Suyash Dubey | Posted on | 2 min Read

Intelligent Digital Mesh is the entwining of people, devices, content, and services enabled by digital models, business platforms and a rich, intelligent set of services to support digital business. We have witnessed the implementation of AI in every technology to leverage the benefits of autonomous systems. Enterprises are now focusing on using AI with technologies like blockchain and immersive technology which will create new categories of apps. In this type of environment, attaining optimum device coverage will be essential to ensure quality services. Now let’s understand the fundamentals of the intelligent digital mesh.


In the near future, most of the mobile applications and services will use artificial intelligence or machine learning at some level. AI will be the inconspicuous force of most of the popular app categories while creating some new ones. Intelligent apps also create a new intelligent layer between people and systems as seen in enterprise advisors and virtual user assistants. Augmented analytics is also gaining ground and helping enterprises in enhancing business intelligence and data analytics using ML and NLP. Another use of AI and ML is in intelligent things like smart vacuums, drones, autonomous farming vehicles. Intelligent devices are getting smarter to serve better and reduce human dependency to a minimum.
Top 10 Strategic Technology Trends for 2019



When we talk about digital, we mean digital twins, cloud to the edge, conversational platforms, and Immersive Experience. A digital twin is a digital representation of real-world objects. It offers information on the state of the counterparts, improves operations and adds value to the operations by responding to the changes. In the near future, all the aspects of human life and the real world will be interconnected with their digital representation capable of advance simulation, analysis, and operation. This combined with immersive technologies like AR, VR, and MR will take extended reality to a new level.


Mesh is the connection between devices, people, businesses, services, and content to build a digital ecosystem that yields high-quality results. Here mesh refers to technologies like Blockchain, Event-driven, and continuous adaptive risk and trust (CARTA). Enterprises are keen to find new ways to sense the new business events to get the most out of it. A business event can be a change in the status of the deal like finalizing a deal. Using new technologies like AI, it will be easier to detect a business event and analyze it in greater detail.
Security is one of the most important and ever-evolving processes in digital businesses. There is a need to think beyond infrastructure and parameter protection. Continuous adaptive risk and trust assessment is a people-centric security approach that allows for real-time risk and trust-based decision making. New methodologies like DevSecOps and adaptive honeypots should be implemented to strengthen the security of digital businesses.

Automation Testing For Intelligent Apps

Intelligent apps are at the core of the intelligent digital mesh. Nowadays most of the apps use artificial intelligence, machine learning or predictive analysis to make suggestions to the customers. The apps use real-time and historical data from user interactions and other sources to predict the needs of their users.
To ensure the quality of apps it is important to test the apps using futuristic tools. Manual testing is just enough and even automation needs to be scalable to get better results. Testing the app on a cloud-based app testing platform is the best choice as you can use as many devices as you want to test your app. Also, parallel testing increases app testing efficiency by multifold.
pCloudy’s AI-powered autonomous testing bot steals the show when it comes to testing intelligent apps. The bot tests the app on real devices with just a single click and generates a detailed report based on the test result.


Mobile devices, by and large, are the focal point of most of the innovations that are happening around the intelligent digital mesh. Whether it is Ai driven development, autonomous things or immersive experience, mobile apps still used as a foundation to provide the technology to the masses. But the growing complexities of intelligent apps makes it crucial to implement new methods of app testing. A cloud-based app testing platform like pCloudy is suitable to ensure quality at speed in mobile app testing. The freedom of accessing hundreds of real devices from anywhere at any time and perform manual or automation testing using futuristic features is the correct way to test intelligent apps.

Suyash Dubey | Posted on | 2 min Read

DevOps helps enterprises to build software at a fast pace and with minimal issues. The time to market is accelerated and the bugs are fixed faster in continuous deployment with the help of automated tools. AI is much in line with DevOps as the main focus is on automating the process and with AI the system can identify patterns, anticipate issues and provide solutions. The proactive approach improves the overall efficiency of the software development life cycle. So let’s have a look at how AI is transforming DevOps.


Feedback Loop and Correlate Data

The main role of DevOps is to take continuous feedback at every stage of the process. often people use performance monitoring tools to get feedback on running applications. These tools gather much information in the form of log files, data sheets, performance matrix, and other types. The monitoring tools use machine learning to identify the issues early and make suggestions. The DevOps teams use these suggestions to make the necessary improvements to the application. Many times teams use two or more tools to monitor the health of the app and the data from all the platforms can be correlated by the help of machine learning to get a more deep understanding of the app functioning.
AI Plan Release Debug - DevOps

Software Testing

AI is changing DevOps for good by enhancing the software development process and making testing more efficient. Whether it is regression testing, user acceptance testing or functional testing, these all produce a large amount of data. AI can figure out patterns in the data collected in the form of results and identify poor coding practices which produce a lot of errors. This information can be used by the DevOps teams to increase their efficiency.

Anomaly Detection

DevSecOps is one of the essential aspects of software development as security is the key to any successful software implementation. Distribution denial of service attacks are increasing and the business needs to prepare themselves to protect their security systems from hackers. DevSecOps can be augmented using artificial intelligence to enhance security by central logging architecture to record threats and running machine learning based anomaly detection. This will help businesses proactively attenuate the attack from hackers and DDOS.


DevOps approach might create scenarios where the team receive an overwhelming amount of alerts without any priority tag. This will create ruckus in the teams as it will be very difficult to handle all the alerts in the continuous development environment. AI can help in this scenario by tagging the alerts and prioritizing them so that the urgent ones can be worked upon immediately.

Root Cause Analysis

To fix an issue permanently, a root cause analysis is necessary. Although it might take time to do it compared to fixing the issue with a patch which will provide the instant solution. In order to find the root cause of an issue, the developers will have to spend time which will delay the release of the product. AI can speed up the process by finding patterns in the data collected and implement to fix the root cause.
The collected data can be used by implementing AI to find a pattern and speeding up the development process. The organized data is more useful and makes prediction possible. The best practice is to use machine learning to automate the tasks which are time-consuming which will ensure the smooth and effective functioning of the DevOps teams.

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  • Suyash Dubey | Posted on | 2 min Read

    Software testing has evolved a lot since the time when the waterfall model was used. All the work was done in a sequential manner and only after the development phase was complete the testers used to test the product. Testers used to find bugs but a lot of time and energy was wasted in the process to rebuild and code again.

    Now companies are using an Agile model where the main goal is to find the bugs in continuous development, fix them quickly and release the app faster. There is a need to improve the automated testing process to complement the manual testing. More emphasis has been given to CI, CD, and DevOps to make the software development effective.

    There has been a considerable change in the functioning of testing tools and test automation frameworks. The most important change is the introduction of AI in a test automation strategy.

    According to G2Crowd, AI-powered bots are expected to cut business cost by $8 billion by 2022. Testing bots are already empowering automation testing and will play a major role in reducing the time and effort spent in mobile app testing.

    Let’s have a look at how AI is breaking new ground for test automation.

    1. Running automated tests that matter

    It’s not a good strategy to run your entire test suite due to a very small change in your app that you couldn’t trace. You are probably already generating a lot of data from your test runs if you are doing continuous integration. But it will take a lot of time to go through the data and search for common patterns. So you need to know if you make a small change in code then what is the minimum number of test you need to run to figure out if the change is needed or not.

    2. Reducing maintenance and eliminating flaky test

    We can run several automated tests on a daily basis to ensure the functionalities of the app are still stable. Although, if we find out that half of this test failed. In that case, we would need to spend a lot of time to troubleshoot the failures and investigate the cause. Then there is a need to find ways to fix the failures and then work on the changes.

    software maintainance

    Using AI we can avoid issues and start detecting issues in the test before they even occur. So instead of reacting to it, we can proactively fix tests. AI can figure out which tests are stable or flaky based on the number of test runs and it can tell us what test needs to be modified to ensure test runs are stable. AI can also handle test running on different resolutions and can optimize the wait time used in the test to wait for the page to load.

    3. Dependencies on other modules

    Writing a test for systems having dependencies on other modules is also a challenge. AI can help us to mock responses from a database or server. The AI can start recording server responses once we have written the test and have run them for a period of time. So the next time we run the test it will access the stored responses and will continue to run without any obstacles. This will speed up the process as the delay in response is eliminated and the server or physical database is no more needed.

    4. Learning from production data

    Real user data can be used to create an automated test and with the help of AI, we can observe and learn how the customer is using our product. We can identify common actions such as search option, using filters, login/logout, etc and compile them into reusable components. These components can be used for our test as well. Therefore, we have an actual test written by AI based on the real data along with the reusable components.

    5. Easy execution of tests and speeding up the release

    In automation testing, the time and effort it takes to write and execute a test is a major challenge due to the complexity of the test automation tools, app, and programing language used. To mitigate these problems AI-based tools are being used. The use of dynamic locators and reusable components has made it possible to write and execute a test in hours which earlier used to take a week.


    The DevOps theory says test early, test often, but this puts a lot of responsibility on the testing team. Also, it’s not feasible for testing teams to spend time to do exploratory testing manually for each new release. AI-based tools can perform codeless automation testing which will save us time and resources and give the testers some space to breathe.

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