To support adding functionality to applications at a layer of abstraction above language-specific implementations of AOP, a language for implementing AOP facilitates runtime monitoring and analysis of an application independent of the language of the application. Aspects can be created for applications written in any supported language. Program code underlying implementations of aspects can be executed based on detecting triggering events during execution of the application. Routines written with the AOP language comprise event-based aspect code triggers that indicate an event which may occur during execution of the application and the associated aspect code to be executed. An agent deployed to a runtime engine to monitor the application detects events and evaluates contextual information about the detected events against the aspect triggers to determine if aspect code should be executed to perform further monitoring and analysis of the executing application.

Techniques are described that determine whether applications and/or services operating in a network environment adhere to risk reduction policies. An example system includes one or more processors, a first agent, a second agent, a third agent, and a compliance analyzer. The first agent analyzes source code in a source code repository associated with an application executing in a network environment according to a first set of policies. The second agent analyzes artifacts of deployed assets on a target deployment environment associated with the application according to a second set of policies. The third agent analyzes access logs to the application according to a third set of policies. The compliance analyzer determines a risk of the application based on the analysis by the first, second, and third agents.

Computerized systems and methods for managing API information. An exemplary method includes receiving an input from a user device associated with a first computer system, the input not including identity of a second computer system. The method includes determining a target API based on the input, the target API being the second computer system's API. The method also includes determining whether a user of the user device has access to the target API. The method includes retrieving documentation of the target API from an API database if it is determined that the user has access to the target API. The method includes providing the user device with the retrieved documentation.

A method for classifying warning messages generated by software developer tools includes receiving a first data set. The first data set includes a first plurality of data entries, where each data entry is associated with a warning message generated based on a first set of software codes, includes indications for a plurality of features, and is associated with one of a plurality of class labels. A second data set is generated by sampling the first data set. Based on the second data set, at least one feature is selected from the plurality of features. A third data set is generated by filtering the second data set with the selected at least one feature. A machine learning classifier is determined based on the third data set. The machine learning classifier is used to classify a second warning message generated based on a second set of software codes to one of the plurality of class labels.

A multi-threaded programming language and compiler generates synchronous digital circuits that maintain thread execution order by generating pipelines with code paths that have the same number of stages. The compiler balances related code paths within a pipeline by adding additional stages to a code path that has fewer stages. Programming constructs that, by design, allow thread execution to be re-ordered, may be placed in a reorder block construct that releases threads in the order they entered the programming construct. First-in-first-out (FIFO) queues pass local variables between pipelines. Local variables are popped from FIFOs in the order they were pushed, preserving thread execution order across pipelines.

A method for providing a development accelerator for microservices is provided. The method includes obtaining a code set that includes first computer program codes representing a framework for developing microservices in a network environment; obtaining, from the network environment, runtime routines relating to second computer program codes of network functions with respect to the microservices; compiling, in a data package, the runtime routines, the code set, and an instruction set relating to textual directions for developing the microservices; and storing the data package in a central repository.

A method for providing a development accelerator for microservices is provided. The method includes obtaining a code set that includes first computer program codes representing a framework for developing microservices in a network environment; obtaining, from the network environment, runtime routines relating to second computer program codes of network functions with respect to the microservices; compiling, in a data package, the runtime routines, the code set, and an instruction set relating to textual directions for developing the microservices; and storing the data package in a central repository.

A core management system identifies changes to a function signature of an instruction in a core framework of a computer program, generates a static code analysis rule indicating the change to the function signature in the computer program, and distributes the rule to one or more consumer systems using the core framework with an update to the core framework.

A request processing method and apparatus, an electronic device, and a computer storage medium are provided, which are related to the technical field of cloud computing. The request processing method includes: receiving a content delivery network (CDN) request; acquiring a dynamic code corresponding to the CDN request, wherein the dynamic code is a pre-configured code; compiling the dynamic code in real time to obtain a compiled code; and executing the compiled code. The request processing method provided in an embodiment of the present application may improve the flexibility of request processing of a CND system, and has no concurrency limitation.

Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.