504results about "Software metrics" patented technology

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 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 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 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.

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, 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.

Disclosed herein are system, method, and computer program product embodiments for detecting anomalies during software testing. The methods include generating a plurality of test reports for the software program by executing one or more test cases on a plurality of versions of the software program, generating a control chart based on the plurality of test reports, generating an alert when at least one testing characteristic includes an anomaly over the plurality of versions of the software program as determined based on the control chart. The control chart includes a plot associated with at least one testing characteristic of the software program, and a historical context associated with execution of the one or more test cases on the plurality of versions of the software program.

Large language model code translation error detection include receiving a code portion of a first programming language, and converting the code portion to a second programming language. A first accuracy of the converting of the code portion to the second programming language is calculated. A difference between the first accuracy and an historical accuracy of a conversion from the first programming language to the second programming language is determined. A potential error in the code portion of the first programming language is indicated based on the difference between the first accuracy and the historical accuracy being greater than a predetermined value is indicated.

Using complexity metrics to assess code generated using artificial intelligence includes generating, by an artificial intelligence (AI) language model, output source code based on input source code; identifying respective complexity scores for the input source code and the output source code using one or more complexity metrics; and generating, based on an evaluation of the respective complexity scores, a validation score for the output source code.

To support the evaluation of the impact of vulnerabilities in an information processing system composed of sub systems built in different environments on each sub system.SOLUTION: A vulnerability impact evaluation support device 40 comprises: a memory device that stores software component information, the software component information being information about software that constitutes each of a plurality of subsystems constructed in different environments that constitute an information processing system; and a control device that creates extended software component information including information about a second subsystem that is affected by a processing impact caused by a vulnerability in software of a first subsystem within the information processing system, identifies other subsystems that are affected by a processing impact caused by a vulnerability in software of a selected subsystem based on the extended software component information, and outputs information about the identified subsystems based on the software component information.SELECTED DRAWING: Figure 1

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.

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.

A method includes accessing a first set of instructions corresponding to a first instance of a software application and a second set of instructions corresponding to a second instance of the software application; and determining a first interaction context associated with the first instance of the software application and a second interaction context associated with the second instance of the software application. The method further includes accessing a set of security roles assigned during a development phase of the first instance of the software application and the second instance of the software application, executing a machine-learning model trained to identify whether a security roles is assigned to both the first interaction context and the second interaction context, and in response to identifying that the a security role is assigned to both the first interaction context and the second interaction context, generating a reassignment recommendation for reassigning the security role.

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.

Techniques are described for securing secrets in software build workflows. In some implementations, build instructions call for execution of a first program module and a second program module, where the first program module has been approved to make a privileged request, but the second program module has not. The first program module can be stored in a trusted repository, separately from the second program module. When the first program module is loaded for execution, a cryptographic signature can be validated to determine that the first program module is authentic and as a condition for passing a privileged credential to the first program module. The second program module has no access to the privileged credential. Instead, when the second program module is loaded for execution, a determination can be made whether the second program module makes any privileged requests. Any privileged requests from the second program module will not be fulfilled.

A computer-implemented method including quantifying pre-defined technical porting goal of a porting project into a technical value model, receiving selected decisions regarding the target system and porting tasks and current version of the porting system, executing traceable porting steps based on the selected decisions, porting tasks and current version of the porting system and creating software artefacts of the target system, evaluating software artefacts created or converted by each of the executed porting steps and / or evaluating test results by applying and / or simulating each ported software artifact, outputting references to the versions of created and / or converted and evaluated software artifacts into a porting log, and training the creation and evaluation component of the porting system with data of the porting log based on the decisions and the outcome of the porting project using porting mechanism to reach the pre-defined goal for the porting system.

Using cross-compilation to determine translation accuracy of artificial intelligence generated code includes receiving a first code portion of a first programming language, converting the first code portion to a second code portion of a second programming language by a generative artificial intelligence model, and converting the second code portion to a third code portion of the first programming language by the generative artificial intelligence model. A translation accuracy score of the converting of the first code portion to the second code portion is calculated.

A continuous integration (CI) pipeline can be executed based on a type of content for a change to source code. For example, a computing system can receive the change to the source code of a software application in response to a request for changing the source code being generated. The computing system can identify the type of content included in the change to the source code. Specifically, the computing system can determine whether the type of content is associated with a prevention of an execution of the CI pipeline. In response, the computing system can perform an action associated with the change to the source code prior to a deployment of the change to the source code.

A method and a system for obtaining an evaluation of a quality of software code that is generated by using a large language model (LLM) are provided. The method includes: receiving a set of instructions for performing a task and generating an output; providing, as an input to an LLM, a list of available application programming interfaces (APIs) and the instructions, together with a submission of a request to the LLM to select one API and to generate a set of executable code based on the instructions; receiving, from the LLM, a selection of one API and the set of executable code; executing the set of executable code in order to perform the first task and generate the output; and evaluating an accuracy, a robustness, and / or a consistency of the set of executable code.

Data are mapped from multiple tools to a common information model during the development life cycle of a software application. The common information model normalizes the data, enabling the data to be correlated even when development tasks are performed by separate entities using different tools. Using the common information model, security issues are identified in a later part of the software development life cycle based on data generated at an earlier phase, such as on an ongoing basis throughout the life cycle. A user can investigate the security issues and associated risk using an interactive dashboard.

Methods, systems, and computer-readable storage media for receiving a crash report provided as a computer-readable file, providing a stack trace from the crash report, adding component information to the stack trace, for each component identified in the stack trace, determining a set of features, processing sets of features through a ML model to provide a prediction identifying a component as a bug component, and assigning the bug component for resolution through a crash management system.

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.

Apparatus, methods, and computer program products for outputting a software development network environment escalate action aggregation interface component to a software development network environment operations action interface in a network environment operation and escalation system are provided. A method may include accessing non-software development network environment escalate action data, determining a non-software development network environment escalate action characteristic set based at least in part on the non-software development network environment escalate action data, the non-software development network environment escalate action characteristic set indicative of at least one non-software development network environment escalate action item, generating a software development network environment escalate action aggregation interface component comprising the non-software development network environment escalate action item, and outputting the software development network environment escalate action aggregation interface component for rendering to a software development network environment operations action interface of a computing device.