How to Analyze Data to Predict Device Coverage

Designing an application is backbreaking work. Consider that after all that work and time creating a complex piece of software, it all amounts to nothing. Upon release, most customers are unable to use the application as it & # 8217; s not functional on most device.

This constitutes a major loss of revenue. Therefore, it & # 8217; s essential for developer to take into circumstance compatibility when designing applications.

Instead of guessing the device that may symbolize their target audience, the developer teams need to use data analytics to read what their consumer market truly seem like and increase device coverage for their coating. This usher describes data analysis strategies to accurately augur the twist coverage for an application and address any issues prior to release.

Understanding Device Fragmentation

can best be defined as such: There are multiple devices, browsers, and operating system. Every user can have a different device and use a different browser or a different OS with different versions. This betoken that the potential user market for an covering can have a embarrassment of potential combinations of devices, browser, OS, and other software.

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The two nearly democratic OS today, instance in the figure above, are iOS and Android. As of August 2022, Android dominates with a market share of 71.47 %, with iOS get in second with a market percentage of 27.88 %.

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All iOS device receive new releases and upgrade simultaneously unless there are hardware compatibility issues. It can be seen that as of August 2022, 73.18 % of iOS users are on version 15.6 or 15.5.

Unfortunately, although Google regularly freeing updates for Android; Most users with Android devices aren ’ t on the virtually current version of the OS. Where iOS controls its package release, phone maker control the Android versions offered on their devices; Often, new Android device have highly outdated edition of Android installed.

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The latest Android variation as of August 2022 was 12.0. However, it can be seen that it is not the most used version. Android 11.0, an outdated version released in 2020, is the most used Android OS adaptation.

In fact, 76.42 % of Android twist users are on antiquated OS versions, as a result of which they are sure more susceptible to malware and other security flaws. This inevitably set a greater burden of responsibility on application developers who have to create software for these out-of-date OS versions.

Looking at this data, it & # 8217; s open why the device fragmentation job is an absolute nightmare for coating developers. When it comes to fragmentation, the iOS market part is manageable. However, the Android market share is the largest and the most problematic. With the battalion of versions, users, and devices, the trouble should be taken into consideration when attempting to attain great data coverage for a software coating.

Pro tip: Tools like SUSA can handle this autonomously — upload your app and get results without writing a single test script.

How to Analyze Data to Infer Potential Device Coverage

There are several aspect that need to be guide into condition when attempting to obtain maximum device coverage for a software covering.

For a pre-existing covering, it is possible to hoard data regarding the users ’ devices and endeavor to increase coverage on that basis. However, survivorship diagonal will come into drama since the exploiter traffic will be from devices that can already support and run the application; Therefore, this method of data collection won ’ t suffice.

Using market data to carry out analysis would be most optimal to extrapolate twist coverage.

One method to transmit out data analysis could be to name the quarry customer groundwork for the application in interrogative. Based on this farther device groups can also be created on the basis of the goals of the covering and the technologies used.

In some cases, mobile device manufacturers get customizations in the Android architecture to enhance the user experience; This is done by using custom ROM [1]. By introducing these changes to the lower degree system architecture, there will also be changes introduced in the high level system. This conduct to compatibility issue since the API will have difficulty convey with the device driver.

Numerous companies, including Xiaomi, One Plus, Huawei, and others, regularly release new smartphones with customized ROMs [1].

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It may be pertinent to place and prevail market data regard brands that are prone to making these changes and identify the application ’ s functionality across these devices. Eventually, it may be reasonable to determine which of these devices has a sizable decent marketplace percentage that it is advantageous to conform the application to run on them.

Another method to promise device reportage could be to collect research data for pre-existing covering which have like functionality and design. This data could include information reckon users ’ devices, OS versions, and browser usage.

By observing the marketplace share of users and device coverage for reference applications, it ’ s possible to obtain a rough estimate for the covering be designed. The data can be segment to name which devices are extend and which are not, and farther analysis could be impart out to identify which devices, that autumn under the mark client bag, are not cover.

If a specific coating can ’ t be found, more generic applications can besides be utilize since this data analysis is being perform to merely gauge the likely device coverage.

Popular Data Sources for Data Analysis

There are several potential information sources that can be used to carry out the information analysis processes detail above.

1. https: //gs.statcounter.com/: This website offers statistic regarding marketplace percentage for device vendors, OS, and several other factor which may be utilitarian for compatibility testing. The statistics available on this website are based on aggregate information gathered by Statcounter from over 1.5 million website, representing a sampling of over 5 billion pageviews every month. Every day, statistics are update and made available, but they are also subject to quality control checks and revisions for 45 day after publication.

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2. statista.com: Statistics on more than 80,000 topics are compiled by Statista.com from more than 22,500 sources. This website offers useful infographics and research with references for market data. This is yet another helpful tool for exploring and counterpoint mobile device models, features, and traffic analysis.

3. deviceatlas.com: DeviceAtlas proffer a method for observe device access on-line material. All firms who wish to optimize revenue from their applications require knowledge pertaining to devise traffic.

Testing to Ensure Device Compatibility and Coverage

In order to ensure that the application being designed has sufficient twist coverage, it is imperative to screen former and to test continuously. To keep up with the thousands of device/OS combinations released every year, you can use tools like BrowserStack.

With BrowserStack Automate, you can integrate with several popular automation frameworks such as,,,, and more to carry out testing across 3000+ background browsers and existent mobile devices. BrowserStack offers consolidation with several uninterrupted integrating tools like, Bamboo, TravisCI, and several others so that continuous testing can be carried out to keep your application relevant and functional across all new freeing.

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