330results about "Software metrics" patented technology

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.

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, 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.

The invention provides an intelligent analysis method, system, equipment and program product for automatic code review, which are applied to the field of computer software engineering and development tools, and the method comprises the following steps: obtaining code difference data, preprocessing to generate a semantic code unit, constructing a structured cue word and inputting the structured cue word into a large language model, according to the scheme, the problems that in existing code review, semantic understanding is insufficient, and efficiency is low can be solved, automatic risk identification and analysis of code changes are achieved, and review efficiency and accuracy are improved.

An assessment system can generate a software quality value based on testing results and analysis of a multitude of factors that impact a readiness evaluation. For example, the system generates a software quality score (e.g., an Applause Quality Score “AQS”) that enables development teams to understand the level of quality they are achieving for a given release and build-over-build. In various examples, the system generates a data-driven score to enable development teams or quality assurance teams to make decisions for when a build is ready for release. In further embodiments, the system can integrate user interfaces that present a software quality score in a user dashboard that is linked to version control systems. On review and acceptance of the score, a user can trigger the release of their new code or product.

The invention relates to the technical field of code development, and provides an AI code effective proportion statistical method and device, a medium and equipment. The method comprises the steps of obtaining related information of codes submitted by a user; according to a user name in the related information, searching log information of an AI code generated by a corresponding user through adoption of a code generation tool; under the condition that the file name of the code submitted by the user is matched with the file name in the log information, searching a corresponding submitted code segment from the code submitted by the user according to the mark information in the log information; and calculating the similarity between the AI code and the submitted code segment, and counting the effective proportion corresponding to the AI code according to the similarity. Therefore, the calculation of the effective proportion considers the quality of the AI code, so that the effective proportion can accurately reflect the real contribution of the AI code, and the calculation process is automatically realized, thereby avoiding the tedious, time-consuming and labor-consuming conditions of manual labeling.

The invention discloses an automobile industry code quality automatic compliance method based on a large language model. The method comprises the following steps: calling a static code analyzer to generate a violation report; the method comprises the following steps: intelligently classifying violation into Tier1, Tier2 and Tier3 based on a multi-feature analysis algorithm of a rule document; for Tier1 violation, performing three times of retry iteration repair by using a large language model, automatically detecting compilation errors and performing rollback; for Tier2 violation, multiple technical schemes are extracted by a large language model for a user to select; a multi-LLM antagonistic cooperation mechanism is adopted for Tier3 violation, automatic restoration of complex violation is achieved through a Generator generation restoration scheme, a Disscriminator review challenge scheme and antagonistic iteration of Arbiter arbitration conflicts, and an optimal scheme is selected through a weighted voting algorithm when conflicts occur. The method obviously reduces the manual intervention rate, improves the repairing quality, and is low in cost and high in universality.

The application 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, which comprises the following steps: performing semantic analysis on business requirements, and constructing a business association graph with weights; performing module division to decompose a complete task into multiple subtasks; calling a coordination agent, assigning a corresponding large model-based module code generation agent to each subtask after decomposition, and injecting context information to generate corresponding module code; wherein the module code generation agent has been pre-trained and learned the corresponding relationship between the task and the injected context information and the code based on domain adaptation in stages; and calling a verification agent to verify the code quality. Advantageous effects: the module division is performed based on the business association graph with weights, the multi-agent collaborative code generation is constructed by adopting the domain adaptation and stage-by-stage training, and the quality of the generated code is verified, thereby improving the code generation efficiency and quality.

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 for detecting secrets in deleted software development platforms (SDP) are disclosed herein, including querying an SDP for account and repository data, the querying being based on a customer name, identifying previously existing content that is no long present in a current version of the SDP, reconstructing the previously existing content, analyzing the reconstructed content for one or more indicators of sensitive information, and generating a report based on the analysis.

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.

Systems and methods are provided for proactive dependency management in software development projects, including initiating a dependency scan within a version-controlled repository to identify and list project dependencies at predetermined intervals, and conducting a health analysis for the project dependencies listed by accessing and utilizing data from a plurality of vulnerabilities databases, the analysis uncovering current security vulnerabilities and assessing a frequency and recency of maintenance updates. A multifaceted criteria matrix is applied to analyzed dependencies to isolate those that exhibit indicators of potential risk, including known security vulnerabilities and evidence of neglect of updates and maintenance. A set of alternative dependencies is aggregated for identified at-risk dependencies by leveraging public repository analysis to discern commonly adopted replacements, further evaluating the alternative dependencies for compatibility with a technology stack of the software development projects and adherence to security and maintenance standards.

One embodiment of a method for modifying program code includes processing, using a first trained language model, program code to identify one or more modifications to the program code; processing, using a second trained language model, the program code and the one or more modifications to generate a plan for applying the one or more modifications; and processing, using a third trained language model, the program code and the plan to generate a modified program code.

In a first use case, a description of source code to be created is validated and used to form a prompt for a large language model (LLM). The prompt is provided to the LLM to generate an output. The output from the LLM is post-processed to generate the source code. In a second use case, a source code file is processed and provided, along with instructions, to an LLM. The LLM provides a list of the methods in the source code file. The LLM is requested to provide a unit test for each of the listed methods. The output from the LLM is post-processed to generate unit tests for the methods in the identified source code file. In a third use case, the LLM is used to generate unit tests for new methods added to the source code file after unit tests were generated.

Systems, devices, methods, and computer-readable media for source of truth storage device management are provided. A method can include receiving, by an event application programming interface (API), a first request to update source of truth data in a source of truth storage device to a new value, formatting, by the event API, a second request, issuing the second request to the source of truth storage device, and updating, by the source of truth storage device, an entry that corresponds to the source of truth data to the new value.

The embodiment of the invention provides a resource optimization method and system based on function point measurement software scale evaluation, and belongs to the technical field of software measurement. The method comprises the steps of selecting an evaluation mode based on current demand scene information, if the current demand scene information is matched with a historical scene, calling a precipitated function point evaluation baseline and generating a baseline value through consistency verification and increment measurement adjustment, and if the current demand scene information is not matched with the historical scene, entering a function point identification process and generating a function point identification result; determining a function point scale based on the baseline value or the function point identification result and outputting a corresponding workload result; based on the function point scale and the workload result, generating evaluation data for resource allocation in combination with historical scheduling data and a resource allocation relationship; and outputting a resource allocation suggestion based on the evaluation data. According to the scheme, the accuracy of software scale evaluation is improved, and executable resource allocation suggestions are generated.

A system and method for generating computer code are provided. The system receives data indicating a trigger event to initiate code generation and selects a code generation agent based on attributes of the trigger event. The selected agent requests indexed context information from a context indexing component. The context indexing component generates indexed context information including project data associated with a user account, such as existing code, text data, file structure data, open file information, and project documentation. First embeddings are generated from the processed project data and stored in a vector database configured to be queried by the code generation agent. The system can further generate environmental data, hierarchical file structure summarizations, and indexed external data, creating additional embeddings stored in the vector database to provide comprehensive context for code generation.

An innovative system for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. By leveraging advanced AI techniques, seamless data synchronization, intelligent task management, and predictive workflow optimization across multiple SaaS platforms are described. Key components include an AI-driven data synchronization system, an intelligent cross-platform task manager, and an adaptive user interface.

An Artificial Intelligence (AI) driven pull request summarization system utilizes a large language model to classify the code changes of a pull request and to generate a summary of the changes contained in the pull request. The large language model predicts the pull request classification and summarization given a prompt that includes the top-k code changes in the pull request of a repository. The code changes are prioritized based on the most impact that a code change has on the files, methods and classes in the repository. Each of the top-k code changes is linked to a related open issue of the repository, if any. A suggested code reviewer for a code change is then selected from an author or commentator associated with the linked open issue.

Provided is a method and system (108) for building and leveraging a knowledge fabric (110) in a Software Development Lifecycle (SDLC). A plurality of SDLC artifacts are received from a plurality of heterogeneous data sources (102). The plurality of SDLC artifacts are then correlated to build an end-to-end correlation and are clustered to generate an SDLC knowledge fabric (110). This includes extracting semantic and contextual data from the plurality of SDLC artifacts using Natural Language Processing (NLP) and deep text analytics and transforming the extracted semantic and contextual data to knowledge graphs. One or more actionable items (112) are then derived using the SDLC knowledge fabric (110) and the one or more actionable items (112) are used to improve overall process efficiency and accelerate software delivery in the SDLC.

System for adaptive workflow orchestration based on engineering efficiency metrics, including: an interface for metric collection that is configured to retrieve real-time performance data from software development and deployment tools; an evaluation engine that is operated to calculate a dynamic efficiency index based on the aforementioned performance data; an orchestration engine configured to adapt active workflows by changing the task order, execution parameters, or control flow in response to changes in the efficiency index; a workflow transformer that is operated to convert workflow specifications between multiple automation formats while preserving logical task dependencies; and a monitoring interface that is operated to display workflow status, performance scores, and customization history for end users.

Presented herein are systems and methods for optimizing legacy source code using generative models. A computing system may generate, for each function of a plurality of functions of a software program, a performance profile comprising a performance attribute during runtime. The computing system may identify, from the plurality of functions of the software program, a function based on the performance attribute of the performance profile for the function being lower than a threshold. The computing system may select first source code of the function for refactoring, responsive to identifying the function. The computing system may provide the first source code of the function to a generative model to determine refactoring opportunities within the first source code. The computing system may generate, based on providing the first source code to the generative model, second source code for the function corresponding to the refactoring opportunities within the first source code.

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for identifying stale or vibrant open-source packages by training a predictive machine learning model with a labeled dataset, wherein the labeled dataset is created by generating package-basis features and version-basis features based on repository data, generating package-basis clusters based on the package-basis features, generating version-basis clusters based on the version-basis features, and generating labels for the labeled dataset based on the package-basis clusters and the version-basis clusters.

A computer-implemented method for code classification is disclosed. The method comprises parsing source code stored on a code carrier to obtain a plurality of code structure characteristics of the source code, and evaluating the code structure characteristics on at least three code structure levels. In some embodiments, the at least three code structure levels comprise one or more of: a global level, a module level, an intermodule level, a function level, an inter-function level, and an implementation level. The evaluation on each code structure level comprises, for each of one or more level-specific conditions, determining whether the code structure characteristics violate the level-specific condition, and (when the level-specific condition is violated) representing the violation by a weighted value. The method also comprises combining the weighted values for at least some of the violated level- specific conditions over the at least three code structure levels to generate one or more code quality indicator for the source code. Corresponding computer program product, apparatus, and system are also disclosed.

To provide an information processing system, an information processing method, an assignment apparatus, a notification apparatus, an assignment program, and a notification program that reduce a time period required for feedback of an evaluation result regarding a program code.SOLUTION: A method includes calculating a first evaluation result regarding a first program code generated in the past by a user who has generated each program code for a problem corresponding to each program code, for each program code stored in one of a plurality of queues corresponding to each of a plurality of evaluation apparatuses performing evaluation of the program code when accepting a new program code, and a second evaluation result regarding a second program code generated in the past using a programming language corresponding to each program code for the problem corresponding to each program code, and specifying a first queue storing the new program code from the plurality of queues and storing the same based on the first evaluation result and the second evaluation result.SELECTED DRAWING: Figure 8

The invention relates to a method (100) for decomposing and distributing unitary applications, comprising the steps of:-deploying (101) the unitary application on a data processing device (1),-generating (102) a behavioral performance analysis of the unitary application on the basis of an analysis of the unitary application during runtime,-distributing (102) the unitary application on the basis of the behavioral performance analysis generated. The method comprises the steps of determining (103) at least one possible solution to decompose the unitary application into modules (2), initiating (104) deployment of the modules (2) on a distributed system (3) to provide a distributed application, initiating (105) monitoring of runtime behavior of the distributed application, and determining (105) deployment of the modules (2) on the distributed system (3) by maximizing similarity between the unitary application and the distributed application. An optimization of the decomposition and deployment of the module (2) is initiated (106). Furthermore, the invention relates to a computer program, an apparatus and a storage medium for this purpose.

A system and method for providing third party compliance to computing environments without providing access thereto. The method includes: generating a representation of the computing environment, the computing environment including a plurality of identities; generating a software inventory of the computing environment utilizing a cybersecurity inspection technique; determining compliance of the computing environment based on the representation and the software inventory; and providing the determined compliance to a third party, wherein the third party is not associated with the plurality of identities.

To support appropriate selection of a cloud system configuration.SOLUTION: A computer system comprises a processor 111, a storage device 112, and an input-output device 141. The storage device 112 stores at least configuration condition information 132 indicative of an evaluation value about a system configuration configurable on a cloud. The input-output device 141 accepts a configuration request indicative of conditions for a system configuration configured on the cloud. The processor 111 calculates an evaluation value corresponding to the configuration request, compares the evaluation value corresponding to the configuration request with the evaluation value of the configuration condition information, and determines a system configuration candidate to be proposed on the basis of the comparison results. The input-output device 141 outputs the proposed system configuration candidate.SELECTED DRAWING: Figure 1