1461 results about "Software testing" patented technology

The invention discloses an unmanned aerial vehicle flight control system vulnerability detection method based on data flow analysis and LLM, and belongs to the technical field of intelligent software testing. Comprising the following steps: extracting a code function module associated with user operation in an unmanned aerial vehicle flight control system through a data flow analysis method, and establishing an operation-code mapping relation library; generating a structured natural language semantic description for each function module code by adopting a large language model LLM, and forming a multi-dimensional semantic feature vector; based on correlation analysis of multi-module semantic features, a combined test scene is constructed, and a natural language test case is generated; the natural language test case is converted into an executable test code through reverse semantic mapping, and coding reconstruction of test logic is completed; and executing a test code and capturing a runtime log in an unmanned aerial vehicle simulation environment, and performing vulnerability feature extraction and root cause positioning by using a large language model. According to the method, the efficiency is improved, and meanwhile, the deep coverage test of a complex interaction scene is supported.

The invention discloses a software test result analysis system which comprises a multi-source test data acquisition module, an intelligent anomaly detection engine, a cross-project association analysis module, a visual decision support terminal and a self-adaptive feedback optimization module. The multi-source test data acquisition module integrates heterogeneous test tool data and performs standardization processing; the intelligent anomaly detection engine realizes dynamic anomaly detection by using an LSTM model and a reinforcement learning algorithm; the cross-project association analysis module completes cross-project data association by means of code tracking, a knowledge graph and the like; the visual decision support terminal visually displays an analysis result in the form of a three-dimensional thermodynamic diagram and the like; and the adaptive feedback optimization module forms closed-loop control and dynamically adjusts a test strategy. The system effectively solves the problems that according to a traditional method, multi-source data cannot be fused, anomaly detection is not sensitive, and visual analysis is lacked, efficient analysis of software test results and optimization of the test process are achieved through cooperative work of all the modules, and the test efficiency and the software quality are remarkably improved.

The invention provides an automatic software testing method and system based on artificial intelligence. The method comprises the following steps: analyzing a software demand document by using an NLP technology, converting the software demand document into structured data, extracting a key function point, and identifying a software function point; generating a test case covering all function points through a deep learning model in combination with historical test data training; automatically executing a test operation, analyzing a test result, analyzing a test log by using a machine learning algorithm, identifying and classifying potential defects by using a data mining and pattern recognition technology, and generating a repair suggestion report; and analyzing feedback data in the test process, dynamically adjusting a strategy according to a test result by using a reinforcement learning algorithm, and optimizing a test case and the strategy. According to the invention, intelligent analysis of test data, automatic generation of test cases and automatic execution of a test process are realized; flexible configuration and expansion can be carried out according to the actual condition of software; the test efficiency and quality are improved, and the test cost and manpower consumption are reduced.

The invention discloses a test case generation method and device, equipment and a medium, and relates to the technical field of software testing, and the test case generation method comprises the following steps: storing request data subjected to structured processing into a seed case library; the request data are various types of request data in the operation process of the service system; determining a cue word according to the target information based on the large model, and generating an initial test case through the cue word and a generation rule; the target information comprises characteristics of request data in the seed case library, an interface document of the tested software and a business logic specification; executing the initial test case according to the test request of the tested software, performing statistical analysis on the code coverage rate of the tested software in the execution process, and screening the initial test case according to an analysis result to obtain a screened test case; and after the execution is finished, filtering the screened test cases according to the first execution result of each test case to obtain a target test case. Therefore, the testing efficiency and quality can be improved, and automatic testing is achieved.

The invention relates to the technical field of software test automation, in particular to a self-repairing UI automatic detection method based on multi-modal fusion, which mainly comprises the following steps: extracting visual features to obtain feature vectors, and constructing a virtual DOM tree with semantic tags to carry out similarity calculation to obtain structural similarity; and carrying out weight dynamic adjustment on the feature vector and the structural similarity through a feature fusion decision maker, outputting a fusion weight proportion under a current confidence threshold, fusing the feature vector and the structural similarity through the weight proportion to form a decision combination, and generating a new locator. Through the method, the problem of test case vulnerability caused by a dynamic user interface is solved, the detection speed of real-time sensing of interface change is improved, and meanwhile, the positioning success rate during use of a new positioner automatically generated by a positioning strategy is also remarkably improved.

An automation testing system includes a processor and a memory storing historical data which at least comprising past test results including input parameters and testing outcomes for each test case included in the past test results. The processor is configured to identify input parameters for a first iteration of a first software application having first functionalities; execute an initial test on the first iteration to generate initial results; based on the input parameters and the initial results, collecting first historical data at least comprising first past test results for at least one second software application having second functionalities corresponding to the first functionalities; training a model employing AI or ML based on the initial results and the first historical data to generate a trained model; executing the trained model with the input parameters as input to generate a set of test cases for testing the first functionalities.

The invention relates to an intelligent cabin software testing method and device, an intelligent cabin and a vehicle. The method comprises the following steps: acquiring a test requirement document of the intelligent cabin software; according to the test demand document, generating a decision tree and a dynamic demand graph corresponding to the test demand document; generating a function knowledge graph corresponding to the intelligent cabin software according to the decision tree and the dynamic demand graph corresponding to the test demand document; according to the function knowledge graph corresponding to the intelligent cabin software, generating a test case of the intelligent cabin software; and according to the test case of the intelligent cabin software, generating and executing a test script of the intelligent cabin software to obtain a test result of the intelligent cabin software. According to the method, the automatic process of compiling the test case and generating the test script can be realized, and the test efficiency of the intelligent cabin software is improved.

The invention provides a vehicle software testing method and device based on a large model, and relates to the technical field of software testing, and the method comprises the steps: obtaining the testing demand information of to-be-tested vehicle software; generating a model prompt according to the test demand information, wherein the model prompt is used for indicating to generate resources required by the vehicle software test; inputting the model prompt into the large model, and obtaining each driving scene and a vehicle software test case output by the large model; performing physical rule verification on each driving scene; determining the driving scene passing the verification as a target driving scene, and inputting the target driving scene into a simulation platform to obtain simulation data of the target driving scene; and inputting the simulation data and the test case into an automatic test platform to enable the automatic test platform to analyze the simulation data, and executing the test case according to the analyzed simulation data to obtain a test result. By applying the method provided by the invention, the test efficiency and the coverage rate can be improved.

The invention relates to a test case generation method and system based on multi-agent efficient collaboration, belongs to the technical field of software testing, and solves the problems of incomplete scene coverage, logic disorder and the like when a single large model processes a complex task. The method comprises the following steps: constructing a multi-modal professional field knowledge base; the task planning agent module generates a test case generation task based on a project development document and a software source code file of a to-be-tested project and distributes the test case generation task to the test demand analysis agent module; based on the test case generation task, extracting a test demand point related to the tested software configuration item, describing the test demand point, and labeling a corresponding tracking relationship between a test demand point ID and a code snippet or a function in the software source code file to construct a test demand-code snippet / function set; and generating a test case and a test description document based on the test demand-code snippet / function set and the multi-modal professional domain knowledge base. And the ability of generating the test case of the large model is enhanced through organic and efficient cooperation of multiple agents.

The present disclosure provides techniques and solutions for automatically correcting software tests. When a test failure is detected, it is determined whether a screenshot or code associated with a second version of a software artifact includes a user interface element that has a semantically equivalent identifier to a user interface element in a screenshot or code associated with a first version of the software artifact. Identifying a semantically equivalent identifier can include determining a section of the user interface, in the screenshot of the code, of the first version of the software artifact where the user interface is located and searching the corresponding section of the user interface of the second version of the software artifact. A definition of the software test can be updated to reference the semantically equivalent user interface element.

The invention discloses an automatic driving system efficient fuzzy test method based on system slicing and backtracking, and the method is based on a data-driven scene library, takes a real-time driving behavior value-at-risk as a feedback signal, achieves the rapid generation and evolution of a high-risk scene through a system state slicing and backtracking mechanism, and achieves the real-time driving behavior value-at-risk. Comprising the steps of introducing a genetic algorithm regulation and control mechanism of multi-source risk information fusion, branch path aggregation evaluation and risk guidance, and a population evolution strategy based on dynamic feedback, so that a self-driven closed-loop mechanism of a test system from generation-test-evaluation to perception-feedback-re-optimization is realized. The method can improve the recognition capability of the automatic driving system for the high-risk long-tail scene and the system-level fault exposure efficiency, and is suitable for the scenes of software testing, simulation verification, risk assessment, safety strategy optimization and the like of the automatic driving system.

The invention relates to the technical field of software testing, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a fault simulation method, device and equipment based on a code probe and a medium. Constructing a corresponding fault logic code according to the payment data link; injecting the fault logic code into the to-be-tested fault object to obtain a target fault object; performing payment fault simulation on the target fault object according to a preset payment fault simulation condition and the fault logic code to obtain fault response data corresponding to the target fault object; and performing fault analysis on the target fault object according to the fault response data to obtain a target fault analysis report of the target fault object. According to the invention, full-link intelligent fault simulation can be realized and the verification reliability of each transaction of the target system can be improved.

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.

The invention discloses a software test automation report generation optimization method which comprises the following steps: automatically registering and authenticating a multi-source test data access endpoint, uniformly collecting and normalizing test data with a multi-dimensional attribute tag, and forming a high-quality grouped data set through structured modeling and automatic clustering; further extracting event and result feature nodes, and establishing an interpretable tree rule dependency relationship; according to the method, the minimum redundancy and reusable customized rule path is generated by applying a dependency tree traversal and optimization algorithm and automatic reasoning, and the continuous adaptive evolution of the rule base among multiple projects is realized by combining conflict detection and visual interaction, so that the efficiency and expandability of data processing in a complex test scene are improved, and the test efficiency is improved. And the flexibility and automation level of report customization rule configuration are enhanced.

The invention relates to the technical field of software testing, and discloses an automatic test script generation method and system, a storage medium and electronic device.The method comprises the steps that test case information and tested environment information are received, and structured case representation is generated; enhancing the structured use case representation based on a preset knowledge base; generating a plurality of positioning candidates for the target control in the testing step, and establishing a priority relationship among the positioning candidates; generating an executable automatic test script; running the script, and when execution failure is detected, automatically adjusting a positioning strategy, a waiting strategy or a business process based on a failure type, and executing again; and feeding back the successfully executed script information to the knowledge base, and updating the content and priority of the knowledge base. Through organic combination of knowledge base enhancement, multi-candidate positioning and a self-healing mechanism, the technical problems that an existing automatic test script is high in maintenance cost, poor in stability and lack of domain knowledge are solved.

The invention discloses a graphical user interface test case generation method based on a multi-agent framework, and relates to the field of graphical user interface tests.The graphical user interface test case generation method based on the multi-agent framework comprises the following steps that S1, a user demand document is obtained, and file text content and file format information are analyzed and extracted; s2, performing paragraph segmentation on the file text content to form logic semantic units; s3, carrying out correlation analysis on the segmented paragraphs, and obtaining a ternary file group; s4, performing paragraph annotation addition on the ternary file group, and constructing a semantic annotation result set; s5, extracting the agent framework to generate a graphical user interface test case, performing test recording, and storing a test result; s6, the result is verified, and a test case list and a software test report are generated. By conducting multi-dimensional analysis on the text content and the format structure in the document, the reduction efficiency from natural language description to structural semantic construction is improved.

The invention discloses a test case execution method, electronic equipment, a storage medium and a program product, and relates to the technical field of software testing, and the method comprises the steps: obtaining test data features in a test case execution process, carrying out the grading of a test case based on the obtained test data features to determine a grade coefficient, according to the method and the device, the priority of the test case is dynamically adjusted according to the test data characteristics of the test case, the test case is executed according to the priority score of each test case, the execution sequence of the test case is dynamically adjusted, the dynamic adaptability of the test case is improved, and the test efficiency is improved. Therefore, the test efficiency can be ensured, and the determination of the priority score also fuses the test module and the test function point, so that the test cases of the important module and the core function point can have higher priority scores, the execution is preferentially performed, and the coverage integrity of the core function and the error-prone module can be ensured.

The invention belongs to the technical field of cloud computing and software testing, and particularly discloses an automatic fault injection and recovery testing method based on cloud native, which comprises the following steps: acquiring test basic data of a to-be-tested cloud native system; constructing a fault test execution model according to the test basic data; according to the fault test execution model, executing an automatic fault injection operation; generating an abnormal alarm log according to the system running state after the fault injection operation, and synchronously recording a fault injection operation log; according to the abnormal alarm log, executing automatic fault recovery operation; and generating a fault test report according to the fault injection operation log, the system monitoring data and the recovery operation process log. The invention aims to solve the problems of low automation degree, narrow scene coverage, weak monitoring evaluation and passive recovery in cloud native system fault test in the prior art, and provides a comprehensive, automatic, monitorable and evaluable fault injection and recovery test scheme.

The invention discloses an agent-based test case generation method and device, equipment and a medium, and relates to the technical field of software testing, the method comprises the following steps: in response to a test case generation instruction for a target function, displaying a test case framework of the target function; in response to an interaction instruction for a preset position in the test case framework, obtaining a target test code of the target function; wherein the interaction instruction is used for triggering interaction between a first agent and a second agent, and the first agent is configured to be used for analyzing a logic structure of a function code of a target function to obtain a structure code of a test point of the target function; the second intelligent agent is configured to be used for generating a target test code based on the structure code of the test point; and displaying the target test code at a preset position to obtain a target test case of the target function. The problem that the generation accuracy of the test case is low can be solved.

The invention discloses a software remote testing system and method based on a distributed architecture, particularly relates to the technical field of software testing, and is used for solving the problem of low task migration execution efficiency in a software remote testing process. According to the method, an anomaly detection mechanism is constructed based on a node state track, potential faults of remote test nodes are accurately identified, modeling is performed in combination with topology cutting and resource capability, an optimal task scheduling path is dynamically generated, and a scheduling structure is updated; structured snapshots are extracted from running tasks in the failure nodes, cross-platform packaging is carried out, state reconstruction is completed at target nodes, and uninterrupted task migration is achieved; by comparing the original state with the recovery state, an asymmetric residual scoring function is constructed, the consistency degree is quantified, and whether the task continues to be executed or rolls back is dynamically judged, so that the scheduling stability and the execution continuity of the distributed test under the fluctuation of the test node are improved.

The invention discloses a test case generation method and device, equipment and a medium, and relates to the technical field of software test optimization. According to the scheme, the multiple initial test cases are obtained, and the test reports corresponding to the initial test cases are generated through the first preset test tool, so that the time for manually compiling the test cases is effectively shortened; when it is determined that a to-be-supplemented test case of which the corresponding code coverage rate index does not meet a preset requirement exists according to each test report, code uncovered information of the to-be-supplemented test case is determined, and test case optimization parameters are configured in combination with project requirements; and based on the parameter, calling a second preset test tool and a large language model to supplement the to-be-supplemented test case to obtain a complete test case. According to the scheme, the availability of the test case is improved through combination of multiple tools; workflows of test cases covering complex environments such as a function calling relation and a control flow structure are generated by utilizing the inference capability of a large language model, and the code coverage rate of the test cases is remarkably increased.

The invention discloses a large model multi-agent-based quantum software defect detection method and a related device, and relates to the technical field of computer software tests.The method comprises the steps that efficient quantum software defect detection is achieved through multi-agent cooperation based on large language model driving, and first, initial defect judgment, abstract syntax tree and framework classification agents are conducted; extracting auxiliary information from the to-be-detected quantum software code to generate multi-dimensional auxiliary data; a static analysis integration agent is driven by the large language model, a corresponding quantum software static analysis tool is dynamically called according to the frame type, and structured alarm information related to the defect is extracted; and finally, based on the multi-dimensional auxiliary data and the structured alarm information, chain reasoning is carried out through the quantum field, defect prediction, defect verification and a defect evaluation agent, and a structured defect report is generated. The method effectively solves the problems that a traditional static tool is single in adaptation, large language model quantum knowledge is insufficient, and multi-source information is isolated, and guarantees defect judgment accuracy and report interpretability.

The invention discloses an aviation airborne software test case intelligent generation and closed-loop optimization method, which comprises the following steps of: knowledge graph construction: analyzing a DO-178C standard document and a related field document, extracting entities and relationships defined in the DO-178C standard document and the related field document, and constructing a field knowledge graph fused with DO-178C standard knowledge; initial test case generation: based on the domain knowledge graph, combining a static analysis result of the source code of the tested airborne software, and utilizing a large language model to drive and generate an initial test case set; and closed-loop iterative optimization: executing the test case, evaluating whether the structural coverage rate reaches the standard or not, automatically identifying uncovered codes when the structural coverage rate does not reach the standard, generating a supplementary test case for iterative optimization, and outputting a final test case set until a coverage rate target corresponding to the software security level is met. According to the method, the test quality and efficiency of the aviation airborne software can be improved.

A digital assistant can provide support for automated testing of applications. A natural language interface can be provided by which a testing user can specify a request for one or more testing actions. A natural language processing model can recognize intents in the request, and the intents can be used to execute executable code to perform the requested testing actions. Multiple actions per request can be supported. An object repository can be leveraged to determine user interface control identifiers, and a test data container can store values for use during testing. Testing functionality can thus be provided to a wider base of testing users. A real time, scriptless approach can conserve computing resources.

The invention discloses a research and development test bidirectional synchronization method based on artificial intelligence, and particularly relates to the technical field of artificial intelligence and software testing. Deep semantic modeling of research and development task behaviors is realized by constructing a multi-modal causal intention map, a test coverage blind area is inferred and recognized based on a graph neural network, a completion task is reversely generated, a complete state set is formed in combination with an original task set, and a condition control vector is adopted to drive generation of a structured test case. Collecting behavior and operation logs after the test is executed, detecting a mismatch area through modal alignment, generating a repair suggestion by utilizing a deviation correction model, and automatically writing back a task system; meanwhile, a state graph dynamic updating mechanism and a reverse task scheduling engine are introduced, and linkage feedback of test results on research and development tasks is achieved; the method can improve test coverage precision and research and development cooperation efficiency, and is suitable for agile development and continuous integration scenes.

The invention relates to the technical field of software testing, and discloses a software defect detection method, a software defect repair method and a software defect repair system, and the detection method performs defect detection on software based on a large language model. The problem that software defects are difficult to efficiently and accurately detect in the prior art is solved.

The invention relates to a test case generation method based on a multi-modal knowledge base and self-adaptive cue words, belongs to the technical field of software testing, and solves the problems of insufficient reuse of historical project data and expert knowledge, lack of knowledge in a large model professional field, prominent illusion problem, low adoption rate of generated test cases, low practicability and the like. The method comprises the steps of generating an original test demand set of a to-be-tested item; forming a test demand-code snippet / function set based on the software source code file and the original test demand set; generating a first-stage test tree graph based on each test demand point and the corresponding code snippet / function; forming a software test outline based on the test items, the test subitems and the corresponding test demand points and code snippets / functions; forming a second-stage test tree graph based on the first-stage test tree graph and the software test outline; and generating a test case based on the second-stage test tree graph and the software test outline. And the specialty, the accuracy and the interpretability of the test case are enhanced.

The invention relates to the technical field of software testing, and discloses a unit test case generation large model fine tuning method based on feedback reinforcement learning, which comprises the following steps: constructing a unit test fine tuning data set based on demand driving; an Actor model is constructed, and optimization is carried out through a demand-driven unit test fine tuning data set; a Critic model is constructed, and optimization is carried out through a demand-driven unit test fine tuning data set; and generating a unit test case based on the optimized Actor model, mapping the unit test case into a single instant reward based on the optimized Critic model, and obtaining a large model generated based on the unit test case through reinforcement learning. The unit test case generation method capable of automatically generating high-quality unit test cases with aligned requirements and consistent styles is constructed, the test efficiency and quality are remarkably improved, and the bottlenecks of traditional test generation in manual dependence, coverage depth and requirement consistency are broken through.

The invention relates to the technical field of software testing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an automatic verification and report generation method, which comprises the following steps of: obtaining identification value data of a target object, performing structured processing, executing accuracy verification containing multi-dimensional verification items to generate verification result data, and generating a verification result; and constructing a test case set based on the verification result, carrying out an automatic test to generate test result data, and generating an informatization verification result report containing states, reasons and suggestions based on the test result data. According to the method, the standardization degree of data organization is improved through structured processing, potential deviation is found in advance through accuracy verification, the verification coverage range is expanded through test case construction, the verification efficiency is improved through automatic testing, and a feedback closed loop is achieved through result report output; therefore, the accuracy, the efficiency and the intelligent level of the identification value verification process are remarkably improved.

The invention discloses an automatic test case generation method and system based on a large model, and the method comprises the steps: multi-modal data fusion: receiving a natural language demand, a UML design drawing, an API document, a historical defect log and system monitoring data, aligning text and image features through a CLIP model, analyzing the interface constraint through CodeBERT, and carrying out the multi-modal data fusion; feature fusion is realized by adopting a cross-modal attention mechanism; a dynamic test scene modeling step: constructing an environment simulator based on reinforcement learning, and simulating high-concurrency, distributed abnormal and other complex scenes; and a layered test case generation step: generating a function test case, a boundary test case and an increment test case through GPT-4, GAN and LSTM respectively, and introducing an entropy evaluation model to quantify the effectiveness of the cases. According to the method, the multi-modal data is brought into the test case generation process for the first time, the test case generation time is shortened by 80%, the complex scene coverage rate is increased to 95%, the semantic matching degree reaches 92%, and the intelligent level and efficiency of software testing are remarkably improved.