Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes processor circuitry to execute computer readable instructions to: execute a machine learning model to generate a first code recommendation for programming code, the first code recommendation being associated with security of the programming code; cause output of the first code recommendation via a user interface; update the machine learning model based on feedback obtained via the user interface; determine a performance of the programming code; generate a second code recommendation, the second code recommendation being associated with the performance of the programming code; and cause output of the second code recommendation via the user interface.

The present disclosure is directed to systems and methods directed to improving the functions of a vehicle. Systems and methods are provided that provide a custom tool that autogenerates a set of software agents that allows a system to separate processing, transmission and receiving of messages to achieve better synchronization. The disclosure herein also provides a simplified method of key provisioning by designating one client as a server and assigning a symmetric key to every other client permanently provisioned between that client and the server. Systems and method are further provided that predict faults in a vehicle. Systems and methods are also provided that preserve data in the event of a system crash. Systems and methods are also provided in which an operating system of a vehicle detects the presence of a new peripheral and pulls the related interface file for that new peripheral. Further, a data synchronization solution is provided herein which provides optimized levels of synchronization.

Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to perform operations processing, in an integrated development environment, a set of program code to identify an assertion within the set of program code; determining compile-time provability of a condition specified by the assertion; and presenting an error condition in response to failing to determine compile-time provability of the condition specified by the assertion, wherein determining compile-time provability of the condition specified by the assertion includes semantically converting the condition specified by the assertion into a Boolean, reducing the Boolean to an intermediate representation, and processing the intermediate representation to detect an expression within the intermediate representation that is non-constant at compile-time.

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.

A method for receiving a first portion of object code, analyzing a first portion of object code in a static manner to determine a call tree hierarchy, dividing, by a synthetic compiler, the first portion of object code into a plurality of modules; and starting to run the first portion of object code to start a runtime phase, with the running of the first portion of the object code including: (i) lazy loading of the modules of the plurality of modules of the first portion of object code, and/or (ii) eager unloading of the modules of the plurality of modules of the first portion of object code.

Using static analysis for vulnerability detection, including: inspecting, using an underapproximate static code analysis, a non-executable representation of an application to identify one or more vulnerabilities in the application; and providing an indication of the one or more vulnerabilities, wherein the underapproximate static code analysis can include a taint analysis that is based on one or more of symbolic execution or incorrectness logic.

Techniques are provided for mapping natural language to code segments. In one embodiment, the techniques involve receiving a document and software code, wherein the document comprises a natural language description of a use of the code, generating, via a vectorization process performed on the document, at least one vector or word embedding, generating, via a natural language processing technique performed on the at least one vector or word embedding, a first label set, generating, via a machine learning analysis of the software code, a second label set, determining, based on a comparison of the first label set and the second label set, a match confidence between the document and the software code, wherein the match confidence indicates a measure of similarity between the first label set and the second label set, and upon determining that the match confidence exceeds a predefined threshold, mapping the document to the software code.

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.