1868 results about "Software testing" patented technology

An example operation may include one or more of receiving, via a user interface, a request to test a software program, reading source code of the software program and identifying a vulnerability in the source code based on the reading, generating repair code for fixing the identified vulnerability based on execution of a generative artificial intelligence (GenAI) model on the source code and a repository of repair code used to repair previous vulnerabilities, and displaying information about the repair code via the user interface.

An example operation may include one or more of generating a large language model via a user interface, executing the large language model on a repository of software test cases and requirements of the software test cases to train the large language model to understand connections between test case components and test case requirements, receiving a description of features of a software program to be tested, and in response to receiving the description of the features, generating a software test case based on execution of the large language model on the received descriptions, and displaying the software test case via the user interface.

An example operation may include one or more of receiving a description of a plurality of testing elements for testing a software program and storing the software test within a storage device, generating an automation script for automating execution of the software test based on execution of a generative artificial intelligence model (GenAI) model on the plurality of testing elements and a repository of automation scripts, attaching the automation script to the software test within the storage device, and in response to a request to execute the software test, executing the plurality of testing elements within the software test based on the attached automation script.

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 an intelligent software testing method and system based on automatic script generation, and relates to the technical field of software testing, and the method comprises the following steps: deploying a distributed data acquisition probe, dynamically adjusting sampling frequency, collecting software operation data, and extracting data features based on a deep learning model; based on the data features, establishing a behavior pattern classification system, and mapping behavior patterns to realize hierarchical behavior classification; the method comprises the steps of constructing a test scene, generating test data, executing a test task, monitoring a test execution state in real time, calculating a test efficiency index, evaluating and feeding back a test effect, and dynamically adjusting a feature extraction strategy. According to the method, deep learning and reinforcement learning technologies are utilized, accurate recognition and classification of software behavior modes are achieved, and comprehensiveness and effectiveness of testing are guaranteed through multi-dimensional coverage rate analysis and a closed-loop optimization mechanism; and meanwhile, the automation level and the intelligent degree of software testing are remarkably improved, and a powerful guarantee is provided for improving the software quality.

The invention discloses a software test case generation method and system based on a large language model, and relates to the technical field of software testing, and the system comprises a to-be-tested module detection subsystem which is used for constructing a to-be-tested module set; the test case generation subsystem is used for constructing a prompt template according to the to-be-tested module and generating a test case by utilizing a program generation model based on an adaptive focus context generation mechanism and the prompt template; the correction verification subsystem is used for performing multi-stage correction processing on the test case, eliminating errors and generating an initial test suite; and the test case supplement subsystem is used for carrying out coverage rate analysis and variation test on the initial test suite, constructing a model input text according to a result, and generating a supplement test case by utilizing a program generation model so as to update the initial test suite and obtain a final test suite. By combining the functional annotation of the to-be-tested module with the logical reasoning ability of the LLM, misjudgment caused by assertion errors can be reduced, and the accuracy of test feedback is ensured.

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.

The invention discloses an automatic test method based on natural language processing, which comprises the following steps: S1, generating a test case: performing semantic analysis on a software requirement document or a test requirement by utilizing a natural language processing technology, extracting key information, and combining the extracted test information and a preset test case template to generate a test case; a preliminary test case is automatically generated; the key information comprises a test purpose, an operation step and an expected result; s2, analyzing the test case; s3, generating a test script; s4, executing the test script; and S5, analyzing a test result. According to the automatic test method based on natural language processing, the test requirements described by the natural language can be automatically analyzed, the test case is generated, the test is automatically executed, and the test report is finally generated, so that the software test efficiency is improved, the workload of manually compiling and executing the test case is reduced, and the test efficiency is improved. And the coverage rate and the accuracy of the test are improved, more software defects can be found, and the quality of software products is guaranteed.

The invention discloses a software testing automation platform and an intelligent optimization method thereof, and particularly relates to the technical field of software testing. The test case management module simplifies definition and automatic classification of test cases through a visual interface and a natural language processing technology; secondly, a test script generation module automatically generates and optimizes a test script by utilizing a preset template library and a self-defined script function in combination with an intelligent optimization tool; the test execution module executes test tasks in parallel in a multi-server or virtual machine environment through a task scheduling algorithm, and extracts environment load and response time characteristics in real time; then, the test result analysis module comprehensively analyzes an execution result by using the data prediction model, and identifies and marks an abnormal test case; and finally, the intelligent optimization module deletes the abnormal cases and dynamically adjusts the priorities of the normal cases to ensure that the high-priority test cases are executed preferentially, so that the automation level of test management is effectively improved, and the test execution efficiency and the result accuracy are optimized.

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 disclosed system for testing of fleet management software generates virtual assets (e.g., simulations of machinery) that are associated with simulated sensor values. For at least one particular asset in the set, the system generates a simulated application programming interface (API) message definition structured to capture the simulated sensor values. The system binds the set of virtual assets to a simulation scenario record that includes a trigger condition. The system causes a scheduler to detect that the trigger condition is met, fetch the simulation scenario record, and generate simulated API messages for the assets according to the API message definitions.

Provided in the embodiments of the present application are an automated software testing method, system and apparatus, and a storage medium. The method comprises: in response to acquiring a function requirement of software or a target function requirement selected by a user, processing the function requirement or the target function requirement on the basis of a test case generation model, so as to generate at least one test case expressed by a natural language (S310); on the basis of a preset condition, obtaining test information of the test case from the test case (S320); on the basis of the test information, constructing a test script corresponding to the test case, and executing the test script to obtain an execution result (S330); and processing the execution result on the basis of an execution result determination model, and determining an execution state of the test case corresponding to the execution result (S340).

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 an automatic driving simulation test scene generation method based on a text traffic accident report, and aims to apply a generative language model technology in the field of natural language processing, a code generation technology in the field of program synthesis and a fuzzy test theory in the field of software test. The problem that diversified and standardized high-quality simulation test scenes are lacked in the current automatic driving test field is solved, and the method can generate diversified scene files in the OpenSCENARIO format on the basis of natural languages, and mutates seed scenes through multiple transformation operators so as to efficiently discover defects existing in an automatic driving system.

Annotations associated with test cases written to test a software application can allow related test cases to be dynamically distributed among different sets of test cases that can be executed simultaneously in different parallel threads, thereby speeding up testing relative to executing the test cases sequentially in a single thread. The annotations can also allow test cases to be identified that are relevant to code changes, such that testing times can be further reduced by executing a subset of test cases relevant to code changes instead of a full set of test cases.

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 deep learning uncertainty measurement system based on a Bayesian segmentation network, and relates to the technical field of intelligent software testing, the method comprises the following steps: 1, a data input module: inputting a to-be-tested deep learning model and a test data set: (1) the to-be-tested deep learning model, the type of which is a deep learning model and a model designed for an image segmentation task; the method has the functions that the model is trained, image input is received, a corresponding segmentation result is output, the segmentation result is a matrix with the same size as the input image, and each element represents a category label of a pixel at a corresponding position in the input image; the format is in the form of a model object in a deep learning framework and comprises all weights and parameters. According to the deep learning uncertainty measurement system based on the Bayesian segmentation network, the uncertainty of an intelligent model is modeled in a convolutional encoder-decoder mode, and the uncertainty of prediction output of each category is obtained by calculating the variance of a softmax sample.

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 provides a software testing method and a software testing system. The software testing method comprises the following steps. And the processor obtains the test case. When the test case has an incremental code, the processor generates a supplementary test case according to the plurality of feature vectors in the vector database and the test case based on a coverage rate of the incremental code. The step of generating the supplementary test case further comprises the following steps. The processor extracts key information of a test case to obtain a plurality of first data vector units. The processor performs a similarity search according to the plurality of first data vector units and the plurality of feature vectors to obtain a plurality of second data vector units. The processor combines the plurality of second data vector units to generate a supplementary test case, thereby improving the performance of the test operation based on the incremental code.

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.