61 results about "Type inference" patented technology

Systems and methods of representing and extracting type information from typeless and dynamically-typed programming languages are provided. These systems and methods can be immune to source file edits and the order in which those edits are executed. Deriving type information from these languages allows tools to perform operations, for example, refactoring, intellisense, recognition of semantic errors, etc. These systems and methods can be implemented using processes that, implicitly or explicitly, gather type information for the program, and can also accept user input as well.

A system and methodology that provide schema inferencing at runtime. An inference component receives as an input an input data stream, and processes the input data stream at runtime to develop a schema based on actual values thereof. The inferred schema is then used to create an inferred output data that is a specialized representation of the input stream data, which can then be accessed efficiently and conveniently.

A method and system for translating source code in one logically distinct object oriented language on one platform into another object oriented language on another platform. This includes the translation of the classes and statements in the original source. It also includes the creation of object oriented interfaces for all types and services used in the source code whose definitions are not provided. Once the source code classes are translated, a complete object oriented interface to the target platform is generated. Type inference analysis is performed on the input source code in order to infer types for undeclared services and to infer type hierarchies for undeclared types. Type inference analysis is utilized to generate object oriented interfaces for all types and services used in the source code whose definitions are not available. The method and system also have the capability of generating, on an as needed basis, specific bridge classes which support bridging the platform differences between the target and the source platform.

The invention provides a Python resource sensitive defect code detecting method based on a depth neural network. The Python resource sensitive defect code detecting method comprises the following steps of 1, obtaining a source code of the historical version of the same software and a source code of a to-be-detected version; 2, utilizing type deduction to extract resource sensitive code modes of all versions; 3, extracting relevant characteristics of the resource sensitive code modes; 4, calculating all characteristic similarities between a defect code mode and a safe code mode and between thedefect code mode and a to-be-detected code mode to generate characteristic vectors and obtaining a training set and a testing set; 5, using the training set to train a depth neural network model to combine the characteristics, then using the mode with a testing set to calculate relevance of the depth neural network model and ranking the relevance; 6, conducting prompt, assisting development and maintenance on possibly wrong resource object operations according to a relevance ranking result in a program development and maintenance period. The Python resource sensitive defect code detecting method based on the depth neural network solves the problems that currently, an automated method aiming at Python language resource sensitive code recognition and defect code detection is lack, then the software risk is lowered, the software quality is improved, and thus the efficiency for a developer and a maintainer to develop and maintain software is improved.

Performance of a program written in dynamic languages is improved through the use of a compiler that provides type inference for methods having a user-defined element. The user-defined element may be an input in a user-defined type. Though, the user-defined element may reflect that the method is user-defined. Type inference may be performed based on a user-defined mapping, relating input types to output types for one or more methods. The mapping may be specified as a data table provided to the compiler or as one or more functions that register with the compiler such that, as the compiler processes portions of a source program and detects a method with a user-defined element, the compiler may access the mapping and infer the type of an output of the method. The inferred type may then be used to optimize code dependent on that output.

The invention provides a method and system for united inference of Chinese events on a chapter level. The method and the system can apply valuable information in argument extraction to assist in trigger word extraction in turn, so that cascading mistakes in a traditional pipeline mode can be reduced. With regard to an implementation method, the method and the system provided by the invention combine machine learning with the inference; and various types of language knowledge is used to recognize a time sequence relation of the Chinese events. The method and the system provided by the invention have the advantage that recognition performance is enhanced to a certain extent in comparison with existing methods and systems.

Computer program source code may be parsed to generate corresponding type constraints. The type constraints may be incrementally resolved to achieve incremental type inferencing of various programmatic elements specified by the source code. In contrast to conventional type inference, incremental type inferencing may enable the inference of type information for programmatic elements specified by a programming language that was not designed for type inference. One or more type constraints may be incrementally added to a constraint set and the constraint set repeatedly unified. Unification anomalies may arise during this process. For example, type mismatches corresponding to potential flaws in the source code may be detected by the unification process. In response to some or all of these anomalies, one or more entries may be made in a type anomaly audit. A separate such audit may be provided with respect to each intended deployment platform.

An improved type inference may be calculated for a path expression that may be classified into a series of input expressions followed by steps. For each such series, the inference may be calculated based on input including a type for the input expression, an axis for the step, and a node test for the step. The cardinality of the input expression type is preserved for the calculation of the step type. Also, a set of one or more matching node types may be identified within the type repository. These matching node types are node types within the axis of the step that match the node test of the step. These matching node types are identified without calculating the full content type implied by the axis. Avoiding the calculation of the full content type of the axis may reduce the processing time required to perform the inference.

A computer program (10) for analysing a specification, determining what kind of data processing operations are carried out, and deducing or inferring from the kind of internal operations, what data types are appropriate. The data types inferred can be compared with specified data types, to check for errors, or optimise the overall process. This is effectively an extension of type inference from individual programs into the higher level technical field of process analysis. It can also be applied to monitoring and optimising processes during operation of the processes.

Techniques for automatically partitioning a multi-platform data transform flow graph to one or more target output platforms are provided. The techniques include performing type inference on a transform graph, wherein the transform graph comprises one or more data transforms, automatically partitioning the transform graph to one or more target output platforms based on one or more policies, performing an optimization of the partitioned transform graph, and generating code, from the partitioned transform graph, for each set of the one or more data transforms based on the one or more target output platforms.

The invention discloses a smart socket-based electrical appliance type inference method and device, wherein the method comprises the following steps: constructing a user power data set according to electrical appliance power consumption data, and constructing an electrical appliance power consumption characteristic model library according to the electrical appliance power consumption characteristic; constructing an electrical appliance behavior data set according to the user power data set and the electrical appliance power consumption characteristic model library; constructing a characteristic vector extracting model according to the electrical appliance behavior data set so as to construct a power consumption characteristic vector data set; constructing an electrical appliance type classifier according to the power consumption characteristic vector data set and a machine learning algorithm; detecting the power consumption data of a to-be-measured electrical appliance and determiningthe electrical type of the to-be-measured electrical appliance through the model and the classifier. According to the method, the power data of the user is collected through a smart socket; by extracting the characteristics of the range of the power consumption of the household appliances, the duty ratio of the power consumption and the duration of time of power consumption, the power utilizationbehavior of the user is inferred, an electrical appliance behavior event data set is generated, and an electrical appliance type classifier is constructed based on the machine learning model, so thatthe type of the utilized electrical appliance can be inferred in real time; the robustness is high; and the method is simple and easy to implement.

The disclosed embodiments provide a system that facilitates the development and execution of a software program. During runtime of the software program, the system delays type inference on a generic type parameter of an implementation of an overloaded function, wherein the generic type parameter is associated with a type interval containing an unbounded lower limit and one or more self-typed constraints. Upon detecting a type query for a dynamic type of the generic type parameter, the system compares a queried type from the type query with a set of inference choices for the generic type parameter. If the queried type matches an inference choice from the set of inference choices, the system uses the inference choice to perform type inference on the generic type parameter.