Abstract: Disclosed embodiments relate to reporting Electronic Control Unit (ECU) errors or faults to a remote monitoring server. Operations may include receiving operational data from a plurality of ECUs in the vehicle, the operational data being indicative of a plurality of runtime attributes of the plurality of ECUs; generating, through a machine learning process, a statistical model of the operational data; receiving live, runtime updates from the plurality of ECUs in the communications network of the vehicle; identifying an ECU error associated with an ECU in the communications network of the vehicle, the ECU error being determined by a comparison of the live, runtime updates with the statistical model of the operational data to identify at least one deviation from the operational data; and wirelessly sending a report to the remote monitoring server based on the live, runtime updates, the report identifying the ECU and the identified ECU error.

Abstract: Disclosed embodiments relate to perform operations for receiving and integrating a delta file in a vehicle. Operations may include receiving, at an Electronic Control Unit (ECU) in the vehicle, a delta file, the delta file comprising a plurality of deltas corresponding to a software update for software on the ECU and startup code for executing the delta file in the ECU; executing the delta file, based on the startup code, in the ECU; and updating memory addresses in the ECU to correspond to the plurality of deltas from the delta file.

Abstract: Disclosed herein are techniques for efficiently providing controller data as part of a maintenance or update process. Techniques include receiving, from a first remote computing device, a message associated with at least one controller; extracting, based on the received message, an image of software associated with the at least one controller; accessing, based on the extracted image, a delta file; and transmitting the accessed delta file to a second remote computing device.

Abstract: Disclosed embodiments relate to automatically providing updates to at least one vehicle. Operations may include receiving, at a server remote from the at least one vehicle, Electronic Control Unit (ECU) activity data from the at least one vehicle, the ECU activity data corresponding to actual operation of the ECU in the at least one vehicle; determining, at the server and based on the ECU activity data, a software vulnerability affecting the at least one vehicle, the software vulnerability being determined based on a deviation between the received ECU activity data and expected ECU activity data; identifying, at the server, an ECU software update based on the determined software vulnerability; and sending, from the server, a delta file configured to update software on the ECU with a software update corresponding to the identified ECU software update.

Abstract: Disclosed embodiments relate to identifying Electronic Control Unit (ECU) anomalies in a vehicle. Operations may include monitoring, in the vehicle, data representing real-time processing activity of the ECU; accessing, in the vehicle, historical data relating to processing activity of the ECU, the historical data representing expected processing activity of the ECU; comparing, in the vehicle, the real-time processing activity data with the historical data, to identify at least one anomaly in the real-time processing activity of the ECU; and implementing a control action for the ECU when the at least one anomaly is identified.

Abstract: Disclosed herein are techniques for creating and using tokens representing portions of programming code. Techniques include identifying a body of programming code; associating a plurality of tokens with respective portions of the body of programming code to generate a token-based representation of the body of programming code, wherein the associating comprises determining at least one canonical representation of at least one of the respective portions of the body of programming code; providing the token-based representation of the body of programming code to an emulator, the emulator being configured to interpret token-based representations; and receiving, from the emulator, an emulation result.

Abstract: Disclosed herein are techniques for training code language models. Techniques include making a plurality of programming code segments available to a code language processing model; providing an output of the code language processing model to one or more regression layers; determining, based on the one or more regression layers, a degree of functional similarity between two portions of the output; providing the degree of functional similarity to the code language processing model; and updating, based on the degree of functional similarity, the code language processing model.

Abstract: Disclosed herein are techniques for dynamically predicting resource usage for code changes. Techniques include identifying an element of programming code; identifying a programming code execution environment; accessing a code language processing model, wherein the code language processing model has been trained to associate programming code execution tasks with amounts of computing resource usage; and predicting, without requiring execution of the element of programming code, an amount of computing resource usage associated with an execution of the element of programming code in the programming code execution environment.

Abstract: Disclosed herein are techniques for creating and using tokens representing portions of programming code. Techniques include identifying a first body of programming code associated with a hardware or software source attribute; associating a plurality of tokens with respective portions of the first body of programming code; configuring model input data for training a code language processing model customized in accordance with the hardware or software source attribute, the model input data comprising the plurality of tokens; and training, using the model input data, the code language processing model to analyze at least a part of the first body of programming code or a part of a second body of programming code, thus producing a customized and trained code language processing model in accordance with the hardware or software source attribute.

Abstract: Disclosed herein are techniques for analyzing control-flow integrity based on functional line-of-code behavior and relation models. Techniques include receiving data based on runtime operations of a controller; constructing a line-of-code behavior and relation model representing execution of functions on the controller based on the received data; constructing, based on the line-of-code behavioral and relation model, a dynamic control flow integrity model configured for the controller to enforce in real-time; and deploying the dynamic control flow integrity model to the controller.

Abstract: Disclosed embodiments relate to automatically providing updates to at least one vehicle. Operations may include receiving, at a server remote from the at least one vehicle, Electronic Control Unit (ECU) activity data from the at least one vehicle, the ECU activity data corresponding to actual operation of the ECU in the at least one vehicle; determining, at the server and based on the ECU activity data, a software vulnerability affecting the at least one vehicle, the software vulnerability being determined based on a deviation between the received ECU activity data and expected ECU activity data; identifying, at the server, an ECU software update based on the determined software vulnerability; and sending, from the server, a delta file configured to update software on the ECU with a software update corresponding to the identified ECU software update.

Abstract: Disclosed herein are techniques for efficiently providing controller data as part of a maintenance or update process. Techniques include receiving, from a first remote computing device, a message associated with at least one controller extracting, based on the received message, an image of software associated with the at least one controller; accessing, based on the extracted image, a delta file; and transmitting the accessed delta file to a second remote computing device.

Abstract: Disclosed herein are techniques for analyzing control-flow integrity based on functional line-of-code behavior and relation models. Techniques include receiving data based on runtime operations of a controller; constructing a line-of-code behavior and relation model representing execution of functions on the controller based on the received data; constructing, based on the line-of-code behavioral and relation model, a dynamic control flow integrity model configured for the controller to enforce in real-time; and deploying the dynamic control flow integrity model to the controller.

Abstract: Disclosed embodiments relate to adjusting vehicle Electronic Control Unit (ECU) software versions. Operations may include receiving a prompt to adjust an ECU of a vehicle from executing a first version of ECU software to a second version of ECU software; configuring, in response to the prompt and based on a delta file corresponding to the second version of ECU software, the second version of ECU software on the ECU in the vehicle for execution; and configuring, in response to the prompt, the first version of ECU software on the ECU in the vehicle to become non-executable.

Abstract: Disclosed herein are techniques for visualizing and configuring controller function sequences.

Abstract: Disclosed herein are techniques for analyzing software delta changes based on functional line-of-code behavior and relation models. Techniques include identifying a prompt to change a first version of code on a controller to a second version of code; constructing, based on the identified prompt, a line-of-code behavior and relation model representing execution of functions of the controller based on the second version of code; performing a signature operation on the generated line-of-code behavior and relation model to produce a signature value; and sending the signature value to the controller; wherein the controller is configured to compare the signature value to a computed signature value that the controller is configured to compute based on the second version of code and determine, based on the comparison, whether to validate the second version of code.

Abstract: Disclosed herein are techniques for identifying software dependencies based on functional line-of-code behavior and relation models. Techniques include accessing a first line-of-code behavior and relation model representing execution of functions of a first portion of executable code, the first portion of executable code being associated with a first symbol; detecting a change to the first portion of executable code; constructing, based on the changed first portion of executable code, a second line-of-code behavior and relation model representing execution of functions of the changed first portion of executable code; determining, based on the constructed second model, a dependency between (i) the changed first portion of executable code or the first symbol and (ii) a second symbol; and generating, based on the determined difference, a report identifying the dependency.

Abstract: Disclosed herein are techniques for using a line-of-code behavior and relation model to determine software functionality changes. Techniques include identifying a first portion of executable code and a second portion of executable code; accessing a first line-of-code behavior and relation model representing execution of functions of the first portion of executable code; constructing, based on the second portion of executable code, a second line-of-code behavior and relation model representing execution of functions of the second portion of executable code; performing a functional differential comparison of the first line-of-code behavior and relation model to the second line-of-code behavior and relation model; determining, based on the functional differential comparison, a status of functional equivalence between the first portion of executable code and the code portion of executable code; and generating, based on the determined difference, a report identifying the status of functional equivalence.

Abstract: Disclosed embodiments relate to automatically providing updates to at least one vehicle. Operations may include receiving, at a server remote from the at least one vehicle, Electronic Control Unit (ECU) activity data from the at least one vehicle, the ECU activity data corresponding to actual operation of the ECU in the at least one vehicle; determining, at the server and based on the ECU activity data, a software vulnerability affecting the at least one vehicle, the software vulnerability being determined based on a deviation between the received ECU activity data and expected ECU activity data; identifying, at the server, an ECU software update based on the determined software vulnerability; and sending, from the server, a delta file configured to update software on the ECU with a software update corresponding to the identified ECU software update.

Abstract: Disclosed herein are techniques for using a line-of-code behavior and relation model to determine software functionality changes. Techniques include identifying a first portion of executable code and a second portion of executable code; accessing a first line-of-code behavior and relation model representing execution of functions of the first portion of executable code; constructing, based on the second portion of executable code, a second line-of-code behavior and relation model representing execution of functions of the second portion of executable code; performing a functional differential comparison of the first line-of-code behavior and relation model to the second line-of-code behavior and relation model; determining, based on the functional differential comparison, a status of functional equivalence between the first portion of executable code and the code portion of executable code; and generating, based on the determined difference, a report identifying the status of functional equivalence.