Tag Archives: Google Play

Google Play services discontinuing updates for API levels 14 and 15

Posted by Sam Spencer, Technical Program Manager, Google Play

The Android Ice Cream Sandwich (ICS) platform is seven years old and the active device count has been below 1% for some time. Consequently, we are deprecating support for ICS in future releases of Google Play services. For devices running ICS, the Google Play Store will no longer update Play Services APK beyond version 14.7.99.

What does this mean as an Application developer:

The Google Play services SDK contains the interfaces to the functionality provided by the Google Play services APK, running as background services. The functionality required by the current, released SDK versions is already present on ICS devices with Google Play services and will continue to work without change.

With the SDK version changes earlier this year, each library can be independently released and may update its own minSdkVersion. Individual libraries are not required to change based on this deprecation. Newer SDK components may continue to support API levels 14 and 15 but many will update to require the higher API level. For applications that support API level 16 or greater, you will not need to make any changes to your build. For applications that support API levels 14 or 15, you may continue to build and publish your app to devices running ICS, but you will encounter build errors when updating to newer SDK versions. The error will look like this:

Error:Execution failed for task ':app:processDebugManifest'.
> Manifest merger failed : uses-sdk:minSdkVersion 14 cannot be smaller than version 16 declared in library [com.google.android.gms:play-services-FOO:16.X.YY]
        Suggestion: use tools:overrideLibrary="com.google.android.gms:play_services" to force usage

Unfortunately, the stated suggestion will not help you successfully run your app on older devices. In order to use the newer SDK, you will need to use one of the following options:

1. Target API level 16 as the minimum supported API level.

This is the recommended course of action. To discontinue support for API levels that will no longer receive Google Play services updates, simply increase the minSdkVersion value in your app's build.gradle to at least 16. If you update your app in this way and publish it to the Play Store, users of devices with less than that level of support will not be able to see or download the update. However, they will still be able to download and use the most recently published version of the app that does target their device.

A very small percentage of all Android devices are using API levels less than 16. You can read more about the current distribution of Android devices. We believe that many of these old devices are not actively being used.

If your app still has a significant number of users on older devices, you can use multiple APK support in Google Play to deliver an APK that uses Google Play services 14.7.99. This is described below.

2. Build multiple APKs to support devices with an API level less than 16.

Along with some configuration and code management, you can build multiple APKs that support different minimum API levels, with different versions of Google Play services. You can accomplish this with build variants in Gradle. First, define build flavors for legacy and newer versions of your app. For example, in your build.gradle, define two different product flavors, with two different compile dependencies for the stand-in example play-services-FOO component:

productFlavors {
    legacy {
        minSdkVersion 14
        versionCode 1401  // Min API level 14, v01
    current {
        minSdkVersion 16
        versionCode 1601  // Min API level 16, v01

dependencies {
    legacyCompile 'com.google.android.gms:play-services-FOO:16.0.0'
    currentCompile 'com.google.android.gms:play-services-FOO:17.0.0'

In the above situation, there are two product flavors being built against two different versions of play-services-FOO. This will work fine if only APIs are called that are available in the 16.0.0 library. If you need to call newer APIs made available with 17.0.0, you will have to create your own compatibility library for the newer API calls so that they are only built into the version of the application that can use them:

  1. Declare a Java interface that exposes the higher-level functionality you want to perform that is only available in current versions of Play services.
  2. Build two Android libraries that implement that interface. The "current" implementation should call the newer APIs as desired. The "legacy" implementation should no-op or otherwise act as desired with older versions of Play services. The interface should be added to both libraries.
  3. Conditionally compile each library into the app using "legacyCompile" and "currentCompile" dependencies as illustrated for play-services-FOO above.
  4. In the app's code, call through to the compatibility library whenever newer Play APIs are required.

After building a release APK for each flavor, you then publish them both to the Play Store, and the device will update with the most appropriate version for that device. Read more about multiple APK support in the Play Store.

Celebrating the developers behind the best apps and games of 2018

Posted by Purnima Kochikar, Director, Business Development, Games & Applications

Today, we announced our annual Best of 2018 list, highlighting the best content on Google Play. But ever wonder about the makers behind your favorite apps and games like PUBG MOBILE or Tasty? Well, we wanted to take a moment to celebrate the developers that brought to life the best U.S. apps and games of 2018. And this year was jam packed with entertainment — all thanks to the developers who pushed the envelope and sparked our imaginations.

Check out the full rundown of the developers behind the best apps and games of 2018 on Google Play:

Best App of 2018

Most Entertaining Apps

Best Self Improvement Apps

Best Daily Helper Apps

Best Hidden Gem Apps

Best Game of 2018

Most Competitive Games

Most Innovative Games

Best Indie Games

Most Casual Games

Announcing Google Play’s “Best of 2018”

As 2018 comes to a close, Google Play is sharing the year’s top content across apps, games, movies, TV and books. Whether you were watching “Black Panther” wishing Wakanda was a real place, or imagining what you’d do if you were thrown into “Jumanji: Welcome to the Jungle,” 2018 has been filled with nonstop entertainment.

For the first time ever, we’ve added a new “Fan Favorite” category, giving you the chance to vote for your favorite game, app and movie of the year. And the votes are in! Thanks to you, “PUBG MOBILE,” “YouTube TV” and “Avengers: Infinity War” have been crowned as the first-ever Fan Favorites in the U.S.

Check out Google Play’s full lists below for this year’s most loved content in the U.S. You can also discover all of the lists on the Best of 2018 section of the Play Store.

Fan Favorites of 2018

Best App of 2018

Best Game of 2018

Top five movies of 2018

Top five TV shows of 2018

Top five ebooks of 2018

Top five audiobooks of 2018

SDK Developers: sign up to stay up to date with latest tips, news and updates

Posted by Parul Soi, Strategic Partner Development Manager, Google Play

Android is fortunate to have an incredibly rich ecosystem of SDKs and libraries to help developers build great apps more efficiently. These SDKs can range from developer tools that simplify complicated feature development to end-to-end services such as analytics, attribution, engagement, etc. All of these tools can help Android developers reduce cost and ship products faster.

For the past few months, various teams at Google have been working together on new initiatives to expand the resources and support we offer for the developers of these tools. Today, SDK developers can sign up and register their SDK with us to receive updates that will keep you informed about Google Play policy changes, updates to the platform, and other useful information.

Our goal is to provide you with whatever you need to better serve your technical and business goals in helping your partners create better apps or games. Going forward we will be sharing further resources to help SDK developers, so stay tuned for more updates.

If you develop an SDK or library for Android, make sure you sign up and register your SDK to receive updates about the latest tools and information to help serve customers better. And, if you're an app developer, share this blogpost with the developers of the SDKs that you use!

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Ring in the holiday season with deals on Google Play

The holiday season is nearing
Awaiting their school break, kids are cheering
And we’re pretty excited over here, too
We’ve got deals for kids, gamers, bookworms and you
So check ‘em out below, find your fave
Head to the Google Play Store—and dollars you’ll save

For the gamer: Up to 80 percent off top titles and special in-app deals like:

For the movie and TV buff: Available on Turkey Day only, rent any move for 99¢. And if you want to catch up on some of this year’s most popular TV shows and movies, check out discounts on titles like:

For the bookworm: Serious discounts on this year’s best-selling audiobooks and ebooks, such as:

For the gift-giver: Once your shopping checklist is complete, it's your turn to unwind. And here are some details that can help:

Excited to get into the holiday spirit? Here's more information on all of our Cyber Week deals.

Combating Potentially Harmful Applications with Machine Learning at Google: Datasets and Models

Posted by Mo Yu, Android Security & Privacy Team

In a previous blog post, we talked about using machine learning to combat Potentially Harmful Applications (PHAs). This blog post covers how Google uses machine learning techniques to detect and classify PHAs. We'll discuss the challenges in the PHA detection space, including the scale of data, the correct identification of PHA behaviors, and the evolution of PHA families. Next, we will introduce two of the datasets that make the training and implementation of machine learning models possible, such as app analysis data and Google Play data. Finally, we will present some of the approaches we use, including logistic regression and deep neural networks.

Using machine learning to scale

Detecting PHAs is challenging and requires a lot of resources. Our security experts need to understand how apps interact with the system and the user, analyze complex signals to find PHA behavior, and evolve their tactics to stay ahead of PHA authors. Every day, Google Play Protect (GPP) analyzes over half a million apps, which makes a lot of new data for our security experts to process.

Leveraging machine learning helps us detect PHAs faster and at a larger scale. We can detect more PHAs just by adding additional computing resources. In many cases, machine learning can find PHA signals in the training data without human intervention. Sometimes, those signals are different than signals found by security experts. Machine learning can take better advantage of this data, and discover hidden relationships between signals more effectively.

There are two major parts of Google Play Protect's machine learning protections: the data and the machine learning models.

Data sources

The quality and quantity of the data used to create a model are crucial to the success of the system. For the purpose of PHA detection and classification, our system mainly uses two anonymous data sources: data from analyzing apps and data from how users experience apps.

App data

Google Play Protect analyzes every app that it can find on the internet. We created a dataset by decomposing each app's APK and extracting PHA signals with deep analysis. We execute various processes on each app to find particular features and behaviors that are relevant to the PHA categories in scope (for example, SMS fraud, phishing, privilege escalation). Static analysis examines the different resources inside an APK file while dynamic analysis checks the behavior of the app when it's actually running. These two approaches complement each other. For example, dynamic analysis requires the execution of the app regardless of how obfuscated its code is (obfuscation hinders static analysis), and static analysis can help detect cloaking attempts in the code that may in practice bypass dynamic analysis-based detection. In the end, this analysis produces information about the app's characteristics, which serve as a fundamental data source for machine learning algorithms.

Google Play data

In addition to analyzing each app, we also try to understand how users perceive that app. User feedback (such as the number of installs, uninstalls, user ratings, and comments) collected from Google Play can help us identify problematic apps. Similarly, information about the developer (such as the certificates they use and their history of published apps) contribute valuable knowledge that can be used to identify PHAs. All these metrics are generated when developers submit a new app (or new version of an app) and by millions of Google Play users every day. This information helps us to understand the quality, behavior, and purpose of an app so that we can identify new PHA behaviors or identify similar apps.

In general, our data sources yield raw signals, which then need to be transformed into machine learning features for use by our algorithms. Some signals, such as the permissions that an app requests, have a clear semantic meaning and can be directly used. In other cases, we need to engineer our data to make new, more powerful features. For example, we can aggregate the ratings of all apps that a particular developer owns, so we can calculate a rating per developer and use it to validate future apps. We also employ several techniques to focus in on interesting data.To create compact representations for sparse data, we use embedding. To help streamline the data to make it more useful to models, we use feature selection. Depending on the target, feature selection helps us keep the most relevant signals and remove irrelevant ones.

By combining our different datasets and investing in feature engineering and feature selection, we improve the quality of the data that can be fed to various types of machine learning models.


Building a good machine learning model is like building a skyscraper: quality materials are important, but a great design is also essential. Like the materials in a skyscraper, good datasets and features are important to machine learning, but a great algorithm is essential to identify PHA behaviors effectively and efficiently.

We train models to identify PHAs that belong to a specific category, such as SMS-fraud or phishing. Such categories are quite broad and contain a large number of samples given the number of PHA families that fit the definition. Alternatively, we also have models focusing on a much smaller scale, such as a family, which is composed of a group of apps that are part of the same PHA campaign and that share similar source code and behaviors. On the one hand, having a single model to tackle an entire PHA category may be attractive in terms of simplicity but precision may be an issue as the model will have to generalize the behaviors of a large number of PHAs believed to have something in common. On the other hand, developing multiple PHA models may require additional engineering efforts, but may result in better precision at the cost of reduced scope.

We use a variety of modeling techniques to modify our machine learning approach, including supervised and unsupervised ones.

One supervised technique we use is logistic regression, which has been widely adopted in the industry. These models have a simple structure and can be trained quickly. Logistic regression models can be analyzed to understand the importance of the different PHA and app features they are built with, allowing us to improve our feature engineering process. After a few cycles of training, evaluation, and improvement, we can launch the best models in production and monitor their performance.

For more complex cases, we employ deep learning. Compared to logistic regression, deep learning is good at capturing complicated interactions between different features and extracting hidden patterns. The millions of apps in Google Play provide a rich dataset, which is advantageous to deep learning.

In addition to our targeted feature engineering efforts, we experiment with many aspects of deep neural networks. For example, a deep neural network can have multiple layers and each layer has several neurons to process signals. We can experiment with the number of layers and neurons per layer to change model behaviors.

We also adopt unsupervised machine learning methods. Many PHAs use similar abuse techniques and tricks, so they look almost identical to each other. An unsupervised approach helps define clusters of apps that look or behave similarly, which allows us to mitigate and identify PHAs more effectively. We can automate the process of categorizing that type of app if we are confident in the model or can request help from a human expert to validate what the model found.

PHAs are constantly evolving, so our models need constant updating and monitoring. In production, models are fed with data from recent apps, which help them stay relevant. However, new abuse techniques and behaviors need to be continuously detected and fed into our machine learning models to be able to catch new PHAs and stay on top of recent trends. This is a continuous cycle of model creation and updating that also requires tuning to ensure that the precision and coverage of the system as a whole matches our detection goals.

Looking forward

As part of Google's AI-first strategy, our work leverages many machine learning resources across the company, such as tools and infrastructures developed by Google Brain and Google Research. In 2017, our machine learning models successfully detected 60.3% of PHAs identified by Google Play Protect, covering over 2 billion Android devices. We continue to research and invest in machine learning to scale and simplify the detection of PHAs in the Android ecosystem.


This work was developed in joint collaboration with Google Play Protect, Safe Browsing and Play Abuse teams with contributions from Andrew Ahn, Hrishikesh Aradhye, Daniel Bali, Hongji Bao, Yajie Hu, Arthur Kaiser, Elena Kovakina, Salvador Mandujano, Melinda Miller, Rahul Mishra, Damien Octeau, Sebastian Porst, Chuangang Ren, Monirul Sharif, Sri Somanchi, Sai Deep Tetali, Zhikun Wang, and Mo Yu.

Calling all Google Play fans: Vote for your favorite content of 2018


For the last several years, Google Play has celebrated the best content in the Store, including top apps, games and movies. This year, we want to hear from you. For the first time, we’re introducing the Fan Favorite category as part of our “Best of Play” yearly list.

Starting today, it’s time for you to vote! Pick the winners from a shortlist of the year’s most loved and trending titles across apps, games and movies, including Quizlet, Candy Crush Friends Saga and Incredibles 2. To select your Fan Favorites of 2018, head over to this page and cast your vote before the polls close on November 26th.

And the fun doesn’t stop there. The Fan Favorite winners, along with the rest of the Best of 2018 picks from our Google Play Editors, will be announced on December 3rd. So stay tuned!

Posted by Brett Bouchard, Global Head of Editorial, Google Play

#IMakeApps: Taking care of resources, from the internet to oceans

Editor’s note: To celebrate the hard work, creativity and entrepreneurial spirit of app makers around the world, we’re featuring founders, product managers, designers and developers from around the world. We’ll showcase their passions and also hear about what they do when they step away from their computers. Meet our next app maker, Jaehyung Daniel Lee, a Korean scuba diver who is passionate about taking care of our resources, whether that’s natural ones like the oceans or human-made ones like internet access, via his EMPO app, which allows people to buy and sell unused mobile data. Check out more #IMakeApps stories on g.co/play/imakeapps.

Jaehyung Daniel Lee has been watching oceanic documentaries on TV with his father since he was a child. He’s always cared deeply about the ocean and taking care of it, especially the cold seas of his country, South Korea. Taking care of our resources is also what inspired his brother’s idea to not let wi-fi go to waste. The result is EMPO, an app to trade unused mobile data.

Where did the idea for the EMPO app come from?

It was actually my little brother William’s idea. He’s a heavy user of mobile data while I can survive with 1 GB every month. One day he asked me if I could sell my leftover data to him, which sparked the idea for the EMPO app. I wanted to build a way for people to share their data-resources. Access to the internet is like water to life in the modern way of life, it’s a basic resource. 

How did you get into technology?

I majored in International Studies: Political Science, which taught me to identify and analyze problems, and then look for solutions. I’m not a software engineer, but that hasn’t prevented me from making apps.

What has been your experience starting the company?

It has given me not only energy and hopes but also insomnia and doubts. It is like riding a roller coaster. There are multiple ups and downs in a day, but I have to find a balance.

What has been your experience launching the app on Android and Google Play?

I think lean operations are a critical element in successful startup cases. Android is the perfect platform to launch because the ten-year-old developer community offers respectable resources and compatibilities. Although I started as a non-engineer, my time with Android and Google Play taught me that building an app on Android Studio is doable even for a political scientist. After a couple of prototypes, I wanted to come up with lines of code and algorithms myself using machine learning, and I began practicing while the app is out there running. As an operator, the sync between Google Ads Universal App Campaigns and the Google Play Console has been the most convenient feature for marketing and collecting meaningful (and unexpected) data at the same time. The Google Play network of users gives me positive pressure to consider product quality as a top priority.

What does your day-to-day look like?

Every morning I swim in cold water for 30 minutes. After breakfast, I join the subway rush to Google for Startups Campus in Seoul, where we launched our EMPO project. At Campus, I plan and analyze day-to-day operations on the app development and business. The most fun part is to interact among our teammates, who give me the energy to move on. There are private booths with whiteboards at Campus, in which we argue, discuss, and come up with solutions, actual solutions! Running the app is pretty hectic; every day we find either technical or operational aspects to improve on. But, making things work is our job, and we are proud of our role. Other than vis-a-vis work with the team, I often meet new people in different startup industries, exchanging advice. My current focus is to improve our app with much better UX and stronger security measures. Our research and development never stops, as we are preparing for a major upgrade before 2019 starts.

How did you get into scuba diving?

I’ve always watched oceanic documentaries with my dad, and at first I got my diver’s licenses so I could see whales in real life. Now I want to contribute to their survival by helping keep the oceans clean; taking care of our resources is very important to me. Scuba diving also helps me disconnect from the busy city life in Seoul and gives me the energy I need to run my app business. The spirit behind EMPO and my scuba diving passion are the same: taking care of our resources, whether that’s internet access or oceans.

Calling all Google Play fans: Vote for your favorite content of 2018

For the last several years, Google Play has celebrated the best content in the Store, including top apps, games and movies. This year, we want to hear from you. For the first time, we’re introducing the Fan Favorite category as part of our “Best of Play” yearly list.

Starting today, it’s time for you to vote! Pick the winners from a shortlist of the year’s most loved and trending titles across apps, games and movies, including Quizlet, Candy Crush Friends Saga and Incredibles 2. To select your Fan Favorites of 2018, head over to this page and cast your vote before the polls close on November 26th.

And the fun doesn’t stop there. The Fan Favorite winners, along with the rest of the Best of 2018 picks from our Google Play Editors, will be announced on December 3rd. So stay tuned!

Celebrating the next generation of game makers on the Google Play Store

Today, the top five finalists of Google Play’s Change The Game Design Challenge are launching their games in the Play Store. While half of mobile game players are women, they only make up 23% of the gaming creator industry. This is what inspired Change The Game to put on a challenge that would bring the next generation of female gamer makers’ games to life. From a shape-shifting young girl navigating through the forest to an artist choosing the right color for their palette—these games were all imagined, written and developed by the winners of the Design Challenge.

Over the past few months, the winners have worked in partnership with Girls Make Games’ development studio LearnDistrict and a team of artists, programmers and producers to build their games.

Want to know more about the top five finalists and their games? Learn about their inspiring stories below:

Grand Prize Winner: Christine, 17 years old, from Vancouver, Washington

Christine is the creator of “Mazu,” a side-scrolling platform game about a young shapeshifter’s journey through a danger-infested forest. As a long time gamer, Christine noticed early on the clear distinction between games made for girls and games made for boys. “As an aspiring artist in the gaming industry, I don’t want to repeat this cycle of gender-based pandering in the future,” says Christine. Her goal in developing a game was to create a memorable challenge that could be enjoyed by everyone, regardless of gender.

Dakota, 14 years old, from Encino, California

Dakota’s game, “EcoVerse,” is a series of minigames to clean, plant, and bring animal life to planets as part of the Galactic Restoration Team. “Many games center around destruction and tearing things down—what if I made a game about building things up and rebirth?” This question and a passion for living eco-consciously inspired Dakota to create “Ecoverse.”

Lily, 14 years old, from Poplar, Wisconsin

Lily was inspired by emotions and eyes—two windows into the soul—to create “The Other Realm,” a puzzle adventure game focused on self-identity. “The game is about looking at things through a different lens,” she says, adding that “eyes can hold emotion that you might not necessarily notice.” After being named a top five finalist, Lily is now exploring game design as a field of study to pursue.

Erin, 18 years old, from Freehold, New Jersey

Erin created “Symphony,” a rhythm game which explores music as a means for a young girl to connect with her deceased grandfather to showcase the healing properties of music. Erin says that through practice and love of craft “anyone can become their own self-made prodigy.” After participating in the Design Challenge, Erin is now interested in entering the gaming industry.

Lauren, 17 years old, from Birmingham, Alabama

Lauren’s game, “Palette,” simulates the eternal struggle of every artist: finding the right color. Reveal famous paintings throughout history by mixing different colors from a palette to match a target color. According to Lauren, “Change The Game taught me that the game industry embraces people with all interests, from musicians to programmers.” Lauren now plans on pursuing a future in game development.

Learn more about Change The Game and our mission to empower the next generation of game-makers.