Abstract: System and method for assessing and enhancing quality of prompts in generative AI-driven software coding is disclosed. The method includes, receiving a user input, wherein the user input comprises at least one keyword associated with a task, predicting an intent of the user input based on the at least one keyword, the task, and a context of the at least one keyword, and predicting a plurality of target elements applicable to the user input based on the predicted intent, wherein the plurality of target elements comprise entities and actions in the user input.

Abstract: A device may receive system data identifying computational components and software components of a distributed and heterogeneous system executing a hybrid cloud application, and may create digital twins for the computational components and the software components. The device may create a central digital twin to receive functional data, operational data, and key performance indicators (KPIs) from the digital twins, and may create complex KPIs based on the functional data, the operational data, and the KPIs. The device may modify, based on the complex KPIs, one or more of the digital twins to generate additional KPIs, and process the additional KPIs, with a principal component analysis model and a self-organizing maps model, to detect anomalies in the distributed and heterogeneous system. The device may generate, based on the anomalies, a KPI cause vector identifying a root cause associated with the anomalies, and may perform actions based on the root cause.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating and updating project plans for software development projects.

Abstract: Method, system, and computer-readable storage media for generating a foundation model pipeline including a set of foundation models for completion of a plurality of tasks. Each task of the plurality of tasks has a set of pre-conditions and a set of post-conditions. Based on the set of pre-conditions and the set of post-conditions, a set of possible plans for processing the plurality of tasks is generated. For each plan of the set of possible plans, the set of foundation models from a plurality of foundation models is identified for performing each task of the plurality of tasks according to the respective plan. Further, an efficiency score is estimated for each plan to perform the plurality of tasks according to the plan. Based on the estimated efficiency score of each plan, the set of foundation models is selected for the plurality of tasks.

Abstract: Methods, systems, and computer-readable storage media for information retrieval using query aware extended chunks. For retrieving relevant information from a foundation model, a plurality of chunks substantially relevant to a query are identified, the plurality of chunks containing information relevant to the query. Based on the plurality of chunks, one or more semantically similar chunks are identified for one or more of the plurality of chunks substantially relevant to the query. By combining the one or more semantically similar chunks with the respective one or more of the plurality of chunks, one or more extended chunks are generated. Based on one or more objectives, a subset of the one or more extended chunks is selected. The query and the selected subset are inputted as a context to the foundation model for retrieving relevant information from the foundation model.

Abstract: Methods, systems, and computer-readable storage media for information retrieval using query aware extended chunks. For retrieving relevant information from a foundation model, a plurality of chunks substantially relevant to a query are identified, the plurality of chunks containing information relevant to the query. Based on the plurality of chunks, one or more semantically similar chunks are identified for one or more of the plurality of chunks substantially relevant to the query. By combining the one or more semantically similar chunks with the respective one or more of the plurality of chunks, one or more extended chunks are generated. Based on one or more objectives, a subset of the one or more extended chunks is selected. The query and the selected subset are inputted as a context to the foundation model for retrieving relevant information from the foundation model.

Abstract: Methods, systems, and computer-readable storage media for generating configuration templates. For generating the configuration templates, conversational queries are generated. Based on conversational responses to the conversational queries, a task context and a task intent are determined using a first foundation model to identify software packages to be configured to perform tasks. Based on the task context, the task intent, and the conversational responses, a workflow template is generated using a second foundation model. Further, based on conversational responses, configuration fields of the workflow template for subtasks of each task are refined using a third foundation model. Based on the configuration fields of the workflow template, configuration fields of the configuration template for each task are generated using a fourth foundation model.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent re-use of knowledge (e.g., across an organization) using a natural text-based querying framework. A knowledge representation of prior work performed for the organization may be generated based on organizational knowledge (e.g., historical work record data that identifies a plurality of work items across an organization). The knowledge representation may include individual work-record entities for each respective work item and individual knowledge graphs corresponding to the individual work-record entities. For each individual knowledge graph, operations may be performed to identity and store project name, subgraph, sentence embedding, and word embedding information.

Abstract: A retraining monitoring system maintains the sustainability of a production machine learning (ML) model system that includes a production ML model retraining platform. The retraining monitoring system collects contextual data from the production ML model system and determines if one or more of a currently-selected architectural options has to be changed for sustainability. An architectural option of the production ML model retraining platform, such as, a processing location is selected from a cloud retraining platform or an on-premises retraining platform by a selection process based on a multi-armed bandit problem. An evaluation of the retraining architecture is dealt with as a reinforcement learning problem to implement one of a periodic retraining architecture or a reactive retraining architecture.

Abstract: A method and system for assisting program code development are disclosed. The method may include obtaining multimodal data of a meeting discussing a program code development, extracting a plurality of topics and a plurality of concepts from the multimodal data, identifying a plurality of meeting segments for the plurality of concepts. The method may further include determining a coding intent from program codes for the program code development, aligning the coding intent to a set of topics, identifying the set of concepts associated with the topic aligned with the coding intent, identifying a set of meeting segments associated with the concept. The method may further include determining an alignment metric of the meeting segment based on an alignment metric between the coding intent and the topic, and outputting one or more meeting segments for the coding intent based on alignment metrics of the one or more meeting segments.

Abstract: Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent re-use of knowledge (e.g., across an organization) using a natural text-based querying framework. A knowledge representation of prior work performed for the organization may be generated based on organizational knowledge (e.g., historical work record data that identifies a plurality of work items across an organization). The knowledge representation may include individual work-record entities for each respective work item and individual knowledge graphs corresponding to the individual work-record entities. For each individual knowledge graph, operations may be performed to identity and store project name, subgraph, sentence embedding, and word embedding information.

Abstract: Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating and updating project plans for software development projects.

Abstract: A method and system for assisting program code development are disclosed. The method may include obtaining multimodal data of a meeting discussing a program code development, extracting a plurality of topics and a plurality of concepts from the multimodal data, identifying a plurality of meeting segments for the plurality of concepts. The method may further include determining a coding intent from program codes for the program code development, aligning the coding intent to a set of topics, identifying the set of concepts associated with the topic aligned with the coding intent, identifying a set of meeting segments associated with the concept. The method may further include determining an alignment metric of the meeting segment based on an alignment metric between the coding intent and the topic, and outputting one or more meeting segments for the coding intent based on alignment metrics of the one or more meeting segments.

Abstract: In some examples, collective application portfolio migration control may include determining, for a plurality of applications that are to be clustered for migration to a cloud environment, a coupling coefficient that represents a type of coupling between pairs of applications from the plurality of applications, a proximity coefficient that represents application proximities, and a connectedness coefficient that represents application connections. A combined application relatedness coefficient may be determined based on the coupling coefficient, the proximity coefficient, and the connectedness coefficient. A portfolio graph may be generated based on the combined application relatedness coefficient to generate migration application clusters to duster the plurality of applications. Migration of the plurality of applications to the cloud environment may be controlled based on the migration application clusters.

Abstract: Intelligent AI-based systems and methods of generating architecture diagrams for cloud computing-based infrastructures. The system can ingest and process requirement data and identify intents associated with the software. Based on the classifications of the requirement data, the system can automatically extract dependencies between software layers and microservices in order to identify the most appropriate components for the application. In some embodiments, validation can be performed in which a digital twin model is implemented. Implementation of such as system can eliminate manual errors and variability based on human skill sets, as well as enable risk-free testing of the architecture based on the application goals.

Abstract: A method and system for evaluating and enhancing a prompt for use by a generative artificial intelligence processing model are disclosed. The method may include obtaining a prompt representing a natural language text for use by a generative artificial intelligence processing model, obtaining a prompt classifier trained to evaluate a classification of the prompt, and inputting the prompt to the prompt classifier to generate a classification of the prompt. The method may further include identifying an intent underlying the prompt and detecting an implicit constraint for the prompt based on the intent. The method may further include transforming the intent in the prompt into a constraint-enhanced intent based on the implicit constraint, generating an enhanced prompt based on the constraint-enhanced intent, and outputting the enhanced prompt for the generative artificial intelligence processing model.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support ontology driven processes to generate and/or update digital twins using a partially automated process. To generate the digital twin, an ontology may be obtained and used to generate a hierarchy model, from which a knowledge graph is generated and represents a digital twin. Similarity measurements may be performed on the knowledge graph, such as components thereof, to determine a similarity between the components. Components are ranked and clustered to identify clusters of similar components and new relationships between components. This new information may be used to update the knowledge model and/or knowledge graph. Updating the knowledge graph or model may enable generation of an updated digital twin and enable continued updating of additional different data structures for other similar elements.

Abstract: A method and system for training a federated learning model are disclosed. The method may include receiving the global federated learning model from the server via the client. The client may control remote computing resources. The method may further include identifying a spare computing instance from the remote computing resources and determine a threshold training load for training the global federated learning model based on a training load assigned to the client. The method may further include, in response to a processing capacity of the spare computing instance being sufficient to process the threshold training load, offloading the threshold training load to the spare computing instance and training the global federated learning model on the spare computing instance with the training dataset stored in a data source maintained by the client.