Friday, 16 February 2018

How do you decide what to automate?

When you start to think about a new automated test suite, the first conversations you have will focus on what you're going to automate. Whether it's your manager that is requesting automation or you're advocating for it yourself, you need to set a strategy for test coverage before you choose a tool.

There are a lot of factors that contribute to the decision of what to automate. If you try to decide your scope in isolation, you may get it wrong. Here are some prompts to help you think broadly and engage a wide audience.

Product Strategy

The strategy for the product under test can heavily influence the scope of the associated test automation solution.

Imagine that you're part of a team who are releasing a prototype for fast feedback from the market. The next iteration of the product is likely to be significantly different to the prototype that you're working on now. There is probably a strong emphasis on speed to release.

Imagine that you're part of a team who are working on the first release version of a product that is expected to become a flagship for your organisation over the next 5 - 10 years. The product will evolve, but in contrast to the first team it may be at a steadier pace and from a more solid foundation. There is probably a strong emphasis on technical design for future growth.

Imagine that you're part of a team who will deliver the first product in a line of new products that will all use the same technology and software development process. An example may be a program of work that switches legacy applications to a new infrastructure. The goal may be to shift each piece in a like-for-like manner. There is probably a strong emphasis on standard practices and common implementation.

What you automate and the way that you approach automation will vary in each case.

You need to think about, and ask questions about, what's happening at a high-level for your product so that you can align your test automation strategy appropriately. You probably won't invest heavily in framework design for a prototype. Your automation may be shallow, quick and dirty. The same approach would be inappropriate in other contexts.

At a slightly lower level of product strategy, which features in your backlog are most important to the business? Are they also the most important to your customers? There may be a list of features that drive sales and a different set that drive customer loyalty. Your automation may target both areas or focus only on the areas of the application that are most important to one set of stakeholders.

Can you map the upcoming features to your product to determine where change might happen in your code base? This can help you prepare appropriate regression coverage to focus on areas where there is a high amount of change.

Risk Assessment

When testers talk about the benefits of automation, we often focus on the number of defects it finds or the time that we’re saving in development. We describe the benefits in a way that matters to us. This may not be the right language to talk about the benefits of automation with a manager.

In the 2016 World Quality Report the number one reason that managers invest in testing is to protect the corporate image. Testing is a form of reputation insurance. It’s a way to mitigate risk.

When you’re deciding what to automate, you need to think about the management perspective of your underlying purpose and spend your effort in automation in a way that directly considers risk to the organisation. To do this, you need to engage people in a conversation about what those risks are.

There are a lot of resources that can help support you in a conversation about risk. James Bach has a paper titled Heuristic Risk-Based Testing that describes an approach to risk assessment and offers common questions to ask. I've used this as a resource to help target my own conversations about risk.

Both the strategy and the risk conversation are removed from the implementation detail of automation, but they set the scene for what you will do technically and your ongoing relationships with management. These conversations help you to establish credibility and build trust with your stakeholders before you consider the specifics of your test automation solution. Building a strong foundation in these working relationships will make future conversations easier.

How do you consider what to automate at a more technical level?

Objective Criteria

As a general rule, start by automating a set of objective statements about your application that address the highest risk areas.

Objective measures are those that can be assessed without being influenced by personal feelings or opinion. They’re factual. The kind of measure that you can imagine in a checklist, where each item is passed or failed. For example, on a construction site a hard hat must be worn. When you look at a construction worker it is easy to determine whether they are wearing a hat or not.

On the flip side, subjective measures are where we find it difficult to articulate the exact criteria that we’re using to make a decision. Imagine standing in front of a completed construction project and trying to determine whether it should win the “Home of the Year”. Some of the decision making in this process is subjective.

If you don’t know exactly how you’re assessing your product, then a tool is unlikely to know how to assess the product. Automation in a subjective area may be useful to assemble a set of information for a person to evaluate, but it can’t give you a definitive result like an objective check will do.

A common practice to support conversations about objective criteria is the gherkin syntax for creating examples using Given, When, Then. A team that develop these scenarios collaboratively through BDD, or Three Amigos, etc. are likely to end up with a more robust set of automation than a person who identifies scenarios independently.

Repetitive Tasks

Repetition can indicate an opportunity for automation, both repetition in our product code and repetition in our test activities.

In the product, you might have widgets that are reused throughout an application e.g. a paginated table. If you automate a check that assess that widget in one place, you can probably reuse parts of the test code to assess the same widget in other places. This creates confidence in the existing use of the widgets, and offers a quick way to check behaviour when that same widget is used in future.

In our testing, you might perform repetitive data setup, execute the same tests against many active versions of a product, or repeat tests to determine whether your web application can be used on multiple browsers. These situations offer an opportunity to develop automation that can be reused for many scenarios.

By contrast, if we have a product that is unique, or an activity that’s a one-off, then we may not want to automate an evaluation that will only happen once. There are always exceptions, but as a general rule we want to seek out repetition.

There is benefit to collaboration here too, particularly in identifying repetitive process. Your colleagues may perform a slightly different set of tasks to what you do, which could mean that you're unaware of opportunities to automate within their area. Start conversations to discover these differences.

Opportunity Cost

When you automate it’s at the expense of something else that you could be doing. In 1998 Brian Marick wrote a paper titled When should a test be automated?. In his summary at the conclusion of the paper he says:

“The cost of automating a test is best measured by the number of manual tests it prevents you from running and the bugs it will therefore cause you to miss.”

I agree this is an important consideration in deciding what to automate and believe it is often overlooked.

When you decide what to automate, it’s important to discuss what proportion of time you should spend in automating and the opportunity cost of what you're giving up. Agreeing on how long you can spend in developing, maintaining, and monitoring your automation will influence the scope of your solution.

Organisational Change

So far we've considered five factors when deciding what to automate: product strategy, risk, objectivity, repetition, and opportunity cost. All five of these will change.

Product strategy will evolve as the market changes. Risks will appear and disappear. As the features of the product change, what is objective and repetitive in its evaluation will evolve. The time you have available, and your opportunity costs, will shift.

When deciding what to automate, you need to consider the pace and nature of your change.

Is change in your organisation predictable, like the lifecycle of a fish? You know what steps the change will take, and the cadence of it. In 2 weeks, this will happen. In 6 months, that will happen.

Or is change in your organisation unpredictable, like the weather? You might think that rain is coming, but you don’t know exactly how heavy it will be or when it will happen, and actually it might not rain at all.

The pace and nature of change may influence the flexibility of your scope and the adaptability of your implementation. You can decide what to automate now with a view to how regularly you will need to revisit these conversations to keep your solution relevant. In environment with a lot of change, it may be less important to get it right the first time.


Deciding what to automate shouldn’t be a purely technical decision. Reach out for information from across your organisation. Establish credible relationships with management by starting conversations in their language. Talk to your team about objective measures and repetitive tasks. Discuss time and opportunity costs of automation with a wide audience. Consider the pace of change in your environment.

It it important to agree the scope of automation collaboratively.

Collaboration doesn’t mean that you call a meeting to tell people what you think. It isn't a one-way information flow. Allow yourself to be influenced alongside offering your opinion. Make it clear what you believe is suited to automation and what isn’t. Be confident in explaining your rationale, but be open to hearing different perspectives and discovering new information too.

You don't decide what to automate. The decision does not belong to you individually. Instead you lead conversations that create shared agreement. Your team decide what to automate together, and regularly review those choices.

Tuesday, 30 January 2018

A stability strategy for test automation

As part of the continuous integration strategy for one of our products, we run stability builds each night. The purpose is to detect changes in the product or the tests that cause intermittent issues, which can be obscured during the day. Stability builds give us test results against a consistent code base during a period of time that our test environments are not under heavy load.

The stability builds execute a suite of web-based user interface automation against mocked back-end test data. They run to a schedule and, on a good night, we see six successful builds:

The builds do not run sequentially. At 1am and 4am we trigger two builds in close succession. These execute in parallel so that we use more of our Selenium Grid, which can give early warning of problems caused by load and thread contention.

When things are not going well, we rarely see six failed builds. As problems emerge, the stability test result trend starts to look like this:

In a suite of over 250 tests, there might be a handful of failures. The number of failing tests, and the specific tests that fail, will often vary between builds. Sometimes there is an obvious pattern e.g. tests with an image picker dialog. Sometimes there appears to be no common link.

Why don't we catch these problems during the day?

These tests are part of a build pipeline that includes a large unit test suite. In the build that is run during the day, the test result trend is skewed by unit test failures. The developers are actively working on the code and using our continuous integration for fast feedback.

Once the unit tests are successful, intermittent issues in the user interface tests are often resolved in a subsequent build without code changes. This means that the development team are not blocked, once the build executes successfully they can merge their code.

The overnight stability build is a collective conscience for everyone who works on the product. When the build status changes state, a notification is pushed into the shared chat channel:

Each morning someone will look at the failed builds, then share a short comment about their investigation in a thread of conversation spawned from the original notification message. The team decide whether additional investigation is warranted and how the problem might be addressed.

It can be difficult to prioritise technical debt tasks in test automation. The stability build makes problems visible quickly, to a wide audience. It is rare that these failures are systematically neglected. We know from experience that ignoring the problems has a negative impact on cycle time of our development teams. When it becomes part of the culture to repeatedly trigger a build in order to get a clean set of test results, everything slows down and people become frustrated.

If your user interface tests are integrated into your pipeline, you may find value in adopting a similar approach to stability. We see benefits in early detection, raising awareness of automation across a broad audience, and creating shared ownership of issue resolution.

Thursday, 18 January 2018

Three types of coding dojo for test automation

The Test Coaches in my organisation provide support for our test automation frameworks. We create tailored training material, investigate new tools in the market, agree our automation strategy, monitor the stability of our suites, and establish practices that keep our code clean.

A lot of this work is achieved in partnership with the testers. We create opportunities for shared learning experiences. We facilitate agreement of standards and strategy that emerge through conversation. I like using coding dojos to establish these collaborative environments for test automation.

When I run a coding dojo, all the testers of a product gather with a single laptop that is connected to a projector. The group set a clear objective for a test automation task that they would like to complete together. Then everyone participates in the code being written, by contributing verbally and taking a turn at the keyboard. Though we do not adopt the strict principles of a coding dojo as they were originally defined, we operate almost exactly as described in this short video titled 'How to run a coding dojo'.

When using this format for three different types of test automation task - training, refactoring, and discovery - I've observed some interesting patterns in the mechanics of each dojo.

Three types of coding dojo for test automation


Where there are many testers working on a product with multiple types of test automation, individuals may specialize e.g. user interface testing or API testing. It isn't feasible for every person to be an expert in every tool.

Often those who have specialized in one area are curious about others. A coding dojo in this context is about transfer of knowledge to satisfy this curiosity. It also allows those who don't regularly see the code to ask questions about it.

The participants vary in skill from beginner to expert. There may be a cluster of people at each end of the spectrum based on whether they are active in the suite day-to-day.

Training dojo

Communication in this dojo can feel like it has a single direction, from expert to learner. Though everyone participates, it is comfortable for the expert and challenging for the beginner. In supporting the people who are unfamiliar, the experts need to provide a lot of explanation and direction.

This can create quite different individual experiences within the same shared environment. A beginner may feel flooded by new information while an expert may become bored by the slow pace. Even with active participation and rotation of duties, it can be difficult to facilitate this session so that everyone stays engaged.


When many people contribute to the same automation suite, variation can emerge in coding practices through time. Occasionally refactoring is required, particularly where older code has become unstable or unreadable.

A coding dojo in this context is useful to agree the patterns for change. Rather than the scope and nature of refactoring being set by the first individual to tackle a particular type of problem, or dictated by a Test Coach, a group of testers collectively agree on how they would like to shape the code.

Though the skill of the participants will vary, they skew towards expert level. The audience for refactoring work is usually those who are regularly active in the code - perhaps people across different teams who all work with the same product.

Refactoring dojo

Communication in this dojo is convergent. There are usually competing ideas and the purpose of the session is to reach agreement on a single solution. As everyone participates in the conversation, the outcome will often include ideas from many different people.

In this example I've included one beginner tester, who might be someone new to the team or unfamiliar with the code. Where the context is refactoring, these people can become observers. Though they take their turn at the keyboard, the other people in the room become their strong-style pair, which means that "for ideas to reach the computer they must go through someone else's hands".


As our existing suites become out-dated, or as people hear of new tools that they would like to experiment with, there is opportunity for discovery. An individual might explore a little on their own, then a coding dojo is an invitation to others to join the journey.

The participants of this dojo will skew to beginner. The nature of prototyping and experimentation is that nobody knows the answer!

Discovery dojo

Communication in this dojo is divergent, but directed towards a goal. People have different ideas and want to try different things, but all suggestions are in the spirit of learning more about the tool.

The outcome of this dojo is likely to be greater understanding rather than shared understanding. Though we probably won't agree, but we'll all know a little bit more.

For training, refactoring, and discovery, I enjoy the dynamics of a coding dojo format. I would be curious to know how these experiences match your own, or where you've participated in a dojo for test automation work in a different context.

Thursday, 4 January 2018

30 articles for tech leaders written by women

When I was first promoted to a leadership role in tech, I looked for leadership resources that were written by women with advice targeted to a tech environment.

It took some time to discover these articles, which resonated with me and have each contributed to my leadership style in some way. They are written by a variety of women in the US, UK, Europe and New Zealand, many have ties to the software testing community.

This list includes several themes: leadership, communication, learning, inclusion, and recruitment. I would love your recommendations for other articles that could be added.

Why we should care about doing better - Lynne Cazaly
Follow the leader - Marlena Compton
Put your paddle in the air - Lillian Grace
Dealing with surprising human emotions: desk moves - Lara Hogan
You follow the leader because you want to - Kinga Witko
Entering Groups - Esther Derby
Agile Managers: The Essence of Leadership - Johanna Rothman
Recovering from a toxic job - Nat Dudley
Yes, and... - Liz Keogh
Ask vs. Guess cultures - Katherine Wu
"I just can't get her to engage!" - Gnarly Retrospective Problems - Corinna Baldauf
Eight reasons why no one's listening to you - Amy Phillips
Don't argue with sleepwalkers - Fiona Charles
What learning to knit has reminded me about learning - Emily Webber
Effective learning strategies for programmers - Allison Kaptur
Five models for making sense of complex systems - Christina Wodtke
The comfort zone - Christina Ohanian
WTF are you doing? Tell your teams! - Cassandra Leung
We don't do that here - Aja Hammerly
Here's how to wield empathy and data to build an inclusive team - Ciara Trinidad
Tracking compensation and promotion inequity - Lara Hogan
The other side of diversity - Erica Joy
Hiring isn't enough - Catt Small
'Ladies' is gender neutral - Alice Goldfuss
Where does white privilege show up? - Kirstin Hull
Better hiring with less bias - Trish Khoo
1000 different people, the same words - Kieran Snyder

Wednesday, 13 December 2017

Pairing for skill vs. Pairing for confidence

I went to a WeTest leadership breakfast this morning. We run in a Lean Coffee format and today we had a conversation about how to build confidence in people who have learned basic automation skills but seem fearful of applying those skills in their work.

I was fortunate to be sitting in a group with Vicki Hann, a Test Automation Coach, who had a lot of practical suggestions. To build confidence she suggested asking people to:
  • Explain a coding concept to a non-technical team mate
  • Be involved in regular code reviews
  • Practice the same type of coding challenge repeatedly

Then she talked about how she buddies these people within her testing team.

Traditionally when you have someone who is learning you would buddy them with someone who is experienced. You create an environment where the experienced person can transfer their knowledge or skill to the other.

In a situation where the person who is learning has established some basic knowledge and skills, their requirements for a buddy diversify. The types of activities that build confidence can be different to those that teach the material.

Confidence comes from repetition and experimentation in a safe environment. The experienced buddy might not be able to create that space, or the person who is learning may have their own inhibitions about making mistakes in front of their teacher.

Vicki talked about two people in her organisation who are both learning to code. Rather than pairing each person with someone experienced, she paired them with each other. Not day-to-day in the same delivery team, but they regularly work together to build confidence in their newly acquired automation skills.

In their buddy session, each person explains a piece of code that they’ve written to the other. Without an experienced person in the pair, both operate on a level footing. Each person has strengths and weaknesses in their knowledge and skills. They feel safe to make mistakes, correct each other, and explore together when neither know the answer.

I hadn’t considered that there would be a difference in pairing for skill vs. pairing for confidence. In the past, I have attempted to address both learning opportunities in a single pairing by putting the cautious learner with an exuberant mentor. I thought that confidence might be contagious. Sometimes this approach has worked well and others not.

Vicki gave me a new approach to this problem, switching my thinking about confidence from something that is contagious to something that is constructed. I can imagine situations where I’ll want to pair two people who are learning, so that they can build their confidence together. Each person developing a belief in their ability alongside a peer who is going through the same process.

Wednesday, 6 December 2017

Conference Budgets

There has been conversation on Twitter recently about conferences who do not offer speaker compensation. If you haven't been part of this discussion I would encourage you to read Why I Don't Pay to Speak by Cassandra Leung, which provides a detailed summary.

I take an interest in these conversations from two perspectives: I regularly speak at international conferences and I co-organise the annual WeTest conferences for the New Zealand testing community.

As an organiser, the events that I help to run cover all speaker travel and accommodation. We make a special effort to care for our conference speakers and have built a reputation in the international testing community as being an event that is worth presenting at.

WeTest is a not-for-profit company that is entirely driven by volunteers. How do we afford to pay all of our speakers?

Humble Beginnings

Our 2014 WeTest conference was a half-day event in a single city.

We had 80 participants who paid $20 per person. They received a conference t-shirt along with a catered dinner of pizza and drinks.

All of our speakers were local to the city, so there were no travel or accommodation expenses. Our budget was balanced by the support of our primary sponsor, Assurity.

Our total budget for this event was approximately $3,000 where our income and expenses were:

WeTest Budget 2014

Stepping Up

By 2016 we felt that we had built an audience for a more ambitious event. We embarked on a full-day conference tour with the same program running in two cities within New Zealand.

We had 150 participants in each city who paid $150 per person. This was a significant jump in scale from our previous events, so we had to establish a formal scaffold for our organisation. WeTest was registered as a company, we created a dedicated bank account, and launched our own website.

This was also the first year that we invited international speakers. 25% of our speaker line-up, or three out of twelve speakers, traveled to New Zealand from overseas. Covering their travel and accommodation costs significantly altered the dynamics of our budget. Running the conference in two different cities meant that there were travel and accommodation costs for our New Zealand based speakers and organisers too.

Our total budget for this event was approximately $50,000 where our income and expenses were:

WeTest Budget 2016

The Big League

Our 2016 events sold out quickly and we had long waiting lists. To accommodate a larger audience, we grew again in 2017. This meant securing commercial venues, signing contracts, paying deposits, registering for a new level of tax liability and formalising our not-for-profit status.

In 2017 we had around 230 participants in each city. We introduced an earlybird ticket at $150 per person, so that our loyal supporters would not experience a price-hike and we could collect some early revenue to cover upfront costs. Our standard ticket was $250 per person.

40% of our speaker line-up, or four out of ten speakers, traveled to New Zealand from overseas. We incurred similar speaker travel and accommodation expenses to the previous year.

Our total budget for this event was approximately $100,000 where our income and expenses were:

WeTest Budget 2017

To re-iterate, WeTest is a not-for-profit organisation that is volunteer-led. The profit of our 2017 events will be reinvested into the testing community and help us to launch further events in the New Year.

In the discussion about speaker reimbursement we often discuss in the abstract. I hope that these examples provide specific evidence of how a conference might approach speaker reimbursement, whether they are a small community event or a larger endeavour.

At WeTest we have consistently balanced our budget without asking speakers to pay their own way. We are proud of the diverse speaker programs that have been supported by this approach. In 2018 we look forward to continuing to provide a free platform for our speakers to deliver great content.

Sunday, 29 October 2017

Strategies for automated visual regression

In my organisation we have adopted automated visual regression in the test strategy for three of our products. We have made different choices in implementing our frameworks, as we use automated visual regression testing for a slightly different purpose in each team. In this post I introduce the concept of automated visual regression then give some specific examples of how we use it.

What is visual regression?

The appearance of a web application is usually defined by a cascading style sheet (CSS) file. Your product might use a different flavour of CSS like SCSS, SASS, or LESS. They all describe the format and layout of your web-based user interface.

When you make a change to your product, you are likely change how it looks. You might intentionally be working on a design task e.g. fixing the display of a modal dialog. Or you might be working on a piece of functionality that is driven through the user interface, which means that you need to edit the content of a screen e.g. adding a nickname field to a bank account. In both cases you probably need to edit the underlying style sheet.

A problem can arise when the piece of the style sheet that you are editing is used in more than one place within the product, which is often the case. The change that you make will look great in the particular screen that you're working in, but cause a problem in another screen in another area of the application. We call these types of problems visual regression.

It is not always easy to determine where these regression issues might appear because style sheets are applied in a cascade. An element on your page may inherit a number of display properties from parent elements. Imagine a blue button with a label in Arial font where the colour of the button is defined for that element but the font of the button label is defined for the page as a whole. Changing the font of that button by editing the parent definition could have far-reaching consequences.

We use automated visual regression to quickly identify differences in the appearance of our product. We compare a snapshot taken prior to our change with a snapshot taken after our change, then highlight the differences between the two. A person can look through the results of these image comparisons to determine what is expected and what is a problem.

Manufactured example to illustrate image comparison

Team One Strategy

The first team to adopt automated visual regression in my organisation was our public website, a product with a constantly evolving user interface.

The test automation strategy for this product includes a number of targeted suites. There are functional tests written in Selenium that examine the application forms, calculators, and other tools that require user interaction. There are API tests that check the integration of our website to other systems. We have a good level of coverage for the behaviour of the product.

Historically, none of our suites specifically tested the appearance of the product. The testers in the team found it frustrating to repetitively tour the site, in different browsers, to try to detect unexpected changes in how the website looked. Inattentional blindness meant that problems were missed.

The team created a list of the most popular pages in the site based on our analytics. This list was extended, so that it included at least one page within each major section of the website, to define an application tour for the automated suite to capture screenshots for comparison.

The automated visual regression framework was implemented to complete this tour of the application against a configurable list of browsers. It launches BrowserStack, which means that it is able to capture images against desktop, tablet, and mobile browsers. The automated checks replace a large proportion of the cross-browser regression testing that the testers were performing themselves.

The team primarily use the suite at release, though occasionally make use of it during the development process. The tool captures a set of baseline images from the existing production version of the product and compares these to images from the release candidate. The image comparison is made at a page level: a pixel-by-pixel comparison with a fuzz tolerance for small changes.

Team Two Strategy

The second team to adopt automated visual regression was our UI toolkit team. This team develop a set of reusable user interface components so that all of our products have a consistent appearance. The nature of their product means that display problems are important. Even a difference of a single pixel can be significant.

The tester in the this team made automated visual regression the primary focus of their test strategy. They explored the solution that the first team had created, but decided to implement their own framework in a different way.

In our toolkit product, we have pages that display a component in different states e.g. the button page has examples of a normal button, a disabled button, a button that is being hovered on, etc. Rather than comparing the page as a whole with a fuzz tolerance, this tester implemented an exact comparison at a component level. This meant that the tests were targeted and would fail with a specific message e.g. the appearance of the disabled button has changed.

The initial focus for this framework was getting component level coverage across the entire toolkit with execution in a single browser. This suite was intended to run after every change, not just at release. The tester also spent some time refining the reporting for the suite, to usefully abstract the volume of image comparisons being undertaken.

Once the tests were reliable and the reporting succinct, the tester extended the framework to run against different browsers. Cross-browser capability was a lower priority than in the Team One.

Team Three Strategy

A third team are starting to integrate automated visual regression into their test strategy. They work on one of our authenticated banking channels, a relatively large application with a lot of different features.

This product has mature functional test automation. There are two suites that execute through the user interface: a large suite with mocked back-end functionality and a small suite that exercises the entire application stack.

For this product, implementing automated visual regression for a simple application tour is not enough. We want to examine the appearance of the application through different workflows, not just check the display of static content. Rather than repeating the coverage provided by the existing large test suite, the team extended the framework to add an automated visual regression test.

This suite is still under development and, of the three solutions, it is the largest, the slowest, and requires the most intervention by people to execute. The team created a configuration option to switch on screenshot collection as part of the existing functional tests. This generates a set of images that will either represent the 'before' or the 'after' state, depending on which version of the application is under test.

Separate to the collection of images is a comparison program that takes the two sets of screenshots and determines where there are differences. The large suite of functional tests means that there are many images to compare, so the developers came up with an innovative approach to perform these comparisons quickly. They first compare a hash string of the image then, in the event that these differ, they perform the pixel-by-pixel comparison to determine what has changed.

In this team the automated visual regression has a fractured implementation. The collection and comparison happen separately. The focus remains on a single browser and the team continue to iterate their solution, particularly by improving the accuracy and aesthetics of their reporting.


We use automated visual regression to quickly detect changes in the appearance of our product. Different products will require different strategies, because we are looking to address different types of risk with this tool.

The three examples that I've provided, from real teams in my organisation, illustrate this variety in approach. We use visual regression to target:
  • cross-browser testing, 
  • specific user interface components, and
  • consistent display of functional workflows. 
As with any test automation, if you're looking to implement automated visual regression consider the problem that you're trying to solve and target your framework to that risk.