Mobile, Cross Browser Testing, DevOps and Continuous Testing Trends and Projections for 2018

As we about to wrap out 2017, It’s the right time to get ready to what’s expected next year in the mobile, cross-browser testing and DevOps landscape.

To categorize this post, I will divide the trends into the following buckets (there may be few more points, but I believe the below are the most significant ones)

  • DevOps and Test Automation on Steroids Will Become Key for Digital Winners
  • Artificial Intelligence (AI) and Machine Learning (ML)/ Tools alignment as part of Smarter Testing throughout the pipeline
  • IOT and Digital Transformation Moving to Prime Time

 

DevOps and Automation on Steroids

If in 2017, we’ve seen the tremendous adoption of more agile methods, ATDD, BDD and organizations leaving legacy tools behind in favor of faster and more reliable and agile-ready testing tools, such that can fit the entire continuous testing efforts whether they’re done by Dev, BA, Test or Ops.

In 2018, we will see the above growing to a higher scale, where more manual and legacy tools skills are transforming into more modern ones. The growth in continuous testing (CT), Continuous Integration (CI) and DevOps will also translate into much shorter release cadence as a bridge towards real Continuous Delivery (CD)

 

Related to the above, to be ready for the DevOps and CT trend, engineers need to become more deeply familiar with tools like Espresso, XCUITest, Earl Grey and Appium on the mobile front, and with the open-source web-based framework like the headless google project called Puppeteer, Protractor, and other web driver based framework.

In addition, optimizing the test automation suite to include more API and Non-Functional testing as the UX aspect becomes more and more important.

Shifting as many tests left and right is not a new trend, requirement or buzz – nothing change in my mind around the importance of this practice – the more you can automate and cover earlier, the easier it will be for the entire team to overcome issues, regressions and unexpected events that occur in the project life cycle.

AI, ML, and Smarter Test Automation

While many vendors are seeking for tools that can optimize their test automation suite, and shorten their overall execution time on the “right” platforms, the 2 terms of AI and ML (or Deep learning) are still unclear to many tool vendors, and are being used in varying perspectives that not always mean AI or ML 🙂

The end goal of such solutions is very clear, and the problem it aims to solve is real –> long testing cycles on plenty of mobile devices, desktop browsers, IOT devices and more, generates a lot of data to analyze and as a result, it slows down the DevOps engine. Efficient mechanism and tools that can crawl through the entire test code, understand which tests are the most valuable ones, and which platforms are the most critical to test on due to either customer usage or history of issues etc. can clearly address such pain.

Another angle or goal of such tools is to continuously provide a more reliable and faster test code generation. Coding takes time, requires skills, and varies across platforms. Having a “working” ML/AI tool that can scan through the app under test and generate robust page object model, and functional test code that runs on all platforms, as well as “responds” to changes in the UI, can really speed up TTM for many organization and focus the teams on the important SDLC activities in opposed to forcing Dev and Test to spend precious time on test code maintenance.

IOT and The Digital Transformation

In 2017, Google, Apple, Amazon and other technology giants announced few innovations around digital engagements. To name a few, better digital payments, better digital TV, AR and VR development API and new secure authentication through Face ID. IOT this year, hasn’t shown a huge leap forward, however, what I did notice, was that for specific verticals like Healthcare, and Retail, IOT started serving a key role in their digital user engagements and digital strategy.

In 2018, I believe that the market will see an even more advanced wave in the overall digital landscape where Android and Apple TV, IOT devices, Smart Watches and other digital interfaces becoming more standard in the industry, requiring enterprises to re-think and re-build their entire test lab to fit these new devices.

Such trend will also force the test engineers to adapt to the new platforms and re-architect their test frameworks to support more of these screens either in 1 script of several.

Some insights on testing IOT specifically in the healthcare vertical were recently presented by my colleague Amir Rozenberg – recommend to review the slides below

https://www.slideshare.net/AmirRozenberg/starwest-2017-iot-testing/ 

 

Bottom Line

Do not immediately change whatever you do today, but validate whether what you have right now is future ready and can sustain what’s coming in the near future as mentioned above.

If DevOps is already in practice in your organization, fine – make sure you can scale DevOps, shorten release time, increase test and platform automation coverage, and optimize through smarter techniques your overall pipeline.

AI and ML buzz are really happening, however, the market needs to properly define what it means to introduce these into the SDLC, and what would success look like if they do consider leveraging such. From a landscape perspective, these tools are not yet mature and ready for prime time, so that leaves more time to properly get ready for them.

Happy New 2018 to My Followers.

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The Role of Artificial Intelligence in E-Commerce Industry

A Guest Blog Post by Ravindra Savaram

When we think about artificial intelligence(AI), the first thing that comes to our mind is a self-driving vehicle or a Terminator-like robot. Both robots and AI are not exactly one and the same. Though often utilized together with bots, artificial intelligence particularly refers to the stimulation of human intelligence processes by machines. AI powers many technologies that we utilize on a daily basis.

Whether AI is something that you have been monitoring for a while or it’s something that you have just come across, it is undeniable that AI is beginning to influence many industries. One place where it is really changing things is e-commerce. From creating personal buying assistants to personalizing the shopping experience, artificial intelligence is something that retailers cannot ignore.

Many areas of e-commerce are ripe for innovation driven by artificial intelligence. Every enhancement to logistics efficiency, recommendations, pricing, or marketing provides retailers an edge over the competition. Retail creates and consumes large volumes of data from various channels. In fact, there is so much data that it’s not possible for a human being to analyze it. These are the ideal conditions for machine learning.

For various data analysis methods, machine learning is the overarching name. In these methods, the computers get insights in data without actually being told where to look for the insights. When exposed a large amount of data, machine learning algorithms can extract patterns and utilize them to generate predictions or insights about the future conditions.

When you upload a cat picture to cat Google Photos, it knows that the object in the picture is a cat. The code that identifies the cat is not written by a human but it is developed as a result of exposing the algorithm to a large number of cat photos(also, the photos of things that are not a cat).

Recommendations

The same principle explained above can be put to use in many e-commerce areas. For instance, the retailers have become really good at recommending products that are related, but the people who do online shopping knows that the recommendation engines get it wrong very frequently. The recommendation engines are quite limited as they can have access to only a small set of data and the ways they can reason about that data are restricted. Machine learning helps merchants find much better ways of modeling the behavior of users so they can make close to exact recommendations about what a customer is interested in buying. With machine learning, the AI can make predictions based on past data. The predictions include what customers will buy next, their typical price threshold, their preferred device and channel, and so on.

Pricing

Today, the online retail industry is constantly presenting new challenges to COOs and CMOs when it comes to pricing. There is a fierce competition among the e-commerce brands of all sizes and guises. Even for an online merchant for a 1000 product list, somewhat tweaking in manual price can become a task that is almost impossible to accomplish. The environment is changing constantly – rival prices, logistics, currency conversions, and delivery rates are just a small sample of numbers or circumstances prone to change continuously.

The tweaking of prices in real time can be accomplished with artificial intelligence depending on multiple data sets including stock levels, resource capacity, internal operations, customer demand and behavior, and market conditions.

High-level of Assistance

The personal shopping assistants were a luxury of the rich or famous once upon a time. Artificial Intelligence has shaken up this scenario and in the process, revolutionized e-commerce. This conversational and intelligent technology has extended to customer service as well. The chatbots and personal digital shopping assistants can suggest the best available products to new visitors in a manner similar to humans, recommend new deals to your returning customers, answer the queries of a customer and provide suggestions, and alert customers when products they may prefer to purchase come into stock or change in price.

Conclusion

By merging intelligent neural networks with massive data sets, the applications of artificial intelligence will help e-commerce companies to build unparalleled competitiveness in the market. The impact of Personalized Merchandising supported by artificial intelligence on the e-commerce industry will continue to rise in the coming years. They not only optimize or automate current processes but also help retailers to avoid common pitfalls of manual approaches, giving customers an enriched experience to maximize profits.

About the Author:

Savaram Ravindra was born and raised in Hyderabad, popularly known as the ‘City of Pearls’. He is presently working as a Content Contributor at Mindmajix.comHis previous professional experience includes Programmer Analyst at Cognizant Technology Solutions. He holds a Masters degree in Nanotechnology from VIT University. He can be contacted atsavaramravindra4@gmail.com. Connect with him also on LinkedIn and Twitter.

UAV COMMUNICATION SYSTEMS FOR EFFECTIVE CONTROL OF FLYING VEHICLES

Guest Blog Contributed by SkyHopper

Unmanned Aerial Vehicles’ (UAV) Quick Evolution

Fast development in technology in the last few years has seen increased abilities of the Unmanned Aerial Vehicles. Though once restricted to government bodies and a few large establishments, today, the large majority of people can afford a UAV or an RC drone.

Think of your very own mobile phone and all its capabilities. The miniaturization of Micro Electro Systems, Gyroscopes, batteries, and Accelerometers as applied in mobile technology has given birth to the ability to build various types of UAVs. This progress has paved the way to the achievement of innumerable operations that initially seemed impossible.

Need For UAV Communication Systems to control the flying vehicles

Who is in charge of the traffic flow of the possible millions of manned, unmanned and radio controlled aircrafts that are likely to flood the sky over our head? How safe are we as these vehicles fly about?

Almost every country in the world has some kind of traffic management system in place to control the flow of the vehicles on the roads. You must have noticed the road signs, designated pedestrian crossing areas, and traffic lights. They all make movement easier and safer for everyone.

At the moment, the systems used to control the taking off and landing of all the planes; both commercial and military are controlled by staff on the ground. These systems are inadequate for UAVs since they highly depend on physical signs.

With the increase in production of flying vehicles, there is need to coordinate their flow. Putting in place suitable UAV communication systems will ensure safety in air traffic flow. Bearing in mind that these vehicles fly at different attitudes, some are fully independent while others are partial; the system should provide a 3-Dimensional method of navigating the sky. This will prevent collisions of the aerial vehicles which would endanger the lives of people on the ground.

 

Are There Any UAV Communication Systems Currently In Place?

Privately owned drones are only allowed to fly within designated regions. Such drones have the systems installed before they can fly. To guarantee safety, the flying vehicles need to provide correct information to the ground control stations and fellow vehicles. Their response to instructions must also be in real-time. There are several companies that are working on systems with such abilities.

Reliable UAV Communication Systems as The Future of Flying Vehicles

There is a need to integrate various technologies to come up with a system that will provide the required safety for the autonomous vehicles. Such will include latest mobile and Internet technologies as well as other technologies that may not necessarily require the Internet. It will perform remote measuring of various parameters, transmit control information and record a complete 360° video of each vehicle. That UAV system will act as the invisible traffic light for the flying vehicles.

Unlike other industries that have benefited from learning from their mistakes, autonomous vehicles don’t have that luxury. The risk is too big to play with. It is important to prioritize the safety of the vehicles and the people. Safety mechanisms must be integrated into every layer of the UAV Communication System from the get-go.

 Author Biography

This article was contributed by SkyHopper, a leading bi-directional drone data link manufacturer, and innovator. Visit us at Skyhopper.biz

The Essentials of iOS App Testing For iPhone X

48 hours ago, Apple revealed its new and futuristic iPhone X. Regardless of its design, and debatable price tag, this device also introduced a whole set of functionality, display, and engagement with the end-user.

iOS11 is turning to be quite different from previous releases from both user adoption which is still low (~30%) and also from a quality perspective – 4 patch releases in 1.5 months is a lot.

Most of the changes are already proving to cause issues for existing apps that work fine on iOS11.x and former iPhones like iPhone 8, 7 and others.

In this post, I’d highlight some pitfalls that testers as well as developers ought to be doing immediately if they haven’t done so already to make sure their apps are compliant with the latest Apple mobile portfolio.

The post will be divided into 2 areas: Mobile testing recommendations and App Development recommendations.

Mobile Testing for iPhone X/iOS 11

  • Test across all supported platforms as a general statement. iOS11 isn’t for every device, and apps are stuck on iOS10 that has different functionality than the iOS11. Test your apps across iOS9.3.5, iOS 10.3.3, and the latest iOS11.x
  • iPhone X comes from the factory with iOS11.0.1, requesting for an update to iOS1.1 – that means, this device will never get the intermediate iOS11.0.2/iOS11.0.3 – if customers haven’t yet updated to iOS1.1, you may want to have 1 device like iPhone 8/7 still on iOS11.0.3 so you have coverage for iOS11.Latest-1
  • Display and Screen Size for iPhone X specifically changed, and this device has a 5.8” screen size that is different for all other iPhones. Testing UI elements, Responsive apps layouts and other graphics on this new device is obviously a must (below is an example taken from CVS native app showing UI issues already found by me while playing with the device). This device is also full screen similar to the Samsung S8/Note 8 devices. A lot of tables, text field, and other UI elements need to be iOS11/iPhopne X ready by the developers.

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  • New gestures and engagement flow impact usability as well as test automation scripts. In iPhone X, unlike previous iPhones, the user has no HOME Button to work with. That means that in order for him to launch the task manager  (see below) and switch or kill a background running app, he needs to follow a different flow. What that means is that at first, the app testing teams need to make sure that this new flow is covered in testing, and more important, if these flows are part of a test automation scenario, the code needs to be adapted to match the new flow.

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In addition to the removal of the Home button that causes the new way of engaging with background apps, the way for the user to return to the Home screen has also changed. Getting back to the Home screen is a common step in every test automation, therefore these steps need to account for the changes, and replace the button press with Swipe Up gesture.

  • Authentication and payment scenarios also changed with the elimination of the Touch ID option, that was replaced with the Face ID. While iPhone X introduced an innovative digital engagement with the Face recognition technology, the de-facto today to log in into apps, make payments and more, is still the Fingerprint authentication. Testing both methods is now a quality and dev requirement. From a scan that I ran through the leading apps in the market (see examples below), there is a clear unpreparedness for iPhone X. Most apps will either show on their UI the option to log in via Touch ID or if they support Face ID, they will allow users to use it, while still showing on the UI and in the app settings the unsupported option.

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  • Testing mobile web and responsive web apps in both landscape and portrait mode with the unique iPhone X display is also a clear and immediate requirement. I also found issues mostly around text truncation and wrong leverage of the entire screen to display the web content.

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In addition, trying to work with Hulu.com website proved to also be a challenge. Most menu content is being thrown to the bottom of the screen under the user control, making it simply inaccessible. Obviously, the site is not ready for iPhone X/Safari Browser.

Mobile Apps Development

  • Optimize existing iOS apps from both UI as well as authentication perspective. As spotted above, there are clear compatibility issues around the removal of the Touch ID option, that needs to be modified on the UI side of the apps when launched on iPhone X. In addition, scaling UI elements on the new screen whether for RWD apps or mobile apps needs refactoring as well. Apple is offering app developers a ui guidlines to help make the changes fast.Image title
  • Leverage advanced capabilities in iOS11 that best suit the new chipset (AI11 Bionic) and the camera sensors, to introduce digital engagement capabilities around augmented reality (ARKit API’s) and others. Retail apps and games are surely the 1st most suitable segments to jump on these innovative capabilities and enrich their end users’ experiences.

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Bottom Line

The new iPhone X might be paving the way together with the Android Note 8 for a new era of innovation that offers App developers new opportunities to better engage and increase business values. If quality will not be aligned with these innovative opportunities, as shown above, that transformation will be quite challenging, slow and frustrating for the end users’

It is highly recommended for iOS app vendors across verticals to get hands-on experience with the new platform, assess the gaps in quality and functionality, and make the required changes so they are not “left behind” when the innovative train moves on.

Enabling Mobile Testing In a Fast Growing DevOps Reality

6 months ago I launched my 1st book called “The Digital Quality Handbook”.

The book aims to address the key challenges in assuring high mobile (as well as web) quality, by avoiding pitfalls that are commonly practiced in the industry.

I have also recently joined the working group of ISTQB to influence the material in the mobile certification course, where I plan to include insights from the book as well.

In this book, I am hosting top leaders from the industry touching the most important aspects in assuring DevOps.

The above image is taken from Amazon recommending my book close to the leading DevOps practitioner books, this is another strong validation of the book relevancy and value.

Few highlights from the book are below:

  1. Shifting quality left and right to cover as many tests automatically throughout the release pipeline is a key to move faster and identify issues earlier in the process (Angie Jones from Twitter, Manish Maturia from InfoStretch and others provide practitioner level insights and tips)
  2. Testing on the right platforms and OS’s is a key to assure high quality across different devices (new, legacy, popular) in various locations and environments
    1. I am referring to this magazine, that I author on a quarterly basis in the book, and highly recommend subscribing to receive this free asset upon each release: http://info.perfectomobile.com/factors-magazine.html 
  3. Robust automation is achieved through best practices such as building a page object model (POM) and using unique object locators rather than flaky XPATHs etc. I am referring to a free online tool that can help score your object as part of your test automation development http://xpathvalidator.projectquantum.io/
  4. Testing not only via the UI is another key for success, so complementing UI testing with API level testing can reduce the time of testing, provide faster feedback and other values. This chapter was actually developed by my twin brother Lior Kinbruner 🙂 – worth checking it out!
  5. Performance testing and UX is another challenge and key to success. A full section of the book is dedicated to wind tunnel testing, user experience testing (JeanAnn Harrison contributes a lot here together with Amir Rozenberg).

The book was #1 in the new best selling book on Amazon, and still rocking today after more than 6 months. It is #43 as of today in the overall Software Testing Book which is a great validation and honor for me and the contributors.

 

If you still haven’t got a copy of the book, i really encourage you to do so – I am already planning on my next journey so stay tuned 🙂

Mobile Testing Coverage Strategy: OS Families vs. Minor OS Versions?

As the author of the Factors coverage magazine, I am often asked about the operating system (OS) testing strategy.

Some teams would state that testing on the Latest, Latest – 1, and Latest -2 for Android is sufficient, and for iOS testing on the Latest and Latest -1 would satisfy their coverage risks. I actually say that it is not about covering the specific minor OS version, but covering sufficiently the representative OS families. In this post, i will explain my recommended strategy.

Mobile OS Versions Market Share

Late September 2017, Android platform is led by top 5 OS families (4.x, 5.x, 6.x, 7.x and latest 8.x).

In fact, Android 4.x includes 2 families – JellyBeans and KitKat, with the latest being more widely used and popular.

OEM’s are very late in updating their smartphones to the latest OS version, and when they do, they only update the most recent smartphone versions and not the legacy ones that in many cases, cover a large user base.

For Android, as we can see above, any app developer that supports the global market, cannot disregard a market share of 15.1% like the KitKat holds – KitKat is by far not a Latest -2 or even – 4 OS platform. Testing, therefore, an Android App needs to go quite far in the history of the Android OS versions to provide sufficient and risk-free testing.

iOS isn’t that simple either. This platform, especially after the recent iOS11 release, is now split into 3 major OS families that include iOS9.3.5, iOS 10.3.3 and iOS11.0. Each iOS family supports quite important devices that were left behind and did not receive the most up to date iOS update. As an example, iPhone 4S, iPad 2, iPad Mini 2 are stuck on iOS9.3.5, and iPhone 5C, iPhone 5 and iPad 4th Generation are stuck on iOS 10.3.3.

 

While the above market share still doesn’t reflect the iOS11 new market share, there is still 9% of iOS9.x users out there and there will be at least a similar number for iOS10.x once most users shift to iOS11. As in Android, here also we see 3 major OS families that need to be included in the testing strategies.

Beta Testing

Many organizations are being naive to the market innovation, and are not taking advantage of the public beta releases and developer previews coming from both Apple and Google. Android Oreo (8.0) and iOS11 were available for testing prior to their general availability release, however, many teams didn’t leverage this and are finding late in the game the defects, and in many cases, are hearing about them from their customers.

Above is the iOS11 GA bug that was reported, while below is an Android Oreo OS version specific defect that impacted many end-users.

 

Recommendations

Each customer has its unique user base, target geographies and in many cases also access to the user’s analytics.

Customers should follow the following guidelines

  • Monitor your user base and build a test lab that addresses the top user’s devices/OS permutations (use your own analytics)
  • Learn and monitor the market trends like the above so they do cover in addition to their analytics the major OS families (5-10% and above market share should be highly considered)
  • Testing on public OS beta’s is a must. Google Pixels, iOS Simulators as well as desktop web beta versions are always available for testing from day one
  • Mix device manufactures with the selected OS families to maximize the coverage (e.g. Samsung XX/Android 6.x, LG XX/Android 5.1.x, etc.)
  • Cover real environment testing to identify real-life glitches like mentioned above and as we see often in the market.

 

Happy Testing!

 

HTML5 Makes Mobile Testing More Encouraging For Gaming Apps

The need for mobile testing of gaming applications has long been apparent. With app stores for Android and iOS devices more or less overflowing with games of all different kinds, it stands to reason that a lot of developers are rushing the process. That is to say, there are a lot of cheaper or less refined games reaching the market in this category, and testing will reveal as much. As one article put it in an overarching write-up on the very concept of mobile testing, app store halls are now littered with thousands of one-star reviews that tell a tragic tale.

This is largely due to frantic developers hoping to be first to market with a given idea, or simply hoping to flood the market with multiple ideas, without worrying much about quality. However, it’s also because the coding languages that serve as the foundation of mobile games are varied and can be inconsistent. Certainly, a capable and creative developer can always find a way to design a beautiful and intuitive experience. But as technology improves and evolves, newer design methods will improve the overall quality of the mobile gaming market to a degree.

One thing to watch in this regard is HTML5. Hailed as a borderline revolutionary cross-platform design option for games, it has been somewhat slow to emerge in the mobile market in a big way. Casino games, in particular, are beginning to show its potential. As a popular gaming platform puts it, there are plenty of developers out there who are more than happy to indulge gamers’ craving for new experiences. In doing so, one method they’ve embraced is the use of HTML5 to put together slot games that exist at online casinos but can easily be downloaded as high-quality apps also. It’s a subtle but smoother version of a game being adapted from scratch to suit mobile devices.

We’ve seen this same process occur with some one-off arcade games as well. Bejeweled, for instance, made such a seamless transition from browser mobile devices because it was actually one of the earlier major HTML5 games. In fact, the same can be said of Angry Birds. Those are probably the two most high profile examples, though there are other mobile games that are recognized for high quality and built on HTML5 as well.

This should gradually be leading to a better selection of high quality, high-performance mobile games, and while it’s difficult to make any kind of overarching statement about mobile game testing, one article, in particular, indicated that HTML5 has had an encouraging effect. Discussing a focus on mobile, the writers declared that they weren’t even bothering to test desktops because performance had been so consistently strong. They referred to this as a positive sign for the maturity of HTML5.

By extension, that means good things for the mobile gaming market.

Complementing Cross-Browser Testing with Headless Unit Testing Solutions

Nothing new in the land of cross-browser testing. Selenium as the underlying API layer serves leading frameworks including WebDriverIO, Protractor (Angular based testing), NightWatchJS, RobotJS and many others.

For web application developers that require fast feedback capabilities post their code commit or bug resolution, there are various testing options. Some would quickly test manually on a set of local or cloud based VM’s, some will develop unit tests (qUnit etc.), but there are also very mature cross browser testing solutions that add more layers of coverage and insights in an automated and easy way.

In a recent eBook that I developed, I’m covering the 10 emerging cross-browser testing tools with a set of considerations around how to choose the right one or the right mix of them.

As can be seen in the 10 tools shown above, there is a mix of a unit as well as E2E functional testing tools mostly javascript based.

Developers who would like to include as part of their quick sanity post commit a validation of the load time it takes the site to load, can easily add this PhantomJS based test into their CI post build acceptance testing and get such visibility after each successful build – that, match the result with a benchmark and take decisions.

In a quick test that I ran on the NFL.com website, I was able to not only detect a slow load of 10sec. but I also identified a long set of errors while the page is loaded.

Another powerful capability tools like PhantomJS can offer is the ability to both capture a specific rendering of a web page by a pre-defined viewport, as well as the ability to generate a page HAR file for network traffic analysis (I am aware that it is not the newest tool, and that Goole already provides a newer version, but still this is a valuable open-source free tool that can help add coverage capabilities to any web development team).

So if as an example, the load time with errors above turns on a red light regarding that site, with 2 simple tests that BTW PhantomJS provides as their starting kit in GIT, the developer can address the above 2 use cases of HAR file generation as well as page rendering screenshot.

The result of the above snippet is the screenshot below:

The HAR file creation that is based on the following GIT code sample will result in the following (I am using the google add-on HTTP Archive Viewer for Chrome, it can be done simply with other HAR viewers as well):

Bottom line

You can download my latest eBook and learn more, but in general – leverage both unit testing powerful tools, as well as traditional E2E tests, hence they do complement each other and add their unique value – And it’s Free!

Happy Testing!

Optimizing Mobile Test Automation Across The Pipeline

With the massive innovation that drives the digital market these days, organizations are continuing to develop features, as well as new test code to cover these features.

What I’ve learned is that often, the test code developers would not always stop and look back into their existing test suites and validate whether the new tests that are being developed are somehow a superset to existing ones. In addition, legacy tests are a continuous load and overhead on your SDLC cycles length if they are not being maintained over time.

Oil Transport

Many Owners To The Same Problem

Since we live in an agile/DevQAOps world, test code development is not a QA only problem, but rather everyone3s. Tests are being executed throughout the pipeline from Dev to integration and pre/post production testing.

Use of smart tagging mechanism for your test scenarios (login), suites (App A) and types unit, regression) can be a good step towards gaining control over your tests.

Without some context, discipline, and continuous structured validation of the tests, it will become harder as you progress your SDLC to debug, analyze and solve defects (would be like finding the key in the below visual mess)

Find the Key in the Picture

Recommended Practices

  • Develop the tests with context, tags and proper annotations that would make sense to you and your team even 12 months from the development day. Make sure that in your execution reports you then have a way to filter using these annotations to only get the view of a given functional area, platform etc.
  • Match your device under tests capabilities to the test code and application under test. Make sure that you focus e.g. your fingerprint based tests only on the devices that support it (API XX and above).
  • Perform test code review every agreed upon time – in such review, group your feature specific test suites and try to optimize, merge, eliminate flakiness, identify missing coverage areas etc. It is harder to do it as the time progresses, so depending on your release cadence and test development maturity, set the right goals – more reviews would be better than less – it will also be shorter and more efficient that way since the delta between such review will be smaller.
  • Drive joint Dev, Test, Product, Marketing decisions based on data – When you have the ability to get quality analysis from your entire test suites, it is recommended to gather all counter parts and brainstorm on the findings. Which tests are most effective, can we shrink based on the data the release cycles, are we missing tests for specific areas, are there platforms that are more buggies than others, which tests takes longer than others to finish etc.
  • Optimize your CI and build-acceptance testing – based on the above intelligence, teams can reach data driven decision about what to include in their CI as well. Testing in the build cycle via CI should be fast, reliable with zero false positives. With quality insights on your tests, you can decide and certify the most valuable and fastest tests to get into this CI testing, and by that to shrink the overall process without risking coverage aspect.

CI_Dash1.png

Bottom Line

A test is code, and like you refactor, maintain, retire and improve your code, you should do the same to your tests. Make sure to always be in control over your tests, and by that, gain control over your quality of your app in a continuous manner.

Happy Testing!

Trends in Cross Browser Testing and Web Development

Typically, i”ll write a lot on mobile app testing, tools, trends, coverage and such.

In this blog, I actually wanted to share some up to date trends as I see them in the web landscape.

The web market has shifted a lot over the past years alongside the mobile space. We see a clear use of specific development languages, development frameworks and of course specific test frameworks aimed to test Angular, jQuery, Bootstrap,.Net and other websites.

From a Dev Language perspective, the web FE developer is mostly using the following languages as part of his job:

Sourcehttp://vintaytime.com/premium/top-programming-languages/

As a clear trend in web development, it shows that JavaScript is the leading language used by web developers. It’s actually not a huge surprise since if you move to the top frameworks used by these web developers, you will see quite a few that are based on JavaScript.

There are some trends seen recently by developers around shifting to non AngularJS web development framework like Aurelia, React, and Vue.JS that are seeing a growing usage and adoption by developers due to considerations such as (larger list of Pro’s/Con’s are in source 1 below). With this trend in mind, and you’ll read in my references below, the new solutions are still not as complete as AngularJS is.

  • Shorter learning curve
  • Simple to use, clean
  • Flexibility
  • Lightweight compared to others (less than half the size of AngularJS e.g.)
  • Better performing
  • Easy to integrate with other front-end stack tools
  • Responsive server-side rendering (Vue.JS supports it, reduces time for users to see rendered content)
  • SEO Friendly
  • Good documentation and Community Support
  • Good debugging capabilities

Source 1: https://www.slant.co/topics/4306/~angular-js-alternatives

Source 2: https://w3techs.com/technologies/details/js-angularjs/all/all

Now, that we have seen the leading web development languages, and frameworks used these days, let’s drill down into what test automation engineers are adopting.

Selenium without a doubt is the leading and base for most frameworks, however, even in this space, we see new and innovative test frameworks such as Casper.JSTestCafeBuster.JSNightwatch.JS together with the traditional Webdriver.IO and of course Protractor.

If we examine the below visual (SourceNPM Trends), it’s a clear market dominance between Selenium and Protractor that underneath its implementation does uses Selenium WebDriver, and supports Jasmine and Mocha tools.

The advantage of tools like Protractor is that they support much easier web sites that were developed in various frameworks like AngularJS, Vue.JS etc. Such advantage allows test automation engineers to agnostically use them for multiple websites regardless to the frameworks they are built with.

It is not that easy, and pink as I described above, but it does give a good headstart when starting to build the test automation Foundation.

Thre are few other players in that space that are aimed at specific unit testing, and headless browser testing (Phantom.JS, Casper.JS, JSDom etc.).

As I blogged in the past, from a test automation strategy perspective, teams might find it beneficial and more complete to leverage a set of test frameworks rather than using only one. If the aim is to have non-UI headless browser testing together with Unit testing and also UI based testing, then a combination of tools like Protractor, Casper.JS, QUnit might be a valid approach.

I hope you find this post useful, and can “swim” in the hectic tools landscape. As always, it is important to match the tool to the product requirements, development methodology (BDD, Agile, Waterfall etc.), supported languages and more.