An aspect of behavior of an embedded system may be determined by (a) determining a baseline behavior of the embedded system from a sequence of patterns in real-time digital measurements extracted from the embedded system; (b) extracting, while the embedded system is operating, real-time digital measurements from the embedded system; (c) extracting features from the real-time digital measurements extracted from the embedded system while the embedded system was operating; and (d) determining the aspect of the behavior of the embedded system by analyzing the extracted features with respect to features of the baseline behavior determined.

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.

A pseudo language is provided for manipulating the complex variables associated with the orchestration flow. Verbs are specified in the pseudo language. The verbs cause operations to be performed on the complex variables during processing of the orchestration flow. A first verb of the verbs is specified with a first operation of the operations The first operation, when processed, transfers data from a first set of source elements in a source complex variable to new target elements in a target complex variable based on a description of a target schema of the target complex variable. The target complex variable does not include the first subset of source elements and the target schema includes the description of the first subset of the source elements.

Recompiling code based on sustainability. Code is recompiled in a manner that accounts for sustainability values. When a deployment request is received, sustainability values are identified. The resources needed to fulfill the deployment request are identified based on the sustainability values and available resources. Once the resources that are likely to best meet the sustainability values are identified, the code is recompiled accordingly.

A computing device may access a target code for implementing an application. The device may identify addresses for one or more functions or one or more variables associated with the target code. The device may generate an interval tree comprising a root node and one or more function nodes. The device may in response to the target code invoking a function or variable: generate an intercept function configured to intercept communication between the target code and a call address for the at least one of the one or more functions or the one or more variables invoked by the target code. The device may intercept data communicated between the target code and the call address. The device may store the intercepted data as a function node in the interval tree. The device may transmit the interval tree to a user device.

A system and process extract software application performance data from disparate ownership sources and make the various source data compatible for comparison data. A software application's performance in the marketplace may be compared to other applications in a same group with comparable data information. A M2M (mobile-to-mobile) technology is an interface layer connection to a backend server that builds machine learning pipelines and may use artificial intelligence to turn massive datasets into identifiable patterns, algorithms and statistical models. This layer is capable of cleaning, aggregating, and organizing data from disparate sources to produce meaningful conclusions to complex problems to inform strategic business decisions.

Methods, systems, and computer-readable media for automated deprecation analysis in a service-oriented system are disclosed. A service deprecation system determines that a first service in a service-oriented system calls a second service in the service-oriented system. The service deprecation system determines that the second service calls a third service in the service-oriented system. The service deprecation system determines that logic of the second service is duplicated by logic of the first service. The first service is modified to call the third service instead of the second service, and the second service is disabled or removed from the service-oriented system.

Load testing a service having a plurality of different states is provided. A multitude of simulated users accessing the service are divided into a plurality of cohorts. Simulated users within a given cohort share a similar personality type. A load test of the service is performed by applying a set of service requests from each respective cohort to the service. In response to a percentage of simulated users of each cohort encountering a particular state in the service, a user response is determined for the percentage of simulated users within each cohort at that particular state based on a probabilistic user behavior model corresponding to a personality type of each cohort such that user responses at that particular state are distributed in accordance with the probabilistic user behavior model. Distributed user responses at that particular state are applied to the load test in accordance with the probabilistic user behavior model.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented as a computational graph on a distributed computing network. A method includes: receiving data representing operations to be executed in order to perform a job on a plurality of hardware accelerators of a plurality of different accelerator types; generating, for the job and from at least the data representing the operations, features that represent a predicted performance for the job on hardware accelerators of the plurality of different accelerator types; generating, from the features, a respective predicted performance metric for the job for each of the plurality of different accelerator types according to a performance objective function; and providing, to a scheduling system, one or more recommendations for scheduling the job on one or more recommended types of hardware accelerators.

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.