A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.

Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.

Embodiments of the present disclosure disclose a model management apparatus, method and system, where the apparatus includes a model storage module and a model update module; the model storage module is configured to store at least one functional model; the model update module is connected to the model storage module, and is configured to obtain a target functional model to be updated from the model storage module according to a received update instruction, perform functional update on the target functional model to be updated based on a target model data set, and transmit an updated target functional model to the model storage module for storage; where the target functional model is called by a model application apparatus to cause the model application apparatus to implement a set function, and the model application apparatus and the model management apparatus are set up independently of each other.

Training a predict model with network traffic and data change messages generated by an existing web application running in a production environment. The predict model being is trained to predict data changes resulted from API calls embodied in network traffic. A stream of network traffic of the existing web application is replayed with an upgraded version of the existing web application to generate real data changes. The stream of network traffic is applied to the predict model to generate predicted data change messages. The predicted data change messages are comparing with real data change messages representing the real data changes. One or more existing APIs is identified as being possibly functionally degraded based on any inconsistency of the predicted data change messages with the real data change messages.

A class of systems for searching the code of conventional software, programmable hardware like Field Programmable Gate Arrays and Application Specific Integrated Circuits for behaviors of interest. This enables behavioral requirements and testing to be applied and automatically positioned in the code. Five using communities are envisioned for variations on the invention. These are Software Safety Program Administration, developers of software Intellectual Property (IP) modules and hardware IP modules, Systems Integrators of such IP, IP brokers, and Cyber Security Vendors. The usage of the invention by these communities is sufficiently different that there are separate and unique claims specific to the needs of each group.

A hardware monitor arranged to assess performance of a hardware design for an integrated circuit to complete a task. The hardware monitor includes monitoring and counting logic configured to count a number of cycles between start and completion of the symbolic task in an instantiation of the hardware design; and property evaluation logic configured to evaluate one or more formal properties related to the counted number of cycles to assess the performance of the instantiation of the hardware design in completing the symbolic task. The hardware monitor may be used by a formal verification tool to exhaustively verify that the hardware design meets a desired performance goal and/or to exhaustively identify a performance metric (e.g. best case and/or worst case performance) with respect to completion of the task.

Described systems and techniques enable prediction of a state of an application at a future time, with high levels of accuracy and specificity. Accordingly, operators may be provided with sufficient warning to avert poor user experiences. Unsupervised machine learning techniques may be used to characterize current states of applications and underlying components in a standardized manner. The resulting data effectively provides labelled training data that may then be used by supervised machine learning algorithms to build state prediction models. Resulting state prediction models may then be deployed and used to predict an application state of an application at a specified future time.

Systems and methods for formal verification of programs. The systems and methods provide a new game-theoretical, strategy-based compositional semantic model for concurrency, a set of formal linking theorems for composing multithreaded and multicore concurrent layers, and a compiler that supports certified thread-safe compilation and linking. Verification of an overlay interface can include determining an intermediate strategy for a primitive operation running on an underlay interface and refining that intermediate strategy to a strategy running on the overlay interface by applying a vertical and a horizontal composition rule. The refined strategy can then be composed with compatible strategies running on the overlay interface according to a parallel composition rule. Strategies may be compatible when rely conditions imposed by each strategy satisfy guarantees provided by the other strategies. The system and method of formal verification can be applied to formal verification of smart contracts.

This application relates to systems and methods for automatically generating experiments based on experiment requests routed to micro-services (model sub-components) using a prefix-based routing mechanism. In some examples, experiment requests may parsed to determine lower layer services (e.g., components) whose properties need to be changed for a model iteration. Prefixes in requests may be used to route the experiment requests and portions thereof to appropriate services or layers for configuration at the micro-service level. Routing tables at each higher layer may be utilized to determine the correct sub-layers to redirect a request and/or portion thereof. At micro-service level, each micro-service may store and use a configuration table to match a received parameter in a request with a property and its corresponding value for the experiment.

Provided are a simulation method and a modeling method. The simulation method includes generating input information regarding a target input/output (I/O) throughput demanded by the user application based on a read request received from a user application and generating output information including an I/O processing rate and a delay time through a neural network by using the input information as an input.