Amazon Technologies, Inc.

Miguel Azancot Roque

Ethan Zane Evans

David Allen Markley

Michael Anthony Frazzini

Ameesh Paleja

Christopher Lawrence Lavin

James Newton Adkins, Iii

Aaron Curtis Rubenson

Mekka Chibuisi Okereke

Mihir Kumar Choudhary

Arindam Bhattacharya

Paul Allan Ryder

Jason Shih Shen Chein

Samuel Jared Alston

Harmeet Singh Gorwara

Antonio Bianco

Ernist Robert Ramirez

Disclosed are various embodiments for application monetization based on application fingerprinting and lifestyle fingerprinting. Application fingerprints are determined for multiple applications. A respective application fingerprint is generated based at least in part on a static analysis, a dynamic analysis, and a behavioral analysis, and is indicative of one or more features of an application. Lifestyle fingerprints are determined for multiple users. A respective lifestyle fingerprint is indicative of one or more preferences of a user. An action is implemented to market a selected application based at least in part on a correlation between the lifestyle fingerprints and a set of application fingerprints that are determined to be similar to the application fingerprint of the selected application.

Application monetization based on application and lifestyle fingerprinting

Disclosed are various embodiments for developing versions of applications based on application fingerprinting. For example, an application may be ported from one operating system platform to another, or an application may be modified to be compatible with a specific device. In one embodiment, an application fingerprint is received for an application. The application fingerprint may be generated based at least in part on a static analysis of the application and a dynamic analysis of the application. A device that is incompatible with the application based at least in part on the application fingerprint may be determined. A modification to the application to make the application compatible with the device may be determined.

Developing versions of applications based on application fingerprinting

Technologies are disclosed for generating and publishing multi-platform application binaries from hosted websites or website source code. A developer can provide source code for a website or a link to a hosted website and identify target platforms for which application binaries should be created. The website is then modified for optimized presentation on devices on the specified target platforms. For example, page layouts, menu styles, image resolutions, and other aspects of the website can be modified for presentation on a mobile device, a tablet, a set top box, or other type of device. Source code is then generated for the target platforms that includes a web view component for rendering the website. A cross-platform compilation service then compiles the source code to generate native applications for the target platforms. The generated applications can then be provided to the developer or automatically submitted to application stores associated with the target platforms.

Generating and publishing applications for multiple platforms

In various embodiments, static, dynamic, and behavioral analyzes may be performed on an application. A set of software libraries employed by the application may be determined. A set of device resources employed by the application may be determined. An application fingerprint is generated for the application. The application fingerprint encodes identifiers for the set of software libraries and identifiers for the set of device resources. Improvements can be recommended based upon an analysis of the application fingerprint.

Recommending improvements to and detecting defects within applications

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Seattle, WA, United States of America

Miguel Azancot Roque