184 results about "Software development process" patented technology

The invention provides a semantic fusion graphical software development management method and system. The method comprises the following steps: acquiring a multi-user interaction behavior data flow containing an operation track sequence and an interface control triggering time sequence; according to operation track sequences of different users and a time-space association mode of an interface control triggering time sequence, constructing a dynamic semantic map; inputting a hierarchical mapping relationship in the dynamic semantic map into a preset edge computing node, and generating a real-time computing power distribution scheme matched with a logic dependency chain in the dynamic semantic map; analyzing a mapping relationship between a control triggering timestamp and a control type in the space-time association mode, and generating an interface control rendering priority queue; the synchronous regulation and control interface control renders the priority queue and an operation instruction response sequence corresponding to the logic dependency chain, and generates a graphical software development process synchronous instruction set; according to the method, the hierarchy and the dependency chain are fused to form the dynamic semantic graph, the hierarchical operation structure and the logic dependency relationship are constructed, and the high efficiency and the consistency of the graphical development process are ensured.

The disclosed technology provides for an improved approach to AI code generation. In various embodiments, the disclosed technology provides for an AI agent architecture platform for generating, revising, testing, and debugging code using a customizable team of agents with specific tasks.

The invention discloses a test case generation method and device and a medium, and the method comprises the steps: obtaining an existing source code and an existing test case of a corresponding item according to a test case generation request sent by a user; performing feature extraction on the existing source code and the existing test case to obtain code features and test case features; and inputting the code features and the test case features into a pre-trained generation model to obtain a target test case. According to the method, the test case is automatically generated through deep learning, so that the burdensome workload of a tester in writing the test case in a software development process can be reduced, the working efficiency is improved, and meanwhile, the problem of low test accuracy caused by incomplete case coverage due to human factors is also reduced.

Systems and processes are disclosed for enhancing cybersecurity and optimizing software repositories through integration of web crawling, web scraping, feature engineering, and advanced machine learning algorithms to detect phishing attempts, prevent account takeover fraud, and identify unused code in repositories. The system collects and refines data from various sources, including transaction logs, customer databases, device details, external data sources, and historical fraud data, to build comprehensive datasets. Feature engineering creates new, meaningful features from the refined data, which are used to train and evaluate machine learning models. The best-performing models are deployed in production to monitor incoming communications and transactions in real-time, flagging suspicious activities and optimizing codebases. This processing ensures timely detection and prevention of security threats while maintaining efficient software development processes. Robust protection is provided against evolving cyber threats and enhances software performance and security through continuous learning and adaptation.

The invention provides a code defect detection method and system based on a large model. The method comprises the following steps: generating an enhanced semantic understanding vector corresponding to an initial source code by utilizing a pre-trained large-scale language model in combination with an attention mechanism; identifying the logic error pattern to determine the defect type and position of the initial source code; the reinforcement learning algorithm selects an optimal repair path by simulating a plurality of repair paths and evaluating the effectiveness of each repair path, and generates a repair code snippet through the optimal repair path; replacing a corresponding part in the initial source code with the repair code snippet, and generating a target source code; performing quality scoring on the target source code based on the code quality evaluation model to obtain a quality feedback result; and outputting the target source code with the change annotation and the quality feedback result. According to the method and the device, the speed and the accuracy of finding and solving coding errors in the software development process are improved, and meanwhile, the quality of repaired codes is also ensured.

The embodiment of the invention provides a software development method based on artificial intelligence and related equipment. In the embodiment of the invention, an AI software development method and mode are provided, and a user and a technical design document generation agent cooperate to complete a technical design document of a programming task; and the programming agent autonomously performs task planning based on the technical design document, and autonomously executes the programming task based on the planned execution plan. In at least one of the technical design stage or the task execution stage of the programming task, a man-machine cooperation interface is provided, and the man-machine cooperation interface is used for a user to participate in the software development process for the programming task. Therefore, the software development efficiency and the code quality are greatly improved through division cooperation and man-machine cooperation interaction among the intelligent agents.

The invention provides a software development upstream and downstream task parameter transmission and optimization method based on artificial intelligence, which relates to the technical field of artificial intelligence and comprises the steps of constructing a parameter knowledge graph, processing parameter transmission data by using a graph neural network to generate a parameter dependency graph, monitoring parameter change in real time and analyzing an influence range. A parameter optimization decision model is constructed based on hierarchical reinforcement learning to generate an optimization scheme, so that parameter conflicts can be effectively reduced, the software development efficiency is improved, the system stability is enhanced, and intelligent management and optimization of parameter transmission in the software development process are realized.

The invention relates to the technical field of software engineering, and discloses a software development code review system and method based on reinforcement learning, and the system comprises a historical code feature collection module, a software development code operation parameter collection module, a code feature matching optimization module, a code feature optimization analysis module, and a code review result output module. The method comprises the following steps: extracting code features in a historical code library according to a time sequence, collecting code operation parameters in a software development process in real time, completing evaluation on software development code operation, performing feature matching optimization on the code operation parameters based on an evaluation result, obtaining an evaluation result of a to-be-evaluated operation code after matching optimization, and performing evaluation on the to-be-evaluated operation code according to the evaluation result. Based on the initial evaluation result and the feature optimization analysis result of the software development code, a code review result is output, early warning information is provided for the client through initial evaluation and matching optimization evaluation, and the code quality and safety in the software development process are improved.

The invention discloses a code adoption rate determination method and device, equipment, a storage medium and a product, and relates to the technical field of software development, the code adoption rate determination method comprises the steps that a code line information base comprising a first code is constructed, and a reverse index corresponding to the first code is constructed based on the code line information base, the first code comprises one of the following: an auxiliary code generated by a large language model, and a new code of a target code warehouse within a target time period after the auxiliary code is generated by the large language model; and matching a second code with the first code through the code line information base and the reverse index to determine the code adoption rate corresponding to the auxiliary code. Therefore, the technical problem that the actual contribution of the large language model in the software development process is difficult to quantitatively evaluate can be solved, and the technical effect of determining the actual contribution of the large language model based on the code adoption rate is achieved.

The invention relates to the technical field of software engineering management, and discloses a computer software development information management system and method. According to the method, real-time multi-dimensional association among four entities, namely a demand entity, a code entity, a test entity and a deployment entity is realized by constructing a dynamic knowledge graph center, and an end-to-end closed-loop control system is formed; a full-link traceable network is dynamically established through explicit binding, implicit capturing and cross-layer traceability mechanisms, demand omission marks are automatically repaired, and accurate attribution of environmental problems is achieved; manual operation blind areas are eliminated through automatic means such as developing and submitting forced association, automatically freezing branches when test fails and deploying environment consistency verification; based on a risk thermodynamic diagram of full-link topology penetration and fusion defect density and change frequency, active early warning of a high-risk module is realized, and controllability and quality assurance capability of a software development process are remarkably improved.

A method for real-time evaluating code leakage during software code development when the developer is using a code assistant tool, comprising performing code leakage estimation by identifying and processing, using an LLM-based model, the most updated code segments as the segments evolve; and evaluating, using the LLM-based model, the extent to which a written code has been inadvertently revealed to one or more code assistant servers by reconstructing the original code from the requests sent to each code assistant server. A method for real-time mitigating code leakage during software code development process when the developer is using a code assistant tool, comprising mitigating code leakage during code writing process by manipulating, using an RL agent, prompts and data being sent to a code assistant service provided by one or more code assistant servers.

The invention discloses a software development control system and method, and belongs to the technical field of software development, the software development control system comprises a task management module, a test management module, an evaluation adjustment module and a visual module which contain a block chain technology, the task management module is responsible for creating, distributing, checking, updating and deleting software development tasks for each current developer, and the test management module is responsible for providing a software development task for each current developer; the test management module is responsible for recording and counting test, time quantity, repair and defect conditions, and the evaluation adjustment module is responsible for sequentially calculating the defect rate Q, the development efficiency KX and the research and development quality YZ of tasks allocated by each current developer in the current month. By improving data security and transparency, accurately calculating defect rate and evaluation efficiency, scientifically and reasonably allocating tasks and comprehensively and quantitatively evaluating research and development quality, transparency and credibility of a software development process are improved, an evaluation mechanism for controlling task allocation and research and development quality is optimized, and scientific and reasonable task allocation is realized.

The invention relates to the technical field of software development data processing, and discloses a software development programming auxiliary data processing system and method based on artificial intelligence, and the system comprises a software development programming tool selection module, a software development editing code structure analysis module, and a software development editing code feedback module. Software development function demand text parameters are dynamically collected through voice input equipment, and accurate search of needed editing code structure parameters in the software development process is carried out in combination with an artificial intelligence algorithm and software development editing code structure parameters, so that intelligent output of multiple target editing code structure parameters in the software development process is realized; based on the use downloading amount of the software development target editing code structure parameters, the software development demand editing code structure parameters with the maximum use downloading amount numerical value are accurately analyzed in combination with numerical analysis, intelligent and standardized generation of the editing code structure parameters in the software development process is achieved, and the software development quality is improved.

The invention discloses an intelligent engineering software development process risk quantitative evaluation system, which belongs to the technical field of risk quantitative evaluation and comprises a data acquisition module, a data processing module, a dynamic risk knowledge graph module, a risk identification module, a risk quantitative evaluation module, a risk monitoring and early warning module, a risk decision module and a feedback optimization module. According to the method, the dynamic risk knowledge graph is constructed, so that the risk change in the software development process can be updated and reflected in real time. The relation network between entities is explored through the graph algorithm, potential risk events and risk factors can be found easily, the accuracy and timeliness of risk identification are improved, potential risk points in the software development process can be accurately identified by combining preprocessed data and the dynamic risk knowledge graph, and the risk identification efficiency is improved. Quantitative assessment of the risk is realized by defining the risk index and calculating the risk score.

The invention relates to the technical field of software testing, and particularly discloses an automatic inspection platform based on a grey box test, comprising a detection console module used for managing and controlling a test process; the grey box security test overall module is used for enabling the data flow generated by the test to penetrate through the network; the grey box security test network module is used for taking a rear-end server as a computing resource and a storage resource, and packaging the rear-end server into a service by a certain management server for external provision; the grey box security test function module is used for detecting newly generated vulnerabilities in the software development process in real time; and the grey box security test tool module is used for detecting security problems in the software. By using the instrumentation technology and the flow agent, real-time vulnerability detection is carried out on the running application and API in the research and development test stage, full life cycle management and multi-dimensional application security management and control of vulnerabilities detected by an application system are supported, and the method has the advantages of high coverage, low false alarm, real-time detection and the like, and can be seamlessly fused with a DevSecOps process.

The invention discloses a security management method applied to software development data, and relates to the technical field of data security management, and the method comprises the following steps: determining associated key data assets in a software development process, including source codes, configuration files, databases and user data, and classifying the key data assets according to the sensitivity and value of the data, and determining the security level of each type of data. By establishing a risk assessment framework, risk assessment can be performed on data assets regularly, potential safety problems can be found in time, corresponding risk management measures can be formulated according to the risk assessment result, and meanwhile, various safety events can be quickly responded by setting different early warning levels and formulating emergency response plans for the levels, so that the risk assessment efficiency is improved. In addition, through continuous risk assessment and monitoring, security vulnerabilities can be found and repaired in time, and the defense capability for new security threats is improved.

The invention relates to the technical field of software development and testing, and discloses an automatic testing integrated system in a software development process, which comprises a testing management module used for formulating a testing plan, designing a testing case and managing testing execution; the automatic testing module is used for integrating and executing unit testing, function testing, regression testing, performance testing and safety testing; the continuous integration and continuous delivery module is used for automatically constructing, testing and deploying the process; the environment management module is used for configuring, simulating and virtualizing a test environment; and a reporting and analysis module. Various testing tools such as unit testing, function testing, regression testing, performance testing and safety testing are integrated into one system, so that the problems of diversity and integration difficulty of the testing tools are effectively solved. Different types of testing tools are managed and coordinated in a unified mode, the complexity of the testing process and the compatibility problem between the tools are reduced, and the testing efficiency is improved.

The invention relates to the technical field of cloud computing data processing, and discloses a software development application data processing method and system based on cloud computing, and the method comprises the steps: obtaining application document data generated in a software development process, and carrying out the classification processing; merging a plurality of application document data of the same category into an archived file; and solving by utilizing the archived file storage optimization model to obtain a scheduling storage strategy of the archived file, and carrying out storage processing on the archived file according to a solving result. According to the method, the application document data is divided into application document data categories with higher semantic similarity and higher phrase approximation degree, the storage unbalance degree, the communication pressure and the load pressure are converted into a multi-target storage optimization function, optimization solution of a scheduling storage strategy is carried out with the minimum server unbalance degree and pressure as the target, and the optimization solution efficiency is improved. And the archived file is stored, so that the storage life of the server cluster is prolonged, and the load balance of data storage in the cloud computing process is realized.

The invention belongs to the technical field of software engineering, and particularly relates to a field-oriented environment association type scene software development method and system. The software development process is divided into three stages of field customization, scene configuration and application development, and a corresponding development system is provided and comprises a meta-tool environment, a field development platform and a scene application development platform. In the field customization stage, the meta-tool environment is used, and configuration is customized for a specific field to obtain a field development platform; in the scene configuration stage, environment-related scene configuration is carried out on the field development platform, intermediate representation of physical equipment is established, configuration access of various Internet of Things platforms is achieved through description, and the field development platform is configured as a scene application development platform; and in the application development stage, development of software in multiple fields is realized by multiplexing the environment-related scene. According to the method, the environment elements in the scene can be quickly configured, and the efficiency of developing and deploying customized scene software is improved.

The invention provides a data processing method and device, computing equipment, a storage medium and a program product, and the method comprises the steps: determining a target task, and distributing the target task to a target client; in response to a code submission request sent by the target client, processing submission information carried in the code submission request by using a task association model, and determining an association task corresponding to the code submission request; under the condition that the associated task is the target task, performing construction and test inspection on the target task; under the condition that the inspection is passed, a target version program corresponding to the target task is released, and a version report corresponding to the target version program is generated and sent to an associated client; the standardization and traceability of version release are improved, and the information transparency of cross-team cooperation is enhanced; the automation level, the quality guarantee capability and the cooperation efficiency of the software research and development process are optimized, and efficient and reliable version management can be achieved.

Provided is a method for identifying and repairing source code defects, which relates to the technical field of software security. The method is implemented based on a source code input / output (I / O) module, a source code defect detection module, and a source code automated-repair module. The method includes: uploading a to-be-detected code file to the source code I / O module, and performing, by the source code defect detection module, deep analysis and defect detection on the source code; performing, by the source code automated-repair module through an abstract syntax tree (AST) template-driven automated repair method, automated repair on a code defect detected upstream. This application makes up for the lack of a method for automated detection and repair of code defects in the prior art, simplifies the work of code standardization during software development, and improves development efficiency.

The invention relates to a unit test generation method based on a large language model, which comprises the following steps: collecting code fine tuning data to obtain an original data set; preprocessing the original data set to obtain an optimized data set; generating unit test data according to a query problem and a code solution in the optimized data set; training a large language model based on supervision fine tuning through the optimized data set and the unit generation data to obtain a unit test generator; and generating a unit test result of the to-be-tested program through the unit test generator. The efficiency, accuracy and flexibility of the unit test generation method can be improved, and support is provided for quality assurance and test optimization in the software development process.

The present invention discloses a software development system based on artificial intelligence technology, and relates to the field of software development technology. A software development system based on artificial intelligence technology includes: a user demand analysis module, a self-awareness module, an automatic code generation module and an intelligent feedback optimization module. The present invention simulates human intelligence through the self-awareness module, perceives changes in the development process in real time, and performs self-learning and adjustment according to historical data and real-time feedback; this adaptive capability enables the system to automatically optimize the development strategy according to different needs, technical changes and development progress, ensuring that the software development process can flexibly respond to various dynamic changes, avoiding frequent manual intervention in the traditional development process due to demand changes or technical bottlenecks.

The invention discloses a method for automatically generating a test case from a product demand document by a knowledge base, which is characterized by comprising a PRD document analysis and function point extraction module, a knowledge base construction and calling module, a test case generation module and a test case generalization module, the PRD document analysis and function point extraction module is used for converting a PRD document into a structured Markdown format by utilizing a large language model; the knowledge base construction and calling module constructs a structured knowledge base by analyzing historical PRD and test case data; the test case generation module generates test cases covering a normal scene, a boundary scene, an abnormal scene and a concurrent scene; and the test case generalization module is used for generating diversified and non-repeated test cases. Compared with the prior art, the method has the advantages that an efficient and comprehensive solution is provided for software testing automation, the method is particularly suitable for function testing, boundary testing, abnormal testing and concurrent testing in the software development process, and the application prospect is good.

A method in an illustrative embodiment comprises configuring a software development process to include at least first and second software-based agents for interacting with one another and with at least one large language model (LLM). The method further comprises assigning a first role in the software development process to the first software-based agent, assigning a second role in the software development process to the second software-based agent, initiating interactions between the first and second software-based agents and between each of the first and second software-based agents and the at least one LLM, determining estimates for completion of respective work items of the software development process based at least in part on the interactions, and adjusting one or more characteristics of the software development process based at least in part on the estimates. The software development process is illustratively part of a continuous integration / continuous deployment (CI / CD) system.

The invention discloses a duplicate checking method and system for products in all stages of software research and development, and belongs to the technical field of software engineering. In order to solve the problems of high efficiency and accuracy of code, document and function duplicate checking, the technical means of abstract syntax tree analysis, hash index construction, deep learning semantic analysis, image feature extraction, table structure comparison, semantic matching calculation and the like are mainly adopted. According to the method, code-level structured duplicate checking, document-level multi-modal comparison and functional-level semantic analysis can be realized, and the accuracy and efficiency of product duplicate checking in the software development process are improved.

The invention belongs to the field of aircraft fault diagnosis and maintenance guarantee, and particularly relates to a flight parameter diagnosis rule dynamic compiling method. According to the method, codes required by the criterion, the blank-leaving criterion formula and the associated parameters are designed in advance according to the criterion function, the criterion formula and the associated parameters are filled after the criterion formula is analyzed, the to-be-compiled criterion file is generated, and finally the to-be-compiled file is compiled into the executable program capable of being linked to the main program through the compiling tool chain. And thus, the criterion is dynamically compiled into an executable program. The time for converting the criterion into the executable program is greatly shortened, the software development process is guaranteed to a great extent, and the guarantee efficiency is greatly improved.

The invention discloses a method and system for directly generating a back-end code package from a form image, and the method comprises the steps: obtaining a form image based on a hand-drawn form design drawing; identifying the form image, performing structured processing, and analyzing to generate a data model definition file; and generating a back-end code package according to the data model definition file. The system comprises an image acquisition application and device, a data file analysis tool and a code generation plug-in. According to the method, the development efficiency in the software development process can be effectively improved, and flexibility and expansibility are achieved.

The invention discloses a software development big data analysis system, which belongs to the technical field of data analysis, and comprises the following steps: comprehensively collecting various data in a software development process, and carrying out standardization processing on the collected various data to obtain development processing data; performing feature extraction on the development processing data to screen out defect associated features related to software defects; based on the development processing data, constructing a software defect prediction model by using a multiple linear regression algorithm, and training and optimizing the model by adjusting parameters of the model; the method comprises the following steps: acquiring corresponding new data in a target software development process, performing processing and feature extraction on the new data, inputting acquired data values corresponding to defect associated features into a software defect prediction model to obtain a prediction result of the number of software defects, and displaying the prediction result in a visual manner. Developers are facilitated to optimize software design and development processes, and software quality and development efficiency are improved.

The present application provides a testing method and a testing system, which can perform a power-on test on a newly submitted first code through a first electronic device before merging the code into a main branch of the code after continuous integration verification in a typical software development process, or perform a power-on test on a newly submitted first code through a first electronic device during continuous integration verification in a typical software development process, or perform a power-on test on a newly submitted first code through a first electronic device before continuous integration verification in a typical software development process. The typical software development process includes continuous integration verification, merging the code into a main branch of the code, etc. Since the present application performs a power-on test on the code before merging the code into the main branch, the efficiency of locating "problem code" can be improved, so that the "problem code" can be located in a shorter time, thereby improving the overall R&D efficiency.