Described herein are systems, apparatus, methods and computer program products for implementing a self executing and self disposing signal for an imperative programming language. The self executing and self disposing signal may be utilized by a ReactiveX specification programming language. The signal may be executed before it is observed by an external observer, reducing load times. Additionally, the signal may allow for multiple observers to observe the output of the signal at the same time and may be self disposing to prevent memory leaks.

Embodiments of the invention are directed to active detection and mitigation of anomalies in program code construction interfaces. The system provides a proactive plug-in with a dynamic machine learning (ML) anomaly detection model cloud component structured to dynamically detect architectural flaws in program code in real-time in a user coding interface. In particular, the system activates a machine learning (ML) anomaly detection plug-in for dynamically analyzing the first technology program code being constructed in the user coding interface. Moreover, the system modifies, via the ML anomaly detection plug-in, the user coding interface to embed interface elements associated with the one or more flaws in the first technology program code detected by the ML anomaly detection model cloud component.

An improved SDK includes a set of APIs and a crash handler registered with the operating system. Each API is an interface accessible by a computer software application. Up on entrance, each API determines the current thread identifier, and inserts it into a list if it is not already in the list. Each thread identifier corresponds to an API call counter, which is incremented by one at the entrance and decremented by one at the exit point of the API. The SDK also records the identifier of the thread it creates for callback functions. When a crash occurs, the crash handler is executed. It determines that the crash is related to a callback interface if the crash thread identifier matches the callback thread identifier. The crash is determined to be caused by the SDK if the API call counter corresponding to the crash thread identifier is greater than zero.

A source code correction assistance apparatus is configured to include a storage device that stores an updated source code, and an arithmetic operational device that generates, as an evaluation code template of the updated source code, a template including a conditional branch sentence related to each case of success or failure of an input condition, notifies an evaluator terminal of a request to create an evaluation code based on the template, controls an access to the updated source code by the evaluator, receives editing by the evaluator on the conditional branch sentence in the template, generates a list of input values for executing all control paths of the evaluation code after the editing, and generates an evaluation code driver that automatically executes the evaluation code by inputting the input value.

Embodiments disclosed herein provide for methods and systems for providing an enterprise synthetic monitoring framework, wherein the enterprise synthetic monitoring framework is configured to provide exhaustive end-to-end monitoring for a variety of applications and workflows including those that are browser and non-browser based, those that are implemented on mobile devices, and those that are implemented utilizing native protocols.

Devices at different geolocations are configured to determine and share information regarding execution of an application under various conditions. Data determined by a user device includes private information, such as screen capture data, location data, or information about the user. The user device processes the data locally, such as by determining performance metrics or other characteristics of execution of the application, and sends this information to a server. The data sent to the server excludes the private information. The server determines additional data associated with execution of the application by devices other than the user device, which may include screen capture data or location data associated with those other devices. The additional data is used in conjunction with the data received from the user device to generate interfaces indicative of performance metrics of the application.

A method for troubleshooting abnormal behavior of an application hosted on a networked computer system. The method may be implemented by a root cause analyzer. The method includes tracking a single application performance metric across all the clients of an application hosted on a networked computer system and analyzing an aggregated application based on the single application metric. The method involves determining outlier client attributes associated with an abnormal transaction of the application and ranking the outlier client attributes based on comparisons of historical and current abnormal transactions. The method associates one or more of the ranked outlier client attributes with the root cause of the current abnormal transaction. Association rule learning is used to associate one or more of the ranked outlier client attributes with the root cause.

A method of generating metrics data associated with a microservices-based application comprises ingesting a plurality of spans and mapping an ingested span of the plurality of spans to a span identity, wherein the span identity comprises a tuple of information identifying a type of span associated with the span identity, wherein the tuple of information comprises user-configured dimensions. The method further comprises grouping the ingested span by the span identity, wherein the ingested span is grouped with other spans from the plurality of spans comprising a same span identity. The method also comprises computing metrics associated with the span identity and using the metrics to generate a stream of metric data associated with the span identity.

Systems and methods for using instrumentation for maintenance of a user-configured program in a cloud computing environment are herein disclosed as comprising, in an implementation, intercepting operation data pertaining to the user-configured program, including a start time, an execution time interval, an operation, and an origin of the operation, canonicalizing the intercepted operation data by stripping operation-specific variable data from the operation data, aggregating the canonicalized operation data based on the start time, the canonicalized operation data, and the origin of the operation, and storing the aggregated operation data within a time series database in the execution time interval based on the start time.

A monitoring system monitors processing of incoming messages by an application, and logs data related to performance of the application. The application includes a plurality of checkpoints, and the monitoring system logs data upon each message traversing the checkpoints in the application. The monitoring system is configured to dynamically modify checkpoints within the application based on latency detection of portions of the application, resulting in improved granularity/resolution of the data collected from congested portions of the application, and reducing the performance penalty of the monitoring system from portions of the application that are not congested.