect of quality you measured before isn’t necessarily the aspect you’re measuring now, and maybe you just got lucky today.

In case of failure, you need a record to do the failure again, or if you can’t reproduce it, go looking for what the real problem is, e.g., a race condition somewhere.

This is part of the Atomic Check pattern as well.

4.       Move setup and teardown operations out-of-line.

Any check might have preliminary setup steps and/or tear-down steps that can be moved out-of-line. The question is this: can any setup or teardown be done asynchronously and in some other process, in some other memory space?

For example, allocating and initializing an environment in which to run a check; that should be done out-of-line.

Allocating, initializing or re-initializing a user identity: that should be done out-of-line.

Any other state for the check, e.g. some file image or system state, that can be done out-of-line and out-of-process, should be done that way, because it means that your checks can run faster and more scalably.

The Precondition Pool pattern of MetaAutomation addresses this.

This page is #2 in a series of six, with the original post here.

Post #1 of 6: If MetaAutomation is too much change for right now…

Most of the patterns and values of MetaAutomation depend on the pattern Atomic Check, and Atomic Check might be a significant change from the way you do quality automation right now.

So, if you’re not ready to take on the big change right now, what can you use of MetaAutomation right now to improve your quality automation?

Following is a series of five blog entries to address some low-cost things you can do to improve the value of your quality automation.

The New Pattern for Quality Automation

There’s a new pattern in town. Actually, it’s a pattern language called MetaAutomation, composed of six patterns, every one based on existing patterns of problem solving in information technology (IT) but with the addition of World Wide Web Consortium (W3C) XML technologies to make them much more powerful in combination.

The existing pattern “Test Automation Framework” (TAF) (Meszaros, “xUnit Test Patterns,” 2007, p. 298) describes existing practices for driving and measuring a software product under development. This works, and it delivers value of course, but it’s limited. With TAF, for example:

·         The customer of the information is people on the QA team. Somebody on the QA team must manually interpret and present the information to the wider software team.

·         The difference between what a human tester and an “automated test” can do is not addressed, causing business risk and opportunity cost.

·         The issues of blocked product quality measurements due to failures and flaky checks is not addressed.

·         The issues of prioritization of measurements is barely addressed.

·         The goal of actionable check failures is not addressed.

MetaAutomation solves all of these problems, and more:

·         MetaAutomation brings a much higher level of visibility and respect to the QA role

·         MetaAutomation brings higher visibility to the developer role

·         MetaAutomation breaks down silo walls with speed and transparency

·         MetaAutomation shows how a single check can drive and measure an Internet of Things (IoT) product on multiple tiers

The costs of MetaAutomation? First, take on some paradigm shifts. These are enumerated on http://MetaAutomation.net.

Second, the quality automation code needs as much care and detail as product code. The team needs at least one person with software development skills to be a part of the QA team or role. There are working solutions available for free on http://MetaAutomation.net.

The diagram below shows how the six patterns of MetaAutomation compose to form the pattern language, and how they fit in the context of the business space. The TAF pattern addresses the QA role, but MetaAutomation delivers value all across the team, even up to the executive suite for Sarbanes-Oxley (SOX) compliance.

For more information on MetaAutomation, see http://MetaAutomation.net. For even more information, see the book MetaAutomation 2nd Edition

DevOps needs MetaAutomation for Quality

DevOps emphasizes collaboration and communication between all members of a software team, including leads, designers, devs, QA and test people. (See the Wikipedia article on DevOps.)

It needs speed and reliability.

A weak quality practice won’t work – regressing and testing can take huge amounts of time, and measuring software quality is more difficult if it’s hampered by outmoded ideas, e.g. “The point of Test is to find bugs,” or “Test automation does what manual testers do, but faster and more reliably … we hope.”

MetaAutomation shows the way to the powerful quality automation that DevOps needs:

1.       The team knows exactly what is measured by the automated checks, and what is not

2.       With self-documenting hierarchical check steps that report “pass,” “fail,” or “blocked” for each step, the correctness of product behavior is clear from the business-facing view and the granular technology-facing view

3.       Since the artifacts (results) of the check runs are pure data, presentation is completely flexible, and analysis is robust

4.       The manual testing role never has to repeat tedious steps, so they can do what they’re good at, which is more fun and satisfying anyway

5.       The QA team delivers quality measurements with speed, accuracy and precision, earning the respect and esteem of their fellow team members

6.       Checks can be run across tiers and devices, with unified artifact results, to make quality measurement simpler, more robust, and less risky

7.       Check runs scale with resources, so results are available almost arbitrarily fast, giving transparency and trust to the rest of the team

Check out http://MetaAutomation.net for more information, and some complete open-source sample implementations with reusable components.

The Paradigm Shifts of MetaAutomation

The current paradigm of functional software quality is in crisis.

“Test automation” is broken. It’s not that it doesn’t bring some value – it does! – but we in the field all know of the many test automation projects that have failed to deliver. The current paradigm is in crisis. It’s time to look again.

The following is extracted from my draft of Chapter 2 of the 2^nd Edition of MetaAutomation, due out in February or March. Interested engineers can also check out http://MetaAutomation.net, and the working samples there that illustrate the radical benefits of MetaAutomation.

Paradigm Shift 1: Automated Verifications, aka Checks, Are Very Different from the Manual Test Role

Traditional model: automated tests are just like manual tests, but faster.

New paradigm: automated verifications are different from manual testing in almost every way.

Paradigm Shift 2: Focusing Checks On Their Unique, Important Capabilities Vastly Increases Business Value

Traditional model: Manual test cases, and automation of same, provide the model and limits for measuring quality.

New paradigm: When quality automation moves away from focusing on making the system under test do stuff and towards measuring and communicating quality, it can provide much greater business value than with current practices.

Paradigm Shift 3: Checks are Bounded in Time, Enabling Structured Artifacts

Traditional model: Checks generate logs, just like any other ongoing process.

New paradigm: Time-bound checks enable robust, structured artifacts with self-documenting steps, vastly increasing the value of the check artifacts.

Paradigm Shift 4: Hierarchical Steps Give Transparency, Robustness, and Flexibility

Traditional model: Check steps are a linear list.

New paradigm: Check steps can be self-documenting and hierarchical, enabling business-facing steps as parents and technology-facing steps as children.

Paradigm Shift 5: The Internet of Things is Distributed, So Measure Quality That Way Too

Traditional model: Automation of a product is limited to one tier for any given check.

New paradigm: Structured data allow any check to manipulate and measure across any number of tiers, enabling end-to-end checks for the Internet of Things.

Paradigm Shift 6: Better Quality Data leads to Quality Power Across the Team and Across the SDLC

Traditional model: Checks described with business-facing terms allow all team members to consume results, but require the QA role to maintain and hide the details of what’s going on.

New paradigm: With hierarchical self-documenting check steps, nothing is hidden and everything is exposed to any team member that wishes to drill down from business-facing parent steps to the child-most steps that describe atomic operations on the product.