58results about "Software metrics" patented technology

In some implementations, a controller may receive a first unstash command, associated with a first node and a first workspace, to unstash a first set of files. The controller may perform an unstash operation for the first set of files, in response to the first unstash command, based on a first stash variable lacking an indicator associated with the first set of files and a global stash variable including the indicator associated with the first set of files. The controller may receive a second unstash command, associated with a second node and a second workspace, to unstash a second set of files. The controller may refrain from performing an unstash operation based on a second stash variable including an indicator associated with the second set of files.

Various methods and processes, apparatuses / systems, and media for improving performance of an agile project management tool (PMT) are disclosed. A processor trains a machine learning model (MLM) with historical story point data corresponding to a project to be developed via the PMT; implements a clustering algorithm that automatically generates T-shirt size categories based on the historical story point data; dynamically generates first mappings data that corresponds to relationships between the T-shirt size and the historical story point data at various levels; implements a learning algorithm onto the PMT for the MLM to continuously learn and adjust mappings based on new project data thereby improving performance of the PMT, providing recommendations data that evolve over time; displays the recommendations data onto a graphical user interface (GUI) that provides a platform for decision making and planning in completing the project; and receive user input, via the GUI, to complete the project.

A method and system for calibrating a simulator instance is disclosed. Specifically, the method and system disclosed herein may receive, by a workload generator, a trace file specifying operations; generate, by the workload generator, a workload based on the operations, wherein the workload includes one or more read or write requests corresponding to the operations of the workload; receive, by the simulator instance, the read or write requests from the workload generator; and process, by the simulator instance, the read or write requests based on a set of simulation parameters to generate simulation results, wherein the simulation instance is calibrated, at least in part, based on the simulation results.

A method and system according to which an online application platform for a client financial institution may be built. The online application platform may include a default configuration, which includes a default theme and a default application framework. A management interface is accessible by the client financial institution. The management interface includes a framework tool for converting the default application framework into a customized application framework. The management interface includes a theme tool for converting the default theme into a customized theme. A customized configuration of the online application platform is provided to applicants of the client financial institution.

The system according to this embodiment aims to automate the software testing process and improve development speed. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, an execution unit, and a reporting unit. The analysis unit analyzes the software specifications and code. The generation unit generates test cases based on the information collected by the analysis unit. The execution unit executes the test cases generated by the generation unit. The reporting unit reports the results obtained by the execution unit.

In some implementations, a development system may receive a set of computer code associated with a user. The development system may provide the set of computer code to a machine learning model to receive a set of quality indicators associated with the set of computer code. The development system may output, to a user device, the set of quality indicators. Additionally, the development system may determine, using the set of quality indicators, a possible mentor out of a plurality of additional users. The development system may transmit a message from the development system and to a device associated with the possible mentor.

A system can receive data that includes a first subset and a second subset relating to a first application and a second application in a cloud computing environment. The system can determine differences between the first subset and the second subset by comparing the first subset and the second subset. The system can generate, by using the differences, quality metrics for a transformation from the first application to the second application in the cloud computing environment. The system can generate, by using historical data that includes historical first subsets and second subsets, a trend that represents a progression of the transformation. The system can determine, by using the differences, the quality metrics, and the trend, code in the second application that is likely to cause the differences. The system can generate a command that is executable in the cloud computing environment to automatically update the code in the second application.

The present invention relates to a method for estimating the efficiency of a software architecture (40) formed by at least one hardware block (44) and a software block (46) comprising a step of: - receiving a set (42) of blocks (44, 46) forming a software architecture (40), - for each of the blocks (44, 46), obtaining the elementary efficiency of the block (44, 46) by reading a database, the database associating with each block likely to be part of a software architecture an elementary efficiency, - for each of the blocks (44, 46), determining the relative contribution of each block (44, 46) to the efficiency of the software architecture (40), and - estimating the efficiency of the software architecture (40) from the elementary efficiencies obtained and the relative contributions determined.

Disclosed herein is a software solution and platform that integrates and combines multiple interchangeable software components using custom data pipes. The software solution and platform, as well as the multiple interchangeable software components, can include machine learning (ML), artificial intelligence (AI), and generative AI. The software solution and platform is a processor executable code or software that is necessarily rooted in process operations by, and in processing hardware of, computing equipment. For ease of explanation, the software solution and platform is described herein with respect to an integration engine.

Computer system security is improved by implementing zero-trust principles for management of source codes in a software development lifecycle. In some embodiments, a source code may be processed with a natural language processor to attribute the source code to its particular developer. Further, a machine learning algorithm may be applied to data related to the coding behavior of the code developer to identify any behavioral anomalies of the code developer in developing the source code. In addition, the interaction, of applications executing in an execution environment of an organization's network of managed compute facilities, with the various components of the network may be analyzed with a machine learning algorithm to identify code execution anomalies.

A method for deploying no-code chat applications includes receiving, from a user, a chat application deployment request requesting deployment of a chat application. The chat application is derived from a no-code application that was generated using a no-code application development environment. The method includes obtaining credentials associated with the user. The method includes generating, using the credentials, a project container for the chat application. The method also includes generating, using the no-code application and the no-code application development environment, the chat application at the project container. The method includes receiving, from the user, parameters for the chat application, the parameters defining user interaction with the chat application. The method also includes configuring, via an application programming interface (API), the chat application using the parameters and deploying the chat application.

The invention relates to a computer-implemented method and technical system for ensuring a quality standard for a code sequence (CS) generated with the aid of a generative AI model (LLM) to fulfill one or more features of a software application to be developed in a software development project, wherein the quality standard is defined by a minimum degree of fulfillment of this or these features, optionally according to their classification in the software development project. The method comprises the following steps, which can be executed in particular with the aid of at least one processor of the technical system (S): a) Extracting this / these feature(s) from artifacts of the software development project, classifying them if necessary, and relating them to each other if there are several features, whereby the quality standard is defined by a minimum degree of fulfillment of this or these features, possibly according to their relation to each other and possibly according to their classification in the software development project; b) Providing one or more rejected code sequence examples in a memory (REP) derived from the information obtained in step a) that fall below the specified minimum level of fulfillment and / or that result from previously rejected code sequences generated by the generative AI model; c) Enriching at least one captured prompt (P), which initiates the generation of the code sequence by the generative AI model, with context formed from the one or more extracted features and with the one or more sorting examples, and applying the at least one enriched prompt to the generative AI model; d) Capturing the code sequence generated using the enhanced prompt and evaluating the generated code sequence based on the quality scale; and e) Outputting the generated code sequence if the result of the evaluation from step d) is positive, by meeting or exceeding the specified minimum level of fulfillment.

In some aspects, the techniques described herein relate to a method including: receiving, at a credential broker, a request including a unique identifier of a client application, wherein the request is from a token software development kit; querying, by the credential broker, an application attestation datastore using the unique identifier of the client application as a lookup key; receiving, at the credential broker and as a result of the querying, a service identifier for a network service; requesting a service ticket from a key distribution center, wherein the service ticket facilitates authentication with the network service; and responding to the token software development kit with a return communication, wherein the return communication includes the service ticket.

The techniques disclosed herein provide a system for automated deprecation of software codebases using a self-training analysis agent and a weighted input representing a plurality of codebase factors. Generally speaking, operating a cloud computing platform is complex and can involve managing a large number of software codebases including source code, infrastructure, configuration, and user applications. Consequently, as new products and / or services are released, technology advances, and various software codebases age, portions of the cloud computing platform may become vulnerable to errors, malicious actors, and other undesirable behaviors that lead to degraded service quality. The system presented herein utilizes a self-training analysis agent to automatically identify a given software codebase for deprecation using a weighted input representing a plurality of factors relating to the codebase such as code type, a code age, and usage metrics. Accordingly, the codebase is archived for a predefined time period prior to final deprecation.

Systems and methods for implementing a FIPS compliance check within the software development process include receiving a Software Bill of Materials (SBOM) associated with software in production; performing a vulnerability scan based on the SBOM; extracting a list of dependencies of the software based on the SBOM and generating a list of cryptographic dependencies associated with the software; and generating a FIPS compliance report for the software based on the vulnerability scan and the list of cryptographic dependencies.

The system according to this embodiment aims to automatically detect bugs in application development and propose corrective actions. [Solution] The system according to the embodiment comprises a detection unit, a proposal unit, an analysis unit, and a response unit. The detection unit detects bugs. The proposal unit proposes a correction based on the bug detected by the detection unit. The analysis unit analyzes the code in real time based on the correction proposed by the proposal unit. The response unit supports multiple programming languages ​​based on the code analyzed by the analysis unit.

Apparatus and methods for creating a real-time functional code reviewer with an architecture placemat are provided. The apparatus and methods may include a server with an artificial intelligence / machine learning (“AI / ML”) real-time functional code reviewer program. The real-time functional code reviewer program may receive source code in a markup language for review. The program may analyze the source code. The program may generate functional context feedback, review feedback, and an architecture placemat. A code reviewer, through a user device, may review the functional context feedback, review feedback, and the architecture placemat. The code reviewer may provide feedback to the source code developer.

A method includes receiving, from a client device associated with an enterprise, information regarding software code, generating, via a large language model (LLM), a recommendation regarding the software code based on the information and a plurality of software library profiles, wherein each of the plurality of software library profiles is associated with a corresponding software library of a plurality of software libraries associated with the enterprise, and wherein each of the plurality of software library profiles indicates a corresponding key performance indicator (KPI) associated with usage of the corresponding software library, and providing, to the client device, the recommendation.

A de-centralized distributed bot system for deployment of automation bots includes distributed agents that execute on client devices, such as desktop machines or the like. The agents poll a remote repository that stores the bots to discover new bots to download to storage local to the agent, or updates for bots the agent has already locally-cached. The agents each have an agent-based user interface for browsing and managing bots, such as the bots in the local cache. In response to selection, via the agent-provided interface, of a bot for execution, an executable for the selected bot is obtained and locally executed. The agent may generate related logs and transmit the logs to a metrics, analytics and alarm service. Bot developers may submit bots to the remote repository for storage via a deployment pipeline service that submits bots to the remote repository using a publishing manager.

A system and methods are described to manage, store, and enhance Software Bills of Materials (SBOMs) that accompany and describe the contents of a particular software product. The platform can import an initial SBOM as might be provided by a developer or vendor of a software product. The initial SBOM is analyzed to identify initial dependencies implicated within the software product. The platform will then schedule a mock software build including these identified dependencies. During this build process, further hidden dependencies are identified which were not apparent in the initial SBOM. The build process continues recursively until all dependencies have been identified, thus ensuring complete visibility of the components that make up the software product. Once all dependencies are identified, the system can produce an enhanced SBOM report that lists these dependencies and assesses their risks. An enhanced SBOM can be generated from the report.

Embodiments relate to methods and systems for managing productivity. The method includes a setup process, including identifying team information. The setup process includes generating positive and negative set of metrics. The method includes performing a productivity assessment process, including obtaining a productivity value for each metric. The productivity assessment process includes performing a uniformity process, including selecting positive maximum and positive minimum values for each metric in the positive set of metrics; and selecting negative maximum and negative minimum values for each in the negative set of metrics. Positive and negative metric scores are determined. The method includes generating one or more productivity scores for each individual or the team.

Exemplary embodiments may provide an integrated system for assessing application fitness. The integrated system may provide a complete and accurate picture of the fitness of all or a portion of the applications used by an organization. The integrated system may ingest information about the applications from multiple sources and may assess the fitness of the application in multiple aspects. Metric scores for the fitness of each application may be generated and displayed on a user interface, such as on dashboards. The metric scores may reflect various aspects of the fitness of the applications. For example, in some exemplary embodiments, the metric scores may reflect software development life cycle (SDLC) standards, non-functional requirements (NFR) standards, and / or application stability standards. In addition, the exemplary embodiments may determine an overall fitness metric score for each of the applications. Visualizations of the metrics may be displayed on user interfaces, such as on the dashboards.

Various methods and processes, apparatuses / systems, and media for automated data governance are disclosed. A processor creates a data catalog that includes metadata identifying what information to be collected, by whom, and at what stage of each identified phase of a product development life cycle (PDLC) process corresponding to the application; embeds the data catalog, a data governance tool, a continuous integration / continuous delivery (CI / CD) pipeline, and a scheme evolution component into the PDLC process by calling corresponding application programming interface; executes a data governance by association algorithm; and updates the data catalog with schemas based on the data governance by association algorithm. Each data producer and data consumer in connection with the developing, the testing, and the production of the application manages the data governance in synchronization during execution of each identified phases of the PDLC process.

The system according to this embodiment aims to efficiently detect potential bugs and implementation omissions by mimicking the actions of service users. [Solution] The system according to the embodiment comprises a user characteristic instruction unit, a test case generation unit, an operation verification unit, and an evidence recording unit. The user characteristic instruction unit instructs the characteristics of a user to be imitated. The test case generation unit generates test cases based on the characteristics instructed by the user characteristic instruction unit. The operation verification unit performs operation verification based on the test cases generated by the test case generation unit. The evidence recording unit records the results obtained by the operation verification unit.

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. 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.

The application discloses a kind of online checking methods of function block based on compiling implantation characteristic code, by reading the source code file of function block, the name of function block in it is parsed, interface etc definition information, automatically generate function block file, improve the accuracy and efficiency of generating function block file;The application uses the CRC value of function block to compare the similarities and differences of function block, reduces the calculation amount of online comparing function block, improves the efficiency of online comparing function block;The CRC value of function block is used to compare the similarities and differences of the model structure of function block in page, reduces the calculation amount of online comparing page, improves the efficiency of online comparing page;The CRC value of function block is embedded into the executable file of function block in the compiling process, for subsequent online CRC comparison with the corresponding function block in the page issued by configuration tool, the consistency of function block in configuration tool and function block actually scheduled and run in controller can be verified online.

Method for analyzing the consumption of material resources by an operating system of an embedded computing system in a motor vehicle, associated device and computer program. This method, implemented by an analysis device comprising a communication interface module configured to communicate with the operating system via a connectivity and interaction protocol, includes the steps of: initialization (46) of a connection to said operating system;sending (50) to the operating system at least one instruction to collect data representative of the consumption of at least one type of hardware resource by the operating system, said at least one instruction being in the form of at least one command or script executable by said operating system, retrieval (58) of said collected data by the operating system, and analysis (60) of said collected data to obtain elaborated data from said collected data and generate a textual and / or graphical description of hardware resource consumption from said elaborated data. Figure for the abbreviation: Figure 2;

An embodiment detects by a Policy Compliance Orchestrator, an infrastructure code, and determines an infrastructure change from the infrastructure code. The embodiment sends the infrastructure change by the Policy Compliance Orchestrator to a Budget Component, which computes a budget metric based on the infrastructure change. The embodiment sends the infrastructure change by the Policy Compliance Orchestrator to a Pricing Component, which computes a pricing metric based on the infrastructure change. The embodiment sends the infrastructure change by the Policy Compliance Orchestrator to a Monitoring Component, which computes a utilization metric based on the infrastructure change. The embodiment also sends the budget metric, the pricing metric and the utilization metric by the Policy Compliance Orchestrator to an Analytics Engine wherein the Analytics Engine computes an impact metric where the infrastructure code is updated with the impact metric.

Disclosed techniques relate to instrumenting applications. In an example, a method involves providing a web page application with a tracer application. The method further involves accessing a source of the web page application. The method further involves detecting a reference to an element of the web page application in the source. The method further involves detecting the user interaction with the web page application. The method further involves automatically logging a start of a span based on the detection of the user interaction. The logging includes associating the span with the tracer application. The method further involves executing operations relating to the element. The method further involves determining that the element is ready for additional user interactions. The method further involves automatically logging an end of the span based upon the determining.

A system and a method are disclosed for identifying libraries used by an application based on the program code of the application. The system accesses a plurality of program codes of the application. For at least one program code, the system extracts raw type data from the program code and normalizes the raw type data to generate target normalized type data. The target normalized type data includes data type information of the program code. The system determines a set of candidate library types corresponding to the program code. The system accesses candidate raw type data associated with each candidate library type and determines a library type corresponding to the program code by comparing the program code of the application with the program code of each of the set of the candidate library types.