Recommending improvements to and detecting defects within applications

• US 9,449,042 B1
• Filed: 06/25/2013
• Issued: 09/20/2016
• Est. Priority Date: 06/25/2013
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Ramirez, Ernist Robert, Roque, Miguel Azancot, Rubenson, Aaron Curtis, Evans, Ethan Zane, Lavin, Christopher Lawrence, Markley, David Allen, Frazzini, Michael Anthony, Okereke, Mekka Chibuisi, Paleja, Ameesh, Bianco, Antonio, Adkins, James Newton III
• Primary Examiner(s): Stevens, Robert
• Application Number:
  US13/926,234
• Time in Patent Office: 1,183 Days
• Field of Search: 707/758, 707/736
• US Class Current: 1/1
• CPC Class Codes: G06F 8/36 Software reuse

Abstract

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.

Claims

1. A non-transitory computer-readable medium having a computer-executable program stored thereon that, when executed by at least one computing device, configures the at least one computing device to perform operations comprising:

identifying an application in an application marketplace;

executing an instance of the application;

performing, in response to executing the instance, an analysis of a set of code fragments employed by the instance;

performing, in response to executing the instance, an analysis of a set of device resources employed by the instance;

determining a resource consumption profile employed by the application based at least in part upon the analysis of the set of code fragments and the analysis of the set of device resources;

generating an application fingerprint based at least in part upon the analysis of the set of code fragments, the analysis of the set of device resources, and the resource consumption profile, the application fingerprint comprising information about the set of code fragments, the set of device resources, and the resource consumption profile;

executing at least one other instance of at least one other application;

generating at least one other application fingerprint based at least in part upon an analysis of at least one other resource consumption profile employed by the at least one other application;

identifying at least one improvement to the application based at least in part upon an analysis of the application fingerprint by comparing a performance metric in the application fingerprint to a corresponding performance metric in the at least one other application fingerprint;

determining that a specific feature is attributable to the corresponding performance metric;

generating a recommendation associated with the at least one improvement, wherein the recommendation comprises incorporating the specific feature that is attributable to the corresponding performance metric into the application; and

incorporating the specific feature that is attributable to the corresponding metric into the application by modifying a code fragment of the application.

2. The non-transitory computer-readable medium of claim 1, wherein the application fingerprint is generated based at least in part upon usage data corresponding to usage of the application by a plurality of users associated with the application marketplace.

3. The non-transitory computer-readable medium of claim 1, wherein the application fingerprint is generated based at least in part upon crash data associated with the application.

4. The non-transitory computer-readable medium of claim 1, wherein the corresponding performance metric in the at least one other application indicates a better performance than the performance metric in the application.

5. The non-transitory computer-readable medium of claim 1, wherein modifying the code fragment of the application comprises replacing the code fragment with another code fragment.

6. A system, comprising:

at least one computing device; and

at least one service executable in the at least one computing device, the at least one service configured to cause the at least one computing device to;

execute an instance of an application;

perform, in response to executing the instance, an analysis of a set of code fragments employed an application and an analysis of a set of device resources employed by the instance;

obtain an application fingerprint for the application, the application fingerprint based at least in part upon the analysis of the set of code fragments employed by the instance and the analysis of the set of device resources employed by the instance;

execute at least one other instance of at least one other application;

obtain at least one other application fingerprint based at least in part upon an analysis of at least one other set of code fragments employed by the at least one other application and an analysis of at least one other set of device resources employed by the at least one other application;

perform an analysis of the application fingerprint of the application to identify at least one improvement to the application based at least in part upon the analysis of the application fingerprint by comparing a performance metric in the application fingerprint to a corresponding performance metric in the at least one other application fingerprint associated with the at least one other application;

determine that a specific feature is attributable to the corresponding performance metric;

generate a recommendation for the at least one improvement to the application, wherein the recommendation comprises incorporating the specific feature that is attributable to the corresponding performance metric into the application; and

incorporate the specific feature that is attributable to the corresponding performance metric into the application by modifying a code fragment of the application.

7. The system of claim 6, wherein the performance metric comprises at least one of:

application sales data, a number of application downloads, or advertising revenue data.

8. The system of claim 6, wherein the performance metric comprises a video frame rate associated with respective video rendered by the application and the at least one other application.

9. The system of claim 6, wherein the performance metric comprises an average usage time associated with the application and the at least one other application by a plurality of users associated with an application marketplace.

10. The system of claim 6, wherein the performance metric comprises a user perceivable lag metric associated with the application and the at least one other application.

11. The system of claim 6, wherein the specific feature comprises at least one of:

a social networking component or a location-based component.

12. The system of claim 6, wherein the at least one service that causes the at least one computing device to generate the recommendation for the at least one improvement further causes the at least one computing device to determine whether a usage rate of the specific feature meets a predefined threshold.

13. The system of claim 6, wherein the at least one service that causes the at least one computing device to generate the recommendation for the at least one improvement further causes the at least one computing device to:

obtain usage data associated with execution of the application by a plurality of users of an application marketplace; and

identify the at least one improvement based at least in part upon the usage data.

14. The system of claim 13, wherein the at least one service that causes the at least one computing device to identify the at least one improvement based at least in part upon the usage data further causes the at least one computing device to:

identify crash data in the usage data;

identify a code fragment associated with at least a threshold percentage of crashes reflected by the crash data;

determine whether the at least one other application employs the code fragment associated with the at least a threshold percentage; and

wherein the at least one improvement comprises a recommendation based at least in part upon the code fragment associated with the at least a threshold percentage.

15. The system of claim 13, wherein the at least one service that causes the at least one computing device to identify the at least one improvement based at least in part upon the usage data further causes the at least one computing device to:

identify whether the application crashed in an initial execution in more than a threshold amount of initial executions reflected by the usage data;

identify a code fragment associated with at least a threshold percentage of crashes reflected by crash data;

determine whether another application having at least one feature in common to the application employs the code fragment associated with the at least a threshold percentage; and

wherein the at least one improvement comprises a recommendation based at least in part upon the code fragment associated with the at least a threshold percentage.

16. A method, comprising:

executing, via at least one computing device, an instance of an application;

performing, via the at least one computing device, in response to executing the instance, an analysis of a set of code fragments employed by the instance;

obtaining, via the at least one computing device, an application fingerprint associated with the application based at least in part upon the analysis of the set of code fragments employed by the application, the application fingerprint encoding respective identifiers for the set of code fragments employed by the application;

determining, via the at least one computing device, that the application includes a defect based at least in part on an analysis of the application fingerprint, wherein the analysis of the application fingerprint comprises comparing a performance metric in the application fingerprint to a corresponding performance metric in at least one other application fingerprint associated with another application; and

implementing, via the at least one computing device, an improvement to the application in response to determining that the application includes the defect, wherein implementing the improvement comprises modifying a code fragment of the application.

17. The method of claim 16, further comprising offering, via the at least one computing device, the application in an application marketplace in response to determining that the defect has been corrected.

18. The method of claim 16, wherein the application fingerprint further encodes respective identifiers of a set of application resources employed by the application, and the method further comprises:

determining, via the at least one computing device, at least one of the set of application resources or the set of code fragments based at least in part on the application fingerprint; and

determining, via the at least one computing device, whether the set of application resources or the set of code fragments are associated with the defect.

19. The method of claim 18, further comprising:

determining, via the at least one computing device, that a plurality of other applications are associated with the defect based at least in part on usage data corresponding to the other application;

identifying, via the at least one computing device, a code fragment employed by the other applications based at least in part on a plurality of application fingerprints corresponding to the other applications; and

associating, via the at least one computing device, the code fragment with the defect.

20. The method of claim 16, wherein the improvement comprises at least one of:

instrumenting the application to facilitate logging relative to a call, sending a notification of the defect to a party associated with the application, refraining from offering the application in an application marketplace or associating a defect indicator with the application in an application marketplace.

21. The method of claim 16, wherein the improvement comprises at least one of modifying object code of the application or modifying source code of the application.

22. The method of claim 16, wherein the improvement further comprises replacing the code fragment of the application with a second code fragment.

23. The method of claim 22, wherein the second code fragment corresponds to a non-defective version of the first code fragment.

24. The method of claim 16, further comprising flagging, via the at least one computing device, the application in an application marketplace by associating the application with a defect indicator.