Category

QA

Category

As many yoga instructors do, they encouraged students to find balance. It’s an effective bit of advice in that it puts the onus on the practitioner. It’s also a popular concept for software testing, as industry experts often recommend that software teams find the balance between automation and manual testing practices. Another similarity is the trajectory of their adoption rates. One does not dive into yoga, but slowly adopts it over time until it becomes a part of their daily routine. The same goes for test automation: you can’t expect to start automating everything from scratch. Good test automation is the culmination of work over time.

Why not all manual?

Manual testing is fine, and was once the status quo, but with high adoption of Agile and continuous delivery methodologies, it just doesn’t scale. For software development, every enhancement or new feature that’s rolled out for an app must have a corresponding set of tests to ensure the new functionality works and does not break the code from the previous version. In this regard, to check all of the file directories, database, workflows, integrations, rules and logic manually would be extremely time consuming.

For companies looking to improve time to market and increase test coverage, automation provides significant advantages over manual testing. A typical environment can host thousands of test cases of varying complexity to be executed effortlessly and nonstop. As a result, automation dwindles the time required to perform mundane, repetitive test cases from days to hours and even minutes if you run in parallel. That inherent velocity increase is what attracts teams to automated testing.

Why not all automation?

The benefits of automation are obvious. Automation provides the ability to quickly react to ever-changing business requirements and generate new tests continuously. Is it reasonable then to assume that the more you automate the more benefits you reap? Then why not just automate everything?

The short answer, its very difficult. According to the World Quality Report 2017-18 Ninth Edition, these are the top challenges enterprise are facing in regards  to test automation.

Traditionally you would automate only once your platform is stable and the user flow is pretty consistent. This was primarily due to the amount of time it took to write a test, sometimes hours and even days. Plus the amount of time it took to update and maintain tests as code changed. The ROI was not justified up to the point your user flow is stable.

Feedback from our customers such it has changed. We recently heard a number of our clients who automate everything and even as early as wireframes are ready. We reduced authoring time by about 70% of the time and maintenance time by about 90%. Creating a user flow now takes a few minutes with Testim and so is updating the user flow. Reducing the time per test to a few minutes instead of hours and days makes it much more affordable to automate, providing immediate ROI from feedback loop benefits.

So should you strive to 100% automation? Well no but you can get to 80%-90% which was unheard of until recently. There are still scenarios that only humans can do such as Face ID. There are also other aspects of your Software Development Lifecycle (SDLC) that automation is dependent on.

Summary

There is an ongoing struggle to keep up with the rate dictated customer pressure and competition to produce on a continuous basis. And not just produce for production’s sake, but a quality product that’s been thoroughly tested. That’s why we created Testim…

To help software teams:

  • Increase coverage by 30% in 3 months
  • Reduce number of missed bugs by 37%   
  • Increase team productivity by 62%
  • Reduce regression testing costs by 80%  
  • Speedup testing schedules by 3 months

Sources

  1. https://techbeacon.com/world-quality-report-2017-18-state-qa-testing
  2. https://dzone.com/articles/automated-vs-manual-testing-how-to-find-the-right
  3. https://blog.qasource.com/the-right-balance-of-automated-vs.-manual-testing-service
  4. http://sdtimes.com/software-testing-is-all-about-automation/#sthash.5Idkps2O.dpuf
  5. http://sdtimes.com/report-test-automation-increasing/#sthash.HlHQmD2l.dpuf
  6. https://appdevelopermagazine.com/4935/2017/2/8/test-automation-usage-on-the-rise/
  7. https://techbeacon.com/devops-automation-best-practices-how-much-too-much

Do you how much quality costs? Have you ever calculated it? If you do, you’ll learn that you spend thousands of dollars and hours of productivity on every bug. From the cost of labor, to the cost of deployment, to the cost of finding the bugs in production, no bug fix is free.

Many companies and academics, from IBM to Microsoft to large banks have performed studies to quantify the cost of finding bugs. It’s been well known for many years that the costs go up the closer to production you go, but the actual numbers are a bit staggering.Their findings show the dramatic financial difference between discovering a bug in the wild, and finding it during development.

  • Fixing a bug in production – Very expensive. These take time to fix, and while repairs take place, your business experiences pain. Your teams are working around the clock to fix something that also requires your developer to rework something they wrote months ago and forgot about.
  • Fixing a bug in QA – Somewhat expensive. The QA person has to report the bug. A senior manager has to determine who to be assigned to this. The developer has to step out of his daily tasks, sometimes impacting the current sprint, attend to the bug, reproduce it, and return to edit some code from the last sprint, which the developer has likely already begun to forget about.
  • Fixing a bug in development – Least expensive. takes a few minutes for the developer to fix. If you have an immediate feedback loop, your developer will get feedback minutes after pushing code. If a test fails, it is still within the current sprint and the developer should recognize the code and its implications as everything is still fresh in their mind. The bug didn’t escalate and the likelihood of on time release is much higher.

Shift left is the term coined for a software development cycle that includes a constant and immediate feedback loop. You may have heard stories about how Facebook has put a billion dollars worth of engineering into creating this feedback loop. The company even wrote a PHP-to-C compiler called HipHop to speed up the loading of its test environments.

If Facebook is willing to spend a billion dollars to get that feedback loop, you can spend a few weeks ensuring your own loop is as fast and reliable as possible. This means fast builds, fast test batteries, and short amounts of time between developer compiling, and developer testing.

This requires certain infrastructure that we’ll get to in a future post but organizations that have deployed it, have seen decrease in cost of quality and increase in on-time delivery. You may even have to disassemble your build pipeline to replace a few slower moving items.

Your goal, here, should be one minute or less between build and test. This may seem impossible, or it may already be the way things work for your team. This can require a lot of work, or a little depending on the existing systems. No matter the environment, there is one fundamental truth for all software development: the faster the developer can see their results in a running binary, the faster they can fix problems and the lower the cost of overall quality.

Want to know how to shift left? Stay tuned.

Sources:

 

Google recently announced that they released Puppeteer, a Node library which provides an API to control headless Chrome. Within 24hrs they received great feedback from the community;

  • 6,685 stars on Github
  • 2.2K likes and 1.2K shares on Twitter

So why should we care? Here’s a snippet from its GitHub documentation:

Puppeteer’s GitHub documentation Q&A

In Google’s own words, there isn’t much difference from Selenium.

The awesomeness of Selenium is that they convinced ALL browser vendors to support the same low level API (and this took years! try convincing Apple, MS, and Google to work together), and even implemented this API in more than 10 languages (including JS).

Most of Puppeteer’s API is very similar to Selenium (or the Webdriver.io/NightwatchJS alternatives) e.g.;

  • Google’s launch() method vs. Selenium’s init()
  • goto() vs. to url()
  • close() vs. end()
  • type() vs. setValue()
  • click() even stayed the same

Google could have picked the same Selenium API and contributed the changes to the Selenium repo. But the biggest issue isn’t the API. It’s splitting the community and not contributing to the Selenium code base. With Google’s resources and talented developers, they could have contributed to the Selenium project, which currently has a few amazing volunteers, supporting this framework, and some parts are closing for lack of resources.

Selenium is known to be relatively slow compared to operating directly on the browser. This is caused by its architecture, which requires a Selenium Grid or Selenium Standalone server, which acts as a proxy (and even just starting it takes a while). This architecture is great when your tests need to run on multiple browsers in parallel, but you experience the overhead when working with a single browser. By helping the Selenium community speed this up, even if it was just for Chrome, would have been more beneficial than trying to create their own (Chrome only) solution.

Puppeteer is a step in the right direction. Google is an innovative company that pushes the web forward with great ideas and specs, amazing developer tools, and now it seems to help improve UI test automation, which we all know is extremely challenging.

Standardization leads to innovation. With Selenium, not only would you be able to run those tests on other browsers, but the entire industry is relying on those standards to create additional (commercialized) products. For example; Testim.io, Saucelabs, BrowserStack, Applitools, Cross Browser Testing, and the list goes on and on.

I would love to hear your opinion about Puppeteer and Selenium.

Cheers,
Oren Rubin
CEO, Testim.io

It’s inevitable. Agile has matured and in addition to speed, now the new set of challenges are about accountability and proving its worth. Just as with other areas of the business, Agile must also answer to traceability, auditability, and compliance. After all, what good is a methodology that delivers fast, but ultimately fails to deliver more value?  Many organizations are now demanding it and teams new to Agile are starting out in an uphill battle with risk-averse management and globally dispersed project teams, in addition to technical debt.  Even though the executive team made the decision to adopt Agile practices, that decision is coming with its own set of expectations that must be met. And it’s qualitative metrics that will be looked to to satisfy this expectation.

So which metrics should you put in place? Is it more effective to track activity within Agile tools, such as JIRA? Or is is better to track metrics within the software itself? The important thing to realize, though, is what’s really being asked: “is Technology actually improving its impact on the business in a tangible way. Or said another way, as phrased by the Kanban community, is Technology  IT becoming more fit for purpose? Answering this question, of course, requires a clear understanding of what is that the Business expects from its interactions with IT.

To ensure metrics remain relevant, they should be inspected and potentially evolve to ensure they still align with the organization’s short and long term goals. Certain metrics may be created to align with the organization’s specific agile maturity phase and as an organization moves from one phase to another, certain metrics may no longer be relevant. Inspection and adaption will ensure metrics align with the specific goals.

Measure the amount of working software value that reaches customers hands, such as “percentage of story points completed within a sprint”. Velocity trends (not absolute values) are also helpful. Although using Story Points is a crude way of doing this, much better to also estimate the business value of each story (so each story gets two estimates, one for complexity and one for value) then you can measure the value produced by the team easily. The tough part about value is – it is not an easy thing to assign the value to a story. In some circumstances, value cannot be a KPI for IT projects because (1) business value is subjective especially if business value points are used; (2) some projects have no direct way to calculate business value (think regulatory projects that must be done to comply with regulations); and (3) Business value varies too much from project to project based on the perceived business benefit making it hard to measure objectively.

KPIs

That said, KPIs to track during a transformation to Agile include those that fall under the categories of time, quality, and effort. Instead of measuring traditional key performance indicators (KPIs) like profitability or transactions per hour, therefore, executives must shift their focus to how well the enterprise is able to deal with changing customer preferences and a shifting competitive landscape.

Quality

In terms of quality, nothing measures a product’s value or worth better than ROI. The trick is to make sure the return is defined clearly and is measureable. The most straight-forward way to do that is to tie it to a number value: sales, downloads, new members, positive comments, social media likes, etc.

The number of defects is a simple metric to track that speaks loads about not only the quality of the product, but also team dynamics and effectiveness. Good metrics to track for defects include, the number of defects found during development, after release, or by customers or people outside of the team. It can also be insightful to track the number of defects that are carried over to a future release, the number of support tickets, and the percentage of manual vs automated test coverage.

Effort

Sprints are fundamental to Agile iterations. KPIs, therefore, become fundamental to sprints. The design phase of sprints is initial to the development process and, while tweaked as needed to remain focused on objectives, is to remain in place unless proved to be unreliable in achieving objectives.

Velocity tracks the number of user story points completed per sprint. It’s effectiveness to depict anything of value outside of the technology team is debateable. It’s often a misunderstood metric because it’s confused with a team’s productivity. It’s not uncommon for executive management to demand a higher number of story points be completed per sprint in an effort to increase speed to market. However, more often than not this results in a lower quality product. Nonetheless, it can be useful if it’s understood and reported on as an effort-based metric instead of time or quality.

When measuring Velocity, it’s important to keep in mind that different project teams will have different velocities. Each team’s velocity is unique. If team A has a velocity of 50 and team B has a velocity of 75, it doesn’t mean that team B has higher throughput. Since each team’s estimation culture is unique, their velocity will be as well. Resist the temptation to compare velocity across teams. Measure the level of effort and output of work based on each team’s unique interpretation of story points. 

Time

Scrum teams organize development into time-boxed sprints. At the outset of the sprint, the team forecasts how much work they can complete during a sprint. A sprint burndown report then tracks the completion of work throughout the sprint. The x-axis represents time, and the y-axis refers to the amount of work left to complete, measured in either story points or hours. The goal is to have all the forecast work completed by the end of the sprint.

A team that consistently meets its forecast is a compelling advertisement for agile in their organization. But don’t let that tempt you to fudge the numbers by declaring an item complete before it really is. It may look good in the short term, but in the long run only hampers learning and improvement. 

SUMMARY

KPIs are vital to the strategic goals of an organization as in addition to revealing whether the direction of project is on-course, they assist in informing key business decisions. Metrics are just one part in building a team’s culture. They give quantitative insight into the team’s performance and provide measurable goals for the team. While they’re important, don’t get obsessed. Listening to the team’s feedback during retrospectives is equally important in growing trust across the team, quality in the product, and development speed through the release process. Use

SOURCES

  1. Banking on Kanban
  2. https://www.atlassian.com/agile/metrics

 

Testing has been in the spotlight of software development, as organizations continually seek to optimize development cycles. Continuous Delivery and its promises of frequent releases, even as often as hourly, is a big factor driving executives  to find ways  to shave time off of any eligible technical processes. As enterprises embark  on their DevOps transformational journeys, delivering at lightning speed without compromising on quality takes more than cultural changes and process improvements. Test automation is key in helping project teams write and manage the smallest number of test conditions, but get the maximum coverage.

The case for automation: If the scenario is highly quantitative, technology is superior to humans. That’s clearly the case in the current technology landscape, where according to the 2015 OpenSignal report, massive fragmentation has resulted in over 24,000 distinct Android devices that all run on different versions of the Android OS, plus the several variations of iOS devices currently in use. Web is no different. It’s impossible to test every scenario manually, so automation must be leveraged. But therein lies the main point, that automated testing is a tool to be leveraged as needed instead of relied upon to dictate an entire testing strategy.Although logic would deem that if automating a few test scenarios yield fast results, that automating any and every eligible scenario will shorten the test cycle even more; but alas,  that’s usually not how it goes. Automation endeavors take a good deal of planning and forethought in order to add value at the anticipated level. More often than not the overhead of maintenance, especially in CI/CD environments where tests are automatically triggered and one needs to analyze the reports and fix locators, a task that , could  take hours.  This won’t work for organizations who are truly DevOps.  If full stack developers are going to be fully responsible to take the code to production, then they will need tools to automate the process through infrastructure, testing, deployment and monitoring.  This continuous framework enables  weekly , daily and hourly releases.  Leading DevOps organizations like Netflix and Amazon are said to deploy hundreds to thousands times per day.

What’s more, studies reveal that a high percentage of projects utilizing automated testing fall short of the anticipated ROI or fail altogether. These transformations fall short due to their duration, ramp up time, skill-set gap and maintenance overhead.  If the benefits of automated testing aren’t significant enough to mitigate risk then speedy releases could become more of a liability than an asset.

There are varying levels of automation just as there are companies of all different shapes and sizes. Instead of one or the other, it’s more fitting that automated and manual testing are looked at as complementary. Agile and DevOps have created new testing challenges for QA professionals who must meet the requirements for rapid delivery cycles and automated delivery pipelines.

DEMANDS OF CONTINUOUS TESTING

Testing used to have the luxury of having its own phase or at least a set timeframe to stabilize new code prior to pushing to production. No longer. In the era of DevOps, QA must adopt a truly agile mindset and continually be prepared to shift focus to testing new features and functionality as they become available – that could be weekly, daily, or hourly.

From web, mobile and connected devices to IoT, a quick inventory of current technology will reveal a multitude of different devices and literally thousands of potential ways that these technologies can be combined. Across networks, apps, operating systems, browsers and API libraries, the number of  combinations with each requiring its own verification and testing support.  As the push towards Devops continues, the upward trend towards continuous testing is inevitable. As a result, testers and/or developers will need to deliver in brief and rapid iterations.

Beyond automation, what does this look like? QA, Engineering, or whomever is responsible for testing will need to shift left and engage in rounding out user story acceptance criteria to ensure accuracy and completion. In addition, active participation in sprint plannings will help to ensure that user stories are truly decoupled and independent. Or if dependencies are necessary, confirming that story precursors and relationships are in-place. Partnership with Product Owners to confirm that the scope for a given sprint or iteration is realistic.

And finally in support of shifting left, collaboration among Dev and Ops to ensure the necessary support in the event that a code check-in causes a bunch of automated tests to fail. The culprit for these tests will need to be checked manually so it’s important that QA is prepared so as not to pose a blocker for the release. All of these activities is largely what comprises testing outside of automation. It’s a broader role than the traditional QA Specialist that requires initiative and the willingness to embrace an always-on mentality that’s necessary to support DevOps efforts.

SUMMARY

Software development practices have changed and the change brings with it new challenges for the testing arena. The growing need for test automation in addition to the profusion of new testing scenarios will constantly evolve the role of testing and QA.  Yes, the world is changing for testers—in a good way. The days of exhaustive manual testing are certainly numbered, and automated testing is an essential part of the delivery pipeline. But testing has never been more important.

SOURCES

  1. IT Revoloution DevOps Guide
  2. World Quality Report 2016-17
  3. Open Signal: Android Fragmentation

 

Introduction
We work hard to improve the functionality and usability of our platform, constantly adding new features. Our sprints are weekly with minor updates being released sometimes every day, so a lot is added over the course of a month. We share updates by email and social media but wanted to provide a recap of the latest enhancements, summarizing the big and small things we delivered to improve your success with the product. We’d love to hear your feedback.

Test History

Is past performance an indicator of future success? The folks on Wall Street don’t think so… But we do! This is why we are pleased to offer the new Test History feature in Testim.  


The new Test History feature allows users to see slide and dice test results to get more actionable insights.

Why should I care?
This gives users much more insight from the test results. Quickly analyze how many tests ran during the selected time frame, how many succeeded and the average duration of each run, how your tests performs across multiple environments and whether certain environments or scenarios consistently fail. This allows project team to see improvement trends across scenarios or environments over time. Click here to learn more.

What’s included?
Test History allows you to filter based on the following parameters:

  • Run time
  • Specific tests / All tests
  • Last X runs
  • Status
  • Browser

Create Dependencies Between Tests
This new capability allows users to create different aspects of dependencies between tests. As a best practice, we recommend to keep the tests as isolated as possible. However we recognize that sometimes you need the ability to create dependencies between tests.

Why should I care?
Now users can create a logical sequence of tests. By working closely with our customers, we’ve seen project teams required to set a sequence of activities. A general testing framework does not allow you to create a sequence, forcing users to create one long test which may result in performance issues and failures. By creating dependencies during test plans you can create shorter discrete actions, order them in sequence and set dependencies and share data between tests. Click here to learn more.

Setting up Cookies
Cookies is a reserved parameter name for specifying a set of cookies you would like Testim to set for your application.  You can set cookies for each browser session before the test is started or before the page is even loaded. Use cookies to set browser data to be available for your tests. The cookies parameter is set as an array, where each cookie object needs to contain a name and value properties.

Why should I care?
Websites use cookies to personalize user experience. By using this feature, you can test different types of personalized experiences. Click here to learn more.

Improved Scrolling
More flexible way of scrolling through your page.

Why should I care?
Traditionally scrolling in your test automation would be set by pixels so you would test your script to skip 60 pixels. Offering a more flexible scrolling ability makes your tests more adaptive. An  example is to scroll to the element, mouse wheel. Click here to learn more.

How do I get my hands on these hot new features?
Customers have access to these feature now, check it out and let us know what you think. If you’re not a customer, test drive the new features by signing up for a free trial to experience code and maintenance free test automation.

In a world gone mad, where customers will access your application through any damn browser they want, how can you meet their expectations that your app will work perfectly whether it’s running on their phone, their laptop, their tablet, or even their internet enabled refrigerator? Cross-browser testing is essential to ensuring your customer has a consistent user experience, no matter how they access your app. But is just not realistic to manually test all the potential combinations of browsers and systems out there.

However, over the last few years there are a number of vendors who have developed different approaches to cross-browser testing. We have assembled a short list of the 12 most interesting. Each has a slightly different set of capabilities, and a different price level too. Some support mobile, some don’t. Some have security built in, and some do not. Selenium support varies as well.

Most, like Testim.io, have free trials, so you can evaluate which one is the right solution for you.

How to Choose a Provider

There are a number of attributes you should consider when choosing a grid provider. These include:

  • Coverage of browsers, versions and operating systems.
  • Stability – a flaky grid will result in test failure, leading to running those tests again at best and developers spending time figuring out why the test failed at worse. A flaky grid will result in valuable time being lost.
  • Enterprise grade – If your app is internal to the organization you will need tunneling and potentially VPN, encryption or other other security requirements as mandated by your CISO. Not all cross browser testing providers support that.
  • Integration – No organization will change its tool-set due to a grid. Your team is likely using Git, Jira, Slack, Jenkins and/or other tools. Integrating into those tools to trigger your tests and read through the results is essential. Without it, you’ll either have to change the process or end up spending your budget on a tool that will be useless.
  • Features – Do you need screenshots? Single sign-on?
  • Mobile – If you are looking to test your mobile app or web you need to decide whether you settle for emulators or need real devices. Then you have to analyze the variety of devices being offered, including geographies, mobile operators, OS versions etc. Many times you have to drill down to the set of actions the grid supports. Does your app use location sensors? Camera? Fingerprints? Not all grid providers support these actions.

Capability Grid

Cross browser tools
Cross browser tools – Comparison

Unit Tests are in the house!
I’m a big advocate for TDD (Test Driven Development). Research shows TDD has a high correlation to quality code, as it prevents bugs from forming in the first place. Also, the cost of authoring tests and fixing bugs is lower

This is true for the back-end. As for the front-end, although MVC and MVVM based frameworks are doing a great job providing easier tools writing unit testing, we still see low adoption. Some claim it stems from frontend comprising mostly glue-code, and some say it’s harder to write test DOM and event (asynchronous) code.

The need for frontend automation rises
Nowadays, a huge part of any project resides in the frontend. Not only did past challenges remain, the plot thickens as we have more environments (e.g. mobile and tablet), more Operating Systems (Window & Mac are both popular), and more browsers to support (IE11, Chrome, Safari, Firefox, Opera, Edge, not to mention old versions of IE.. may it rest in peace already).

Percentage of core logic – backend vs. frontend

Since more R&D teams shifted to agile, QA has become the bottleneck. A big part of being agile is the frequent releases. When a six month project comprises a three week testing cycle it’s one thing, but when a team wants to deploy weekly or even daily without bugs, QA is challenged with shrinking that to a cycle that takes 1-2 hours. The feedback to the developer should be instantaneous, as research show that longer the feedback cycle is, the longer it takes to fix the problem.

Why testing frontend is difficult
With the move to frontend, high maintenance overhead resurfaced. The maintenance for frontend test automation was as high as 50% twenty years ago. It has only improved marginally in recent years and is still as high as 30%.

To mimic a user interaction, you must first locate the element you wish to act upon. There are two major approaches to locate an element:

  • Visual based – look at the pixels
    Frameworks such as Sikuli use that technique, but it’s super fragile, as render per OS/browser/display-adapter will generate different pixels. Some display adapters are non-deterministic in nature. To have stable tests you must be a computer vision guru.
  • Object based – look at the object hierarchy (DOM).
    Frameworks such as Selenium spread like fire. As opposed to twenty years ago, better software design pattern emerged (such as the Page Object) which helped separate the tests’ business logic from the implementation, and urged reuse.

Nonetheless, both approaches result in flaky and unstable tests. They require high maintenance. or every UI (code) change, a test change is required as well (as both rely on the UI). It’s easier for those practicing the shift left paradigm, where quality is inherent in the development cycle, as finding the cause is easier, but they suffer from the same amount of failures. For those with a QA team, it’s still catastrophic. As almost every morning starts with understanding why tests fail and whether it’s a bug or merely a code change.

Crowdsourcing

Crowdsourcing

For End-to-End testing, some companies turn to crowdsourcing to alleviate the pain. This works great for regression tests which require no prior knowledge of the app. The required ramp up is just to write down the instructions in plain English. The big advantage is the low cost of people willing to click all day long. The problem arises when one tries to implement the shift left paradigm. This requires testing not only in the main branch, but also in all dozens of feature branches. Not only does this makes it not cost effective, but developers are custom to get feedback in seconds or minutes, not hours.
Image a developer promoting a hotfix to be released to fix a critical bug yet having to wait for someone in the other side of the world to wake up and test the new version.
Communication is also challenging at the developer and tester have no experience working together.
As for exploratory testing, crowdsourcing still great. It allows one to test different devices and different networks from all around the world. The rose of the garden is the seductive business model, paying only for proven bugs, comprising a thorn where every little bug is reported and the devs find themselves trying to prove that some of them are not real bugs. Until today, crowdsourcing does not provide a sustainable solution when shifting to continuous delivery.

As mentioned in the previous post the transition between backend and front-end has been a major transformation.

During the mainframe era, most code resided on the server, moving to the client when personal computers came into play. It shifted back to the servers with the web, and in the last five years, we’re seeing code shifting back to the client side as Single Page Applications (SPA) become more popular (and I assume most of you already heard of Angular or React or Ember or Vue and list goes bigger every day).

For back-end testing, most find API testing sufficient as it tests with high fidelity most of the server code. Unit Tests weren’t popular until recent years

As for front-end testing, little code resided on the client side, and API (backend) testing covered most of the app’s code. Many companies historically relied on manual testing. Automation was expensive due to 5 main reasons:

  1. Highly trained people – they are developers in any way: They write complicated code; They spring VMs and browsers on and off and deal with large scale when it comes to performance testing.
  2. Finding great developers is hard. Finding great developers who want to write test automation for a living has the same chances of Shaquille O’Neal not missing a single free throw in ten consecutive shots. 40% of a test automation person time is spent on authoring tests (1).
  3. High maintenance – In the agile world, UI changes are frequent, This implies that changing the UI requires changes in the UI-tests. 30% of a test automation person time is spent on maintaining tests (1).
  4. Long ramp-up – it usually takes weeks or months until you have a dozen of tests.
  5. Few environments. The OS and browsers were not as dispersed as today. Windows ruled the Desktop world, and IE had 95% market share.

Record/Playback

Many platforms tried to alleviate slow authoring and the required dev skills by recording the user’s behavior and creating a test, with the ability to play it back again and again. Unfortunately, those tests required massive maintenance, making the total ROI even lower than with dedicated developers.

Visual Validation

Visual Validation – an image is worth 1000 validations

The complexity of the UI, as opposed to API testing, crosses more than functional testing (the shown data) but also includes visual validation, as visual bugs are not merely cosmetic bugs. They impact usability and user experience. As UI validations were usually associated with E2E testing, most attempts were to take screenshots of the entire page as a validation (as opposed to a small component). Those attempts suffered from the next set of challenges:

  1. Stability – Computers’ display adapter are non-deterministic by nature, and rendering the same page twice might result in different images. The main diffs stem mostly from anti-aliasing and sub-pixels shifting. The human eyes won’t notice, but a simple pixel comparison might result in a  5% difference.
  2. Maintenance – Changing a single component which is located across pages (e.g. logo) can imply that hundreds of baseline images are not incorrect and has to be reviewed.
  3. Storage – Maintaining huge volumes of large screenshots in a non-lossy format were something heavy to any version control repository, specifically the well adopted Git.

Visual validation became scarce until recently.
We’ll mention some solutions in the next blog post, stay tuned!

[1] Source: http://www.softwaretestingnews.co.uk/res/14072016/TEST_Benchmark-Report-2016.pdf

 

I’ve been a software developer for many years. I’ve seen two major transformations: one is the transition between frontend and backend code. The other is in release cycles and speed.

Frontend-backend code – Those working many years in software development remember the eras of shifting code back and forth, from the server to the client and back. During the days of the mainframe, most code resided on the server, and moved to the client with the emergence of personal computers. It shifted back to the servers with the transition to web. In the last five years, we’re seeing code shifting back to the client side as Single Page Applications (SPA) become more popular (and I assume most of you have already heard of AngularJS or ReactJS or Ember.js or Vue.js, and the list goes bigger every day) as well as mobile.

Release cycles – In recent years we have seen a transition from waterfall development to agile. Waterfall cycles were long (e.g. Microsoft releasing a new version of Windows every 2 years), and QA teams would spend months manually testing the software. The short cycles in agile drive the need for automation and Test Driven Development (TDD). The road to automation is still bumpy so some organizations only test the backend via API, while most heavily rely on manual testing. Few claim to have sufficient coverage (both client and server) to release a version knowing it’s completely safe (AKA Continuous Deployment). Lack of proper coverage is risky given the renaissance of code shifting to the frontend and the focus on user experience.

The latter is the outcome of legacy tools for functional testing that haven’t taken advantage of technological evolution: improved computing power at a lower cost, cloud infrastructure & software as a service, real-time big data processing in microseconds, deep user analytics, and behavioral flows. Combining those technologies and thinking about quality in a different way can help us leapfrog to a future where tests are automatically created with every new feature. Reducing investment in testing yet improving quality and user experience is not a myth. If driving can be autonomous – so can tests. Over the next couple of blog posts I’ll describe the evolution of software development and quality assurance and why it leads to autonomous testing.

Be smart & save time...
Simply automate.

Get Started Free