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.