An application analysis computer obtains reports from user terminals identifying operational states of instances of an application being processed by the user terminals. Sequences of the operational states that the instances of the application have transitioned through while being processed by the user terminals are identified. Common operational states that occur in a plurality of the sequences are identified. For each of the common operational states, a frequency of occurrence of the common operational state is determined. For each state transition between the common operational states in the sequences, a frequency of occurrence of the state transition is determined. State predictive metrics are generated based on the frequencies of occurrence of the common operational states and the frequencies of occurrence of the state transitions. The state predictive metrics are communicated, such as to an application server to control access to the application by user terminals.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer program product, comprising: a non-transitory computer readable storage medium comprising computer readable program code embodied in the medium that when executed by a processor of an application analysis computer causes the processor to perform operations comprising: obtaining reports from user terminals, the reports identifying operational states of instances of an application being processed by the user terminals; identifying sequences of the operational states that the instances of the application have transitioned through while being processed by the user terminals; identifying common operational states that occur in a plurality of the sequences; for each of the common operational states, determining a frequency of occurrence of the common operational state; for each state transition between the common operational states in the sequences, determining a frequency of occurrence of the state transition; generating state predictive metrics based on the frequencies of occurrence of the common operational states and the frequencies of occurrence of the state transitions; and communicating the state predictive metrics. 2. The computer program product of claim 1, wherein the communicating the state predictive metrics, comprises: communicating the state predictive metrics to an application server containing the application. 3. The computer program product of claim 1, wherein the operations further comprise: communicating commands to the user terminals to control processing of the application based on the state predictive metrics. 4. The computer program product of claim 1, wherein the generating state predictive metrics based on the frequencies of occurrence of the common operational states and the frequencies of occurrence of the state transitions, comprises: determining state probabilities based on the frequencies of occurrence of the common operational states; determining state transition probabilities based on the frequencies of occurrence of the state transitions; and generating the state predictive metrics based on the state probabilities and the state transition probabilities. 5. The computer program product of claim 4, wherein the operations further comprise: estimating, based on the state probabilities and the state transition probabilities, a number of times one of the sequences of the operational states will occur when a defined number of further instances of the application are processed by user terminals; and generating the state predictive metrics based on the estimate of the number of times. 6. The computer program product of claim 4, wherein the operations further comprise: identifying an early-terminated sequence of the operational states that did not reach a defined operational state before instances of the application terminated processing; generating, based on the state probabilities and the state transition probabilities relating to the early-terminated sequence of the operational states, a statistical probability that the early-terminated sequence of the operational states will occur when further instances of the application are processed by user terminals; and generating an alert message when the statistical probability satisfies a defined threshold value. 7. The computer program product of claim 4, wherein the determining state transition probabilities based on the frequencies of occurrence of the state transitions, comprises: assigning a threshold state transition probability to any operational states that have not been identified as occurring in the plurality of the sequences. 8. The computer program product of claim 4, wherein the determining state transition probabilities based on the frequencies of occurrence of the state transitions, comprises: for each of the common operational states, identifying any other operational states that are subsequently transitioned to from the common operational state and counting occurrences of transitions from the common operational state to each of the any other operational states; determining the state transition probabilities based on the counted occurrences. 9. The computer program product of claim 4, wherein the operations further comprise: determining operational state durations based on content of the reports, each of the operational state durations indicating a time duration during which one of the instances of the application remained within one of the operational states; generating the state predictive metrics based on the operational state durations, the state probabilities, and the state transition probabilities. 10. The computer program product of claim 9, wherein the generating the state predictive metrics based on the operational state durations, the state probabilities, and the state transition probabilities, comprises: determining probability time metrics for the sequences of the operational states based on a combination of the operational state durations, the state probabilities, and the state transition probabilities; and generating the state predictive metrics based on the probability time metrics. 11. The computer program product of claim 10, wherein the operations further comprise: identifying one of the sequences of the operational states having a probability time metric that satisfies a defined rule; and selectively performing the communicating state predictive metrics associated with the one of the sequences of the operational states based on the probability time metric satisfying the defined rule. 12. The computer program product of claim 10, wherein the operations further comprise: ranking the sequences of the operational states based on the probability time metrics for the sequences of the operational states. 13. The computer program product of claim 10, wherein the operations further comprise: obtaining weight values for the operational states; combining the weight values for the operational states with the state transition probabilities and the state probabilities to generate weighted metrics; and determining the probability time metrics for the sequences of the operational states based on a combination of the weighted metrics, the operational state durations, the state probabilities, and the state transition probabilities. 14. The computer program product of claim 13, wherein the obtaining weight values for the operational states, comprises: querying a repository containing weight values of operational states of a plurality of applications using an identifier for the application to obtain the weight values for the operational states of the application. 15. The computer program product of claim 10, wherein the operations further comprise: identifying an early-terminated sequence of the operational states that did not reach a defined operational state before instances of the application terminated processing by user terminals; and selectively performing the communicating state predictive metrics associated with the early-terminated sequence of the operational states based on identification of the early-terminated sequence. 16. A method comprising: performing operations as follows on a processor of an application analysis computer: obtaining reports from user terminals, the reports identifying operational states of instances of an application being processed by the user terminals; identifying sequences of the operational states that the instances of the application have transitioned through while being processed by the user terminals; identifying common operational states that occur in a plurality of the sequences; for each of the common operational states, determining a frequency of occurrence of the common operational state; for each state transition between the common operational states in the sequences, determining a frequency of occurrence of the state transition; generating state predictive metrics based on the frequencies of occurrence of the common operational states and the frequencies of occurrence of the state transitions; and communicating the state predictive metrics. 17. The method of claim 16, wherein the generating state predictive metrics based on the frequencies of occurrence of the common operational states and the frequencies of occurrence of the state transitions, comprises: determining state probabilities based on the frequencies of occurrence of the common operational states; determining state transition probabilities based on the frequencies of occurrence of the state transitions; and generating the state predictive metrics based on the state probabilities and the state transition probabilities. 18. The method of claim 17, wherein the determining state transition probabilities based on the frequencies of occurrence of the state transitions, comprises: for each of the common operational states, identifying any other operational states that are subsequently transitioned to from the common operational state and counting occurrences of transitions from the common operational state to each of the any other operational states; determining the state transition probabilities based on the counted occurrences. 19. The method of claim 17, wherein the operations further comprise: determining operational state durations based on content of the reports, each of the operational state durations indicating a time duration during which one of the instances of the application remained within one of the operational states; generating the state predictive metrics based on the operational state durations, the state probabilities, and the state transition probabilities. 20. The method of claim 19, wherein the operations further comprise: obtaining weight values for the operational states; combining the weight values for the operational states with the state transition probabilities and the state probabilities to generate weighted metrics; and determining the probability time metrics for the sequences of the operational states based on a combination of the weighted metrics, the operational state durations, the state probabilities, and the state transition probabilities.