Predictive app failure mitigation and remediation

1 . A computer-implemented method, comprising:

executing a data communication code in conjunction with an application programming interface (API) configured to be in communication with a code of a crash analysis library executing at a first device, to receive a first usage data relating to a behavior of an app during an execution on the first device;

extracting from the first usage data a feature related to the behavior;

clustering, based on the feature, the first usage data into a cluster with a second usage data from a second device;

determining that a user input data in the first usage data indicates a state in the app such that a set of additional user input steps after the state will result in a crash, the crash comprising an unexpected termination of the app;

obtaining, from a remote server in response to sending the cluster to the remote server, resolution data related to the feature, wherein the resolution data comprises at least a state of development of code related to the behavior and a workaround configured to operate on the first device and avoid the crash;

predicting, as a score, and by using the first usage data and the resolution data as inputs to a trained prediction model, a likelihood of the crash of the app on the first device; and

outputting, causing the crash analysis library to present an interactive user interface (UI) on the first device, the score, the state of development of the code, and the workaround.

2 . The computer-implemented method of claim 1 , further comprising:

forecasting a resolution timeline using the resolution data and the cluster, wherein the resolution data corresponds to the cluster; and

outputting to the first device, the resolution timeline.

3 . The computer-implemented method of claim 2 , further comprising:

receiving an update to the resolution data;

forecasting a new resolution timeline; and

sending to the first device, the new resolution timeline.

4 . The computer-implemented method of claim 2 , wherein the workaround is configured to operate on the first device until an expiry of the resolution timeline.

5 . The computer-implemented method of claim 1 , further comprising:

constructing a first feature vector corresponding to the feature using the first usage data;

constructing a second feature vector corresponding to the feature using the second usage data;

determining, as a part of the clustering, that the first feature vector is within a threshold degree of similarity of the second feature vector;

transmitting the cluster to a development environment, wherein the resolution data is responsive to the transmitting and corresponds to the cluster.

6 . The computer-implemented method of claim 1 , wherein the feature of the first usage data comprises navigation data, wherein the navigation data comprises a path data indicative of a user's navigation path in the app, and wherein the navigation data is up to a point in the app such that a set of additional navigation steps after the path will result in the crash.

7 . The computer-implemented method of claim 6 , wherein the navigation data comprises a sequence of screen transitions in the app, each screen transition having a corresponding time stamp.

8 . The computer-implemented method of claim 1 , wherein the user input data comprises at least one input on the UI of the app.

9 . The computer-implemented method of claim 8 , wherein the user input data comprises a sequence of button taps on the UI, each button tap having a corresponding time stamp.

10 . A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a set of one or more processors to cause the set of one or more processors to perform operations comprising:

executing a data communication code in conjunction with an application programming interface (API) configured to be in communication with a code of a crash analysis library executing at a first device, to receive a first usage data relating to a behavior of an app during an execution on the first device;

extracting from the first usage data a feature related to the behavior;

clustering, based on the feature, the first usage data into a cluster with a second usage data from a second device;

determining that a user input data in the first usage data indicates a state in the app such that a set of additional user input steps after the state will result in a crash, the crash comprising an unexpected termination of the app;

obtaining, from a remote server in response to sending the cluster to the remote server, resolution data related to the feature, wherein the resolution data comprises at least a state of development of code related to the behavior and a workaround configured to operate on the first device and avoid the crash;

predicting, as a score, and by using the first usage data and the resolution data as inputs to a trained prediction model, a likelihood of the crash of the app on the first device; and

outputting, causing the crash analysis library to present an interactive user interface (UI) on the first device, the score, the state of development of the code, and the workaround.

11 . The computer program product of claim 10 , the operations further comprising:

forecasting a resolution timeline using the resolution data and the cluster, wherein the resolution data corresponds to the cluster; and

outputting to the first device, the resolution timeline.

12 . The computer program product of claim 11 , the operations further comprising:

receiving an update to the resolution data;

forecasting a new resolution timeline; and

sending to the first device, the new resolution timeline.

13 . The computer program product of claim 11 ,

wherein the workaround is configured to operate on the first device until an expiry of the resolution timeline.

14 . The computer program product of claim 10 , the operations further comprising:

constructing a first feature vector corresponding to the feature using the first usage data;

constructing a second feature vector corresponding to the feature using the second usage data;

determining, as a part of the clustering, that the first feature vector is within a threshold degree of similarity of the second feature vector; and

transmitting the cluster to a development environment, wherein the resolution data is responsive to the transmitting and corresponds to the cluster.

15 . The computer program product of claim 10 , wherein the feature of the first usage data comprises navigation data, wherein the navigation data comprises a path data indicative of a user's navigation path in the app, and wherein the navigation data is up to a point in the app such that a set of additional navigation steps after the path will result in the crash.

16 . The computer program product of claim 15 , wherein the navigation data comprises a sequence of screen transitions in the app, each screen transition having a corresponding time stamp.

17 . The computer program product of claim 10 , wherein the user input data comprises at least one input on the UI of the app.

18 . The computer program product of claim 10 , wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system.

19 . The computer program product of claim 10 , wherein the program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, the operations further comprising:

metering a use of the program instructions associated with the request; and

generating an invoice based on the use.

20 . A computer system comprising a set of one or more processors and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the set of one or more processors to cause the set of one or more processors to perform operations comprising:

executing a data communication code in conjunction with an application programming interface (API) configured to be in communication with a code of a crash analysis library executing at a first device, to receive a first usage data relating to a behavior of an app during an execution on the first device;

extracting from the first usage data a feature related to the behavior;

clustering, based on the feature, the first usage data into a cluster with a second usage data from a second device;

determining that a user input data in the first usage data indicates a state in the app such that a set of additional user input steps after the state will result in a crash, the crash comprising an unexpected termination of the app;

obtaining, from a remote server in response to sending the cluster to the remote server, resolution data related to the feature, wherein the resolution data comprises at least a state of development of code related to the behavior and a workaround configured to operate on the first device and avoid the crash;

predicting, as a score, and by using the first usage data and the resolution data as inputs to a trained prediction model, a likelihood of the crash of the app on the first device; and

outputting, causing the crash analysis library to present an interactive user interface (UI) on the first device, the score, the state of development of the code, and the workaround.