Abstract: Techniques regarding augmenting one or more training datasets for training one or more AI models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise training augmentation component that can generate an augmented training dataset for training an artificial intelligence model by extracting a simplified source code sample from a source code sample comprised within a training dataset.

Abstract: In a computer-implemented method for improving a static analyzer output, a processor receives a labeled data set with labeled true vulnerabilities and labeled false vulnerabilities. A processor receives pretrained contextual embeddings from a contextual embeddings model. A processor maps the true vulnerabilities and the false vulnerabilities to the pretrained contextual embeddings model. A processor generates a fine-tuned model with classifications for true vulnerabilities.

Abstract: Approaches presented herein enable fault detection. More specifically, implementation code of one or more functions is identified from source code. The implementation code of the one or more functions is converted to corresponding Abstract Syntax Trees (ASTs). The implementation code of the one or more functions is represented as a first plurality of sets of AST paths over the ASTs. Classification results for the one or more functions are generated with a classifier based on the first plurality of sets of AST paths for the implementation code of the one or more functions. Each of the classification results indicates a probability of having at least one fault in a corresponding function of the one or more functions. Fault detection results of the source code are generated based on the classification results.

Abstract: Techniques regarding AI model introspection are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise model introspection component that can analyze artificial intelligence model learning behavior for a code understanding task by comparing an output of an artificial intelligence model with respect to a plurality of testing data subsets that have varying code complexity distributions.

Abstract: Techniques regarding augmenting one or more training datasets for training one or more AI models are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise training augmentation component that can generate an augmented training dataset for training an artificial intelligence model by extracting a simplified source code sample from a source code sample comprised within a training dataset.

Abstract: Techniques regarding training one or more AI models for a source code understanding task are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a training component that can train an artificial intelligence model on source code samples for a source code understanding task. The source code samples can be ranked based on code complexity.

Abstract: One or more systems, computer-implemented methods, and computer program products to facilitate a process for consensus regarding proceeding with a request of a transaction are provided. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a services component that operates a service of a transaction, where the services component approaches consensus regarding a request of the transaction by communicating with one or more other services components that operate one or more other services immediately upstream or downstream in the transaction to the service. The services component can approach the consensus by communicating only with the one or more other services components. To approach the consensus, the services component can communicate one or more messages that include one or more requests, votes or final decisions.

Abstract: A system, computer program product, and method are provided for probing model signal awareness. An iterative process is employed to systematically isolate one or more relevant tokens of an input sequence to generate a reduced input sequence. The reduced input sequence is validated and presented to a trained artificial intelligence (AI) model and prediction output is generated. The reduction process is continued while the prediction output stays the same as that of the input sequence, and until a minimal sub-sequence is identified. A signal existence in the minimal sub-sequence is verified and signal awareness of the trained AI model is evaluated. The evaluation includes measuring the verified signal existence against an original signal from the input sentence.

Abstract: Approaches presented herein enable fault detection. More specifically, implementation code of one or more functions is identified from source code. The implementation code of the one or more functions is converted to corresponding Abstract Syntax Trees (ASTs). The implementation code of the one or more functions is represented as a first plurality of sets of AST paths over the ASTs. Classification results for the one or more functions are generated with a classifier based on the first plurality of sets of AST paths for the implementation code of the one or more functions. Each of the classification results indicates a probability of having at least one fault in a corresponding function of the one or more functions. Fault detection results of the source code are generated based on the classification results.

Abstract: A method, a computer system, and a computer program product for building pre-trained contextual embeddings is provided. Embodiments of the present invention may include collecting programming code. Embodiments of the present invention may include loading and preparing the programming code using a specialized programming language keywords-based vocabulary. Embodiments of the present invention may include creating contextual embeddings for the programming code. Embodiments of the present invention may include storing the contextual embeddings.

Abstract: In a computer-implemented method for improving a static analyzer output, a processor receives a labeled data set with labeled true vulnerabilities and labeled false vulnerabilities. A processor receives pretrained contextual embeddings from a contextual embeddings model. A processor maps the true vulnerabilities and the false vulnerabilities to the pretrained contextual embeddings model. A processor generates a fine-tuned model with classifications for true vulnerabilities.

Abstract: Using a natural language processing model, a historical defect report comprising a defect description in narrative text form is parsed. Within a code repository, source code associated with the historical defect report is identified. From the historical defect report and the source code, a logging rule comprising a defect type, logging placement information corresponding to the defect type, and logging format information corresponding to the defect type is generated. By parsing a new defect report using the natural language processing model, the new defect report reporting a defect in new source code, it is determined that the logging rule applies to the new defect report. Logging source code generating logging output when executed is placed within the new source code according to the logging rule. Execution of the new source code including the logging source code is caused, generating the logging output.

Abstract: A method, a computer system, and a computer program product for building pre-trained contextual embeddings is provided. Embodiments of the present invention may include collecting programming code. Embodiments of the present invention may include loading and preparing the programming code using a specialized programming language keywords-based vocabulary. Embodiments of the present invention may include creating contextual embeddings for the programming code. Embodiments of the present invention may include storing the contextual embeddings.

Abstract: A method, a computer system, and a computer program product for vulnerability analysis using contextual embeddings is provided. Embodiments of the present invention may include collecting labeled code snippets. Embodiments of the present invention may include preparing the labeled code snippets. Embodiments of the present invention may include tokenizing the labeled code snippets. Embodiments of the present invention may include fine-tuning a model. Embodiments of the present invention may include collecting unlabeled code snippets. Embodiments of the present invention may include predicting a vulnerability of the unlabeled code snippets using the model.

Abstract: Techniques regarding autonomously generating one or more graph query language schemas are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an interface component, operatively coupled to the processor, that can generate a schema for a graph query language wrapper that can translate a query to a request against a target application programming interface. The schema can comprise a sanitation map that can delineate a relation between a raw data format expected by the target application programming interface and a sanitized data format exposed by the graph query language wrapper.

Abstract: Techniques regarding autonomously generating one or more graph query language schemas are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an interface component, operatively coupled to the processor, that can generate a schema for a graph query language wrapper that can translate a query to a request against a target application programming interface. The schema can comprise a sanitation map that can delineate a relation between a raw data format expected by the target application programming interface and a sanitized data format exposed by the graph query language wrapper.

Abstract: A method and system of determining whether a specification is an accurate representation of an application program interface (API) is provided. The specification is received electronically over a network. Service calls to be tested are identified based on the specification. A test case is created for each of the identified service calls. A sequence is created for the test cases. A test plan is generated based on the created sequence. The generated test plan is executed. Upon identifying an error in response to the executed test plan, a notification is generated, indicating that the specification is not an accurate representation of the API.

Abstract: A method and system of determining whether a specification is an accurate representation of an application program interface (API) is provided. The specification is received electronically over a network. Service calls to be tested are identified based on the specification. A test case is created for each of the identified service calls. A sequence is created for the test cases. A test plan is generated based on the created sequence. The generated test plan is executed. Upon identifying an error in response to the executed test plan, a notification is generated, indicating that the specification is not an accurate representation of the API.

Abstract: A method for a contact center to provide information to one or more users in response to one or more inquiries from the one or more users is presented. For example, the method includes accessing, using at least one client adaptor, at least one agent group that includes at least one agent having knowledge regarding the one or more inquiries. The method further includes the at least one agent responding to the one or more inquiries using the at least one client adaptor. The at least one agent group has pre-existing communications infrastructure that is accessed by a computing platform of the contact center using the at least one client adaptor.

Abstract: Techniques for role-based service operation status reporting to clients are provided. In one aspect, a method for reporting a status of a service operation to a client is provided. The method includes the following steps. A sequence of business process steps involved in performing the service operation is identified. One or more abstractions of the business process steps are made, each abstraction containing a sequence of a fewer number of steps than the business process, wherein the number of steps in each of the abstractions correlates with a level of detail about the service operation. The status of the service operation is reported to the client based on a given one of the abstractions having the level of detail best suited to a role of the client.