Abstract: The present disclosure describes methods, systems, and computer program products for providing additional stack trace information for time-based sampling (TBS) in asynchronous execution environments. One computer-implemented method includes determining whether time-based sampling is activated to capture a time-based sampling data during execution of a JavaScript function; in response to determining that the time-based sampling is activated to capture the time-based sampling data, determining whether a callback stack trace is active; in response to determining that the callback stack trace is active, loading the callback stack trace; retrieving a current stack trace of the JavaScript function; and saving the loaded callback stack trace and the current stack trace of the JavaScript function as the time-based sampling data.

Abstract: The present disclosure describes methods, systems, and computer program products for providing additional stack trace information for time-based sampling (TBS) in asynchronous execution environments. One computer-implemented method includes determining whether time-based sampling is activated to capture a time-based sampling data during execution of a JavaScript function; in response to determining that the time-based sampling is activated to capture the time-based sampling data, determining whether a callback stack trace is active; in response to determining that the callback stack trace is active, loading the callback stack trace; retrieving a current stack trace of the JavaScript function; and saving the loaded callback stack trace and the current stack trace of the JavaScript function as the time-based sampling data.

Abstract: The present disclosure describes methods, systems, and computer program products for providing additional stack trace information for time-based sampling (TBS) in asynchronous execution environments. One computer-implemented method includes determining whether time-based sampling is activated to capture a time-based sampling data during execution of a JavaScript function; in response to determining that the time-based sampling is activated to capture the time-based sampling data, determining whether a callback stack trace is active; in response to determining that the callback stack trace is active, loading the callback stack trace; retrieving a current stack trace of the JavaScript function; and saving the loaded callback stack trace and the current stack trace of the JavaScript function as the time-based sampling data.

Abstract: The present disclosure describes methods, systems, and computer program products for providing additional stack trace information for time-based sampling (TBS) in asynchronous execution environments. One computer-implemented method includes determining whether time-based sampling is activated to capture a time-based sampling data during execution of a JavaScript function; in response to determining that the time-based sampling is activated to capture the time-based sampling data, determining whether a callback stack trace is active; in response to determining that the callback stack trace is active, loading the callback stack trace; retrieving a current stack trace of the JavaScript function; and saving the loaded callback stack trace and the current stack trace of the JavaScript function as the time-based sampling data.

Abstract: The present disclosure describes methods, systems, and computer program products for providing additional stack trace information for time-based sampling (TBS) in asynchronous execution environments. One computer-implemented method includes determining whether time-based sampling is activated to capture a time-based sampling data during execution of a JavaScript function; in response to determining that the time-based sampling is activated to capture the time-based sampling data, determining whether a callback stack trace is active; in response to determining that the callback stack trace is active, loading the callback stack trace; retrieving a current stack trace of the JavaScript function; and saving the loaded callback stack trace and the current stack trace of the JavaScript function as the time-based sampling data.

Abstract: The present disclosure describes methods, systems, and computer program products for providing additional stack trace information for time-based sampling (TBS) in asynchronous execution environments. One computer-implemented method includes determining whether time-based sampling is activated to capture a time-based sampling data during execution of a JavaScript function; in response to determining that the time-based sampling is activated to capture the time-based sampling data, determining whether a callback stack trace is active; in response to determining that the callback stack trace is active, loading the callback stack trace; retrieving a current stack trace of the JavaScript function; and saving the loaded callback stack trace and the current stack trace of the JavaScript function as the time-based sampling data.

Abstract: The present disclosure describes methods, systems, and computer program products for providing additional stack trace information for time-based sampling (TBS) in asynchronous execution environments. One computer-implemented method includes determining whether time-based sampling is activated to capture a time-based sampling data during execution of a JavaScript function; in response to determining that the time-based sampling is activated to capture the time-based sampling data, determining whether a callback stack trace is active; in response to determining that the callback stack trace is active, loading the callback stack trace; retrieving a current stack trace of the JavaScript function; and saving the loaded callback stack trace and the current stack trace of the JavaScript function as the time-based sampling data.

Abstract: The present disclosure describes methods, systems, and computer program products for providing additional stack trace information for time-based sampling (TBS) in asynchronous execution environments. One computer-implemented method includes determining whether time-based sampling is activated to capture a time-based sampling data during execution of a JavaScript function; in response to determining that the time-based sampling is activated to capture the time-based sampling data, determining whether a callback stack trace is active; in response to determining that the callback stack trace is active, loading the callback stack trace; retrieving a current stack trace of the JavaScript function; and saving the loaded callback stack trace and the current stack trace of the JavaScript function as the time-based sampling data.

Abstract: A thread dump viewer presents information for a plurality of threads. The information about the threads can be presented in a table view. The thread dump viewer can allow the user to expand a thread in the table view to see a stack trace for the thread. The stack trace may include information for objects that are associated with other threads. For example, the thread may be waiting on a lock object locked by another thread. The thread dump viewer can present information about the associated thread based on a user interaction. This process can be continued iteratively, allowing the stack traces of interrelated threads to be seen to arbitrary depths.

Abstract: A method for profiling an application on a virtual machine is provided. A series of analysis steps to be performed on profiled data can be created. The series of analysis steps can be saved as a report specification. A back-end profiler can then be caused to perform profiling on the application. Profiled data can be received from the back-end profiler. The profiled data can be stored as a model. The model can then be adapted based on the series of analysis steps from the report specification. Output data can be generated based on the adapted model. Finally, the output data is displayed to a user.

Abstract: Systems and methods are provided to automatically analyze performance of an automatic memory management system. One example embodiment involves automatically gathering, using at least one processor of the server, garbage collection information associated with the garbage collection process and storing the garbage collection information in a garbage collection output file of a file system. The garbage collection output file may be analyzed to identify a plurality of flags associated with a performance of the server system that does not meet one or more performance thresholds. In certain embodiments, a first flag of the plurality of flags is associated with a first portion of the garbage collection information, and a second flag of the plurality of flags is associated with a second portion of the garbage collection information that is different from the first portion of the garbage collection information.

Abstract: In an example embodiment, a communication is received from a software tool. The communication may be intended for a virtual machine and may contain an identification of a virtual file. A virtual file can then be retrieved using the identification. The virtual file defines a file socket, the file socket providing a communications channel for stream-based communications. Then the file socket can be established as a communications channel between the software tool and the virtual machine based on the virtual file.

Abstract: A thread dump analysis tool analyzes a series of thread dumps and identifies one or more potential problems in the application from which the thread dumps were generated. Hints regarding the potential problems are presented. The hints can be generated based on relative values generated by analysis of sequential dumps. The hints may be hints that could not be generated by analysis of a single thread dump. Other hints may be hints that are enhanced by analysis of multiple thread dumps, whose importance is made clearer by appearance in multiple thread dumps, or that are unchanged in detection, importance, or both, by the use of multiple thread dumps. The hints can then be presented in order of importance. Additionally or alternatively, hints below a certain threshold of importance can be hidden.

Abstract: A method for profiling an application on a virtual machine is provided. A series of analysis steps to be performed on profiled data can be created. The series of analysis steps can be saved as a report specification. A back-end profiler can then be caused to perform profiling on the application. Profiled data can be received from the back-end profiler. The profiled data can be stored as a model. The model can then be adapted based on the series of analysis steps from the report specification. Output data can be generated based on the adapted model. Finally, the output data is displayed to a user.

Abstract: Systems and methods are provided to automatically analyze performance of an automatic memory management system. One example embodiment involves automatically gathering, using at least one processor of the server, garbage collection information associated with the garbage collection process and storing the garbage collection information in a garbage collection output file of a file system. The garbage collection output file may be analyzed to identify a plurality of flags associated with a performance of the server system that does not meet one or more performance thresholds. In certain embodiments, a first flag of the plurality of flags is associated with a first portion of the garbage collection information, and a second flag of the plurality of flags is associated with a second portion of the garbage collection information that is different from the first portion of the garbage collection information.

Abstract: A thread dump analysis tool analyzes a series of thread dumps and identifies one or more potential problems in the application from which the thread dumps were generated. Hints regarding the potential problems are presented. The hints can be generated based on relative values generated by analysis of sequential dumps. The hints may be hints that could not be generated by analysis of a single thread dump. Other hints may be hints that are enhanced by analysis of multiple thread dumps, whose importance is made clearer by appearance in multiple thread dumps, or that are unchanged in detection, importance, or both, by the use of multiple thread dumps. The hints can then be presented in order of importance. Additionally or alternatively, hints below a certain threshold of importance can be hidden.

Abstract: A thread dump viewer presents information for a plurality of threads. The information about the threads can be presented in a table view. The thread dump viewer can allow the user to expand a thread in the table view to see a stack trace for the thread. The stack trace may include information for objects that are associated with other threads. For example, the thread may be waiting on a lock object locked by another thread. The thread dump viewer can present information about the associated thread based on a user interaction. This process can be continued iteratively, allowing the stack traces of interrelated threads to be seen to arbitrary depths.

Abstract: In an example embodiment, a communication is received from a software tool. The communication may be intended for a virtual machine and may contain an identification of a virtual file. A virtual file can then be retrieved using the identification. The virtual file defines a file socket, the file socket providing a communications channel for stream-based communications. Then the file socket can be established as a communications channel between the software tool and the virtual machine based on the virtual file.

Abstract: A system and computer-implemented method for determining a runtime of a thread of an application. Synchronization events for a first thread of an application executing on the computer system are received, the synchronization events including at least a first synchronization event and a second synchronization event for the first thread. A first difference between a synchronization event timestamp of the first synchronization event and the synchronization event timestamp of the second synchronization event is calculated. A second difference between an accumulated timestamp of the first synchronization event and the accumulated timestamp of the second synchronization event is calculated. A runtime of the first thread of the application is calculated as a difference between the first difference and the second difference.

Abstract: The present disclosure describes methods, systems, and computer program products for providing remote debugging of a cloud application across a wide area network. A method includes transmitting, from a remote communication device to a cloud computing device, instructions to adjust a running application to a debugging mode; receiving, at the remote communication device from a server coupled to the cloud, aggregated thread data in a data packet by using a second debugging data protocol different from the Java Debug Wire Protocol; receiving a debugging command and applying the debugging command to the cloud application running in the debugging mode.