909results about "Software metrics" patented technology

A customized prompt generation service automates prompts to a large language model to perform a specified software engineering task. The service stores the custom data of a client that includes code diff hunks, source code segments, code reviews, repaired code, and unit tests from a code base or repository of the client. Prompt templates are associated with each software engineering task that include the requisite information needed for the large language model to perform the target task. A prompt to a large language model includes examples of the software engineering task from the custom data of the client of the service.

An example operation may include one or more of receiving a first code base and a second code base from a software repository, wherein each of the first and second code bases comprise source code of a plurality of different software programs, identifying a source code within the first code base that is interdependent with a source code within the second code base based on execution of a generative artificial intelligence (GenAI) model on the first and second code bases, determining a software component to connect the source code within the first code base to the source code within the second code base, and displaying an identifier of the software component via a user interface.

Systems and methods for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative AI model about structured data in a SaaS environment, enabling users to interact with and manipulate data through AI-assisted interfaces. One of the systems maintains a generative AI agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative AI agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AI. The systems and methods represent advancement in AI-driven SaaS customization and data analysis.

The invention relates to methods and systems for integrating generative artificial intelligence (AI) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining AI agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows AI agents to be added as platform users. The generative AI agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of AI outputs, and management of AI resources as limited assets. This approach enhances SaaS functionality by enabling AI-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

The systems and methods provide a model deployment criterion. The model deployment criterion indicates a difference in a value against which the proxy model may be measured to determine when, if ever, the proxy model should be deployed to replace the existing rule engine. The model deployment criterion may be keyed to the proxy model (e.g., based on a difference in its size, throughput speed, number of changes, etc.), the existing rule engine (e.g., based on a difference in its age, update occurrences to its rule base, etc.), and / or comparisons between models (e.g., based on differences in results, throughput speed, efficiency, etc.).

Systems and methods for generating code for artificial intelligence models without requiring training data that is specific to model components and objectives. For example, the system may receive an original version of a rule engine. The system may input the original version, using a first input condition, into a regeneration model to generate a first regenerated version of the rule engine. The system may determine whether the first regenerated version includes a first hallucination based on comparing the first regenerated version to alternative versions of the rule engine, wherein each of the alternative versions were generated using a respective alternative input condition. The system may, in response to determining that the first regenerated version includes the first hallucination, determining whether the first hallucination comprises a positive mutation.

The systems and methods provide a model deployment criterion. The model deployment criterion indicates a difference in a value against which the proxy model may be measured to determine when, if ever, the proxy model should be deployed to replace the existing rule engine. The model deployment criterion may be keyed to the proxy model (e.g., based on a difference in its size, throughput speed, number of changes, etc.), the existing rule engine (e.g., based on a difference in its age, update occurrences to its rule base, etc.), and / or comparisons between models (e.g., based on differences in results, throughput speed, efficiency, etc.).

The systems and methods provide a model deployment criterion. The model deployment criterion indicates a difference in a value against which the proxy model may be measured to determine when, if ever, the proxy model should be deployed to replace the existing rule engine. The model deployment criterion may be keyed to the proxy model (e.g., based on a difference in its size, throughput speed, number of changes, etc.), the existing rule engine (e.g., based on a difference in its age, update occurrences to its rule base, etc.), and / or comparisons between models (e.g., based on differences in results, throughput speed, efficiency, etc.).

Methods and systems for assessing a software bill of materials (SBOM) include building a knowledge graph from repositories, using function fingerprints of software packages in the repositories. Dependencies of an application are identified using function fingerprints of the application and comparing to function fingerprints of the software packages. A quality score for an SBOM of the application is generated based on a comparison of the identified dependencies to claimed dependencies of the SBOM. An action is performed responsive to the quality score.

Disclosed herein are computer-implemented systems and methods for code value assessment. For example, in one aspect, the system comprises, an input module configured to receive program code submissions from developers, a processing unit equipped with Text-Based Models with Contextual Understanding (TBM-CUs) configured to evaluate the functional meaning, purpose, and value of submitted code segments and distinguish between code contributions from human programmers and machine learning systems, a visualization module configured to present the assessed value of the analyzed code over various time periods and dimensions, offering insights into trends, patterns, and comparative performance, a communication module configured to translate programming code meaning or function into plain language summaries for non-technical stakeholders, and an AI peer review module configured to automatically review, accept or reject code contributions.

An innovative system for integrating generative artificial intelligence (Al) capabilities within Software as a Service (SaaS) platforms. By leveraging advanced Al techniques, seamless data synchronization, intelligent task management, and predictive workflow optimization across multiple SaaS platforms are described. Key components include an Al-driven data synchronization system, an intelligent cross-platform task manager, and an adaptive user interface. The system also features a cross-departmental health monitoring and notification mechanism, alongside an intent¬ based interaction system. This solution enhances organizational efficiency, improves decision¬ making, and provides unprecedented levels of automation and insight in managing complex SaaS ecosystems.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating and updating project plans for software development projects. In one aspect, a method includes obtaining data indicative of initial project plan parameters associated with a software development project; identifying multiple program modules associated with the software development project; obtaining, for a first set of modules of the multiple modules, corresponding source code segments; executing a quality assessment process to compute, for each corresponding source code segment, a corresponding quality score; identifying, based on the quality scores, that at least a subset of the corresponding source code segments are integrable into a corresponding first subset of the first set of modules; and generating a revised project plan by generating updates to the initial project plan parameters by accounting for the corresponding source code segments being integrated in to corresponding ones of the first set of modules.

The invention provides a business module source code generation method and system based on large model business reasoning, and belongs to the technical field of code generation. The business module source code generation method comprises the steps that semantic analysis is conducted on business requirements, and a business association graph with weights is constructed; performing module division, and decomposing a complete task into a plurality of sub-tasks; calling a coordination agent, distributing a corresponding module code generation agent based on a large model for each decomposed subtask, and injecting context information to generate a corresponding module code; wherein the module code generation agent learns a corresponding relationship between tasks and injected context information and codes in advance based on field self-adaptive staged training; and calling the verification agent to perform code quality verification. The method has the beneficial effects that the module division is performed based on the business association graph with the weight, the multi-agent collaborative code generation is constructed by adopting the field-adaptive staged training, and the quality of the generated code is verified, so that the code generation efficiency and quality are improved.

The invention relates to methods and systems for integrating generative artificial intelligence (Al) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining Al agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows Al agents to be added as platform users. The generative Al agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of Al outputs, and management of Al resources as limited assets. This approach enhances SaaS functionality by enabling Al-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

Methods, apparatuses, or computer program products provide for outputting software development insight components to a software development insight interface. A multi-resource software development work graph structure associated with one or more development unit identifiers may be retrieved. The multi-resource software development work graph structure may be configured to represent relationships among a code pull request set and an issue object set that are associated with the one or more development unit identifiers. A software development context object associated with the one or more development unit identifiers may be received. One or more software development insight components may be determined by traversing the multi-resource software development work graph structure based on the software development context object. One or more software development insight components may be outputted for rendering to the software development insight interface.

Systems and methods for integrating generative artificial intelligence (Al) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative Al model about structured data in a SaaS environment, enabling users to interact with and manipulate data through Al-assisted interfaces. One of the systems maintains a generative Al agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative Al agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AL The systems and methods represent advancement in AI-driven SaaS customization and data analysis.

A retrieval-augmented neural transformer model with chunk cross-attention predicts a code review given a proposed source code change, represented as a code diff hunk, and a set of historical code review comments. The code diff hunk represents proposed edits to a source code snippet with its surrounding context that has not been changed. The historical code review comments are associated with code edits that are semantically similar to the proposed source code changes. The code diff hunk is partitioned into chunks which are used to find semantically similar historical code review comments. The set of historical code review comments is aggregated and used to guide the model in makings its predictions.

Systems and methods for detecting anomalies in generative outputs are disclosed herein. The system receives a user prompt indicating a request for data over a time period. The system inputs, into a model, the user prompt to cause the model to generate an output based on the user prompt. The system then generates the first tokens based on the output. To generate the second tokens, the system retrieves, based on the user prompt, sources relating to the data requested by the user prompt. The system then generates queries to request, from the sources, the data over the time period and generates the second tokens based on the retrieved data. The system then performs a comparison of the first tokens and the second tokens and accepts or rejects the output of the model based on the comparison.

Systems and methods provide a first deployment criterion for deploying modified decision engines. A first existing decision engine is accessed, as well as a first modified decision engine that includes rule data generated by an artificial intelligence model based on the first existing decision engine. A first difference between a first output and a first modified output is determined, where the first output is generated by the first existing decision engine and the first modified output is generated by the first modified decision engine. A first selected decision engine is deployed to process subsequent data items to produce subsequent outputs, based on whether the first difference satisfies first deployment criterion. When metric generated based on the subsequent outputs satisfies a criterion modification condition, the artificial intelligence model is used to generate a second deployment criterion, wherein a second selected rule-based decision engine is deployed based on the second deployment criterion.

A configuration tool receives user input associated with a project to deploy a software application to a cloud computing environment. Based on the user input, the configuration tool generates an application package that includes application files, infrastructure configuration data, and customized deployment instructions. A user can follow the deployment instructions to use the application files and infrastructure configuration data within the cloud computing environment, such that the infrastructure configuration data automatically configures the cloud computing environment, and the cloud computing environment executes the software application based on the application files.

A system and method for automatically generating a change history of source code using a generative artificial intelligence (“AI”) system. In examples, a generative AI system receives a request inquiring about one or more changes made to software code of a software service or application. In response to receiving the request, the generative AI system navigates one or more information sources to collect code change context relevant to history of the code change(s). The generative AI system generates an instruction corresponding to the received request, where the instruction and the code change context are provided as input to a language model (LM) (e.g., a generative AI model). Based on the inquiry of the request, the LM processes the input, generates, and provides a corresponding output. The generative AI system then uses the output to generates and provide an explanation about the code change(s) to a requestor of the request.

Systems and methods described herein enable adaptive, threshold-based modification of node maps representing ontologies, knowledge graphs, or code development pipelines using generative artificial intelligence. The disclosed platform can retrieve a node map and generate one or more candidate perturbations that modify nodes or relationships within the node map. The disclosed platform can evaluate the effect of the perturbations by comparing respective outputs against ground-truth data. Perturbations can be automatically determined based on changes in external datasets, compliance policies, or operational requirements. The perturbations can be implemented when a computed perturbation quality value satisfies a threshold quality criterion. As such, the system enables efficient, policy-compliant evolution of relational system architectures in dynamic environments.

Systems and methods of scoring quality of open source software documentation are disclosed. In one aspect, a method includes downloading the open source software project code and documentation of the open source software project code, analyzing the open source software project code and the documentation, parsing the open source software project documentation into sections, validating the sections of the documentation with project or stack metrics, assessing a quality of sections of the documentation, assessing a quality of language of the documentation; and computing the quality score of the documentation based on the assessed quality of sections and assessed quality of language.

Methods and systems for assessing a software bill of materials (SBOM) include extracting package names from code of an application to identify first dependencies of the application. Package names are extracted from accesses by the application to identify second dependencies of the application. A quality score is generated for an SBOM of the application based on a comparison of the contents of the SBOM to the first dependencies and the second dependencies. An action is performed responsive to the quality score.

The systems and methods provide a model deployment criterion. The model deployment criterion indicates a difference in a value against which the proxy model may be measured to determine when, if ever, the proxy model should be deployed to replace the existing rule engine. The model deployment criterion may be keyed to the proxy model (e.g., based on a difference in its size, throughput speed, number of changes, etc.), the existing rule engine (e.g., based on a difference in its age, update occurrences to its rule base, etc.), and / or comparisons between models (e.g., based on differences in results, throughput speed, efficiency, etc.).

A method for developing and deploying a machine learning pipeline is provided. The method may be implemented by at least one processor. The method may include collecting different types of data from one or more data sources, wherein the different types of data includes batch data and real-time data; continuously processing the data to build a plurality of layers of a Lambda application, wherein each layer is configured to be deployed independently of the other layers; building a modular machine learning pipeline in a modifiable reference file, wherein the modular machine learning pipeline defines a framework comprising a no-code / low-code parameterized machine learning framework; deploying the modular machine learning pipeline as a single deployment machine learning pipeline that can be modified for different use cases by revising the source code of a CI / CD (Continuous Integration and Continuous Delivery / Continuous Deployment) pipeline, while the framework of the machine learning pipeline remains unchanged.

Systems, methods, and devices that relate to assessing uncertainty associated with entities are disclosed. In one example aspect, the method receives artifacts relating to an entity and categories for assessing uncertainty. For each category, a generative model retrieves and standardizes data points from the artifacts. A rule-based model inputs the standardized data points to output a rating. The generative model then generates an assessment of the rating and data points according to a predefined structure. The method outputs a summary, rating, and standardized data points for each category. These outputs can be used by other systems for assessing the uncertainty of the entity and taking action based on the assessment.

A system for automated code modification improves application performance in cloud environments by integrating telemetry analysis with large language model (LLM)-driven reasoning. The system collects contextual information about a target application, including metadata, source code, and configuration files, and correlates it with real-time telemetry data related to application performance. Based on this data, the system constructs a structured LLM prompt using a predefined schema, which is transmitted to an LLM. The prompt instructs the LLM to recommend modifications to source or configuration files that may enhance performance, along with natural language explanations for those recommendations. In response to receiving the LLM's response, the system extracts the proposed code or configuration changes and associated rationale, and presents them to a user via a client device interface.

A method by a computing system to determine a resource configuration for a function as a service application. The method includes generating, for each of one or more functions of the application, a performance profile of the function that indicates performance characteristics of the function with respect to an amount of resources allocated to the function, determining a resource configuration for the application based on an optimization objective and the performance profiles of the one or more functions, and deploying the application using the resource configuration determined for the application.

The invention discloses a code generation method, which comprises the following steps that: a code development platform receives input information of a user in a first code file in a project, acquires a context in the file and a first cross-file context according to a static structure of the project, and acquires a second cross-file context according to behavior characteristics of the user in a development project, obtaining a third cross-file context according to the evolution coupling degree of at least one second code file in a code warehouse of the project and the first code file, and generating prompt information according to the input information, the context in the file, the first cross-file context, the second cross-file context and the third cross-file context, and inputting the prompt information into the code generation model for reasoning to obtain at least one group of generation codes, and displaying the at least one group of generation codes to the user. According to the method, the multi-dimensional item-level context and the user input serve as the input of the code generation model, the capability of the code generation model for utilizing the cross-file context in the code generation process is improved, and the code generation effect is improved.