An automated system automatically creates compliance checking code that is used to test the functional aspects of implemented product code. Intermediate code blocks are created that are then written into compliance checking code, to enable automatic creation of compliance checking scripts designed to test the implemented product code for compliance with persona, outcome, states and state transitions, consistency rules, and annotations specified by the codified user experience design.

In an approach to risk prediction for bug-introducing changes, a computer retrieves one or more historic pull requests. A computer determines a unique file linking for each file included in the historic pull requests. A computer generates a file risk dataset. A computer performs chronological partitioning on the file risk dataset. A computer determines bug-introducing changes in the file risk dataset. A computer computes a collaborative file association between two or more of the files in the file risk dataset. A computer labels each of the files in the file risk dataset with an associated risk of introducing a bug. A computer generates a labelled file risk inducing ground truth dataset. A computer inputs the labelled file risk inducing ground truth dataset to a file risk prediction model. A computer extracts pull request features from the historic pull requests. A computer generates a pull request risk prediction model.

Exemplary embodiments may programmatically determine the number and locations of breakpoints and table values in a lookup table so as to reduce their counts. In some exemplary embodiments, the minimum number of breakpoints and breakpoint locations that conform with the error tolerance may be determined along with table values for those breakpoints. The exemplary embodiments may determine if a lookup table that conforms with the error tolerance is feasible and may inform a user of the feasibility or infeasibility of generating the lookup table. Where it is feasible to generate the lookup table, the exemplary embodiments may provide automatic programmatic generation of a lookup table with minimal breakpoints and optimal or near-optimal table values.

A method, computer program product, and computing system for defining a library of functional modules; enabling a user to select a plurality of functional modules from the library of functional modules; and enabling the user to visually arrange the plurality of functional modules to form a conversational application.

The present invention relates to a method for bug localisation in an RTL description, the RTL description corresponding to a design. The method comprises the steps of identifying a failing property; obtaining a counterexample corresponding to the failing property; obtaining a plurality of supportive counterexamples, by iteratively modifying the failing property with signal-value combinations selected from the counterexample; mining assertions based on the plurality of supportive counterexamples; filtering and ranking the mined assertions for removing redundant assertions, and obtaining a filtered set of assertions; identifying and mapping a set of one or more RTL lines corresponding to each filtered assertion of the filtered set of assertions; identifying and mapping individual RTL lines corresponding to each of the filtered assertion of the filtered set of assertions; and prioritising each of the mapped RTL line, thereby localising a buggy RTL line.

While the effectiveness of a model inspection method as a means for detecting software defects is known, large-scale software cannot be handled due to great amounts of calculation required for inspection. According to the present invention, after a model inspection problem of software is attributed to be a satisfiability determination problem, the problem is converted to a type that can be solved by a solver used for solving a notification optimization problem having constrained conditions, and the satisfiability is determined in a numerically analytical manner.

A cloud computing system can be configured to generate data models. A model optimizer of the cloud computing system can provision computing resources of the cloud computing system with a data model. A dataset generator of the cloud computing system can generate a synthetic dataset for training the data model. The computing resources can train the data model using the synthetic dataset. The model optimizer can store the data model and metadata of the data model in a model storage. The cloud computing system can receive production data from a data source by a production instance of the cloud computing system using a common file system. The production data can be processed using the data model by the production instance. The computing resources, the dataset generator, and the model optimizer can be hosted by separate virtual computing instances of the cloud computing system.

An information processing device includes one or more processors. The one or more processors are configured to divide targets into a plurality of units for each predetermined identifier; define a set of a first predetermined number of said units as a sub-block from each of the units for the plurality of units; encode the sub-block by predetermined conversion, and obtain a code; and use the code as a key, and detect existence of another sub-block having identical content with the sub-block corresponding to the code when duplication of the key is detected.

A computer-implemented method, system, and computer program product for writing and checking functions of a code file in interactive development environments. The method may include, responsive to determining the function matches a historical function in a log of previously executed functions and their corresponding execution responses, identifying the execution response corresponding to the matching historical function in the log. The method may also include determining an expected result of the function based on the identified execution response.

An apparatus to facilitate synthetic data generation for enhanced microservice debugging is disclosed. The apparatus includes one or more processors to: load a filter for a synthetic data generator for a service deployed in a datacenter system, the filter configured for the service based on service policies; prioritize synthetic parameters of the filter based on service parameters used to model microservices deployed for the service; generate a synthetic dataset for ingestion using the prioritized synthetic parameters in the filter of the synthetic data generator, the synthetic dataset generated by applying the synthetic parameters of the filter to an original infield dataset of the service; demultiplex the synthetic dataset in response to a synthetic activation profile generated using the synthetic dataset matching one or more monitored previous activation profiles of the service; and reverse map the demultiplexed synthetic dataset to match to original data in the original infield dataset.