562 results about "Software testing" patented technology

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.

The invention discloses a software test expected result prediction method and system based on deep learning, and the method comprises the steps: obtaining multi-source heterogeneous data of a tested system, and carrying out the preprocessing and correlation fusion based on the multi-source heterogeneous data, so as to determine a multi-modal data set; performing feature extraction on the multi-modal data set through a text encoder, a structured data encoder and a time sequence feature encoder to obtain a plurality of feature vectors, and performing fusion processing on the feature vectors to generate a feature vector set; performing deep learning reasoning on the feature vector set, and generating a multi-dimensional prediction result through multi-head self-attention mechanism and cross-modal attention fusion; and carrying out dynamic verification and conflict resolution processing on the multi-dimensional prediction result to generate a test expectation result conforming to system constraints and business logic. According to the method, the multi-modal features are automatically extracted and fused through the deep learning technology, the accurate test expected result is generated, manual intervention is reduced, and the software test efficiency and accuracy are improved.

The invention provides an AI-based automatic test case generation method and system, and relates to the technical field of software testing, and the method comprises the steps: converting an original demand document uploaded by a user into an image format document, and calling a multi-modal large model to recognize text features and non-text features in the image format document, integrating to form an analyzed demand document; dynamically cutting the analyzed demand document to obtain a plurality of logically coherent content blocks; calling a large language model to generate a corresponding test case title for each content block, and forming a test case title set; and inputting each test case title in the test case title set and the corresponding content block into the large language model to generate a complete test case corresponding to the original demand document. The method has the beneficial effects that through multi-modal content analysis and intelligent workflow arrangement, automatic analysis and understanding can be performed on image-text features of a demand document, a high-quality test case is automatically generated, and the test period is greatly shortened.

The invention belongs to the field of software testing and defect detection, focuses on reliability verification of a physical simulation engine, is suitable for scenes depending on physical simulation, such as robot simulation, automatic driving testing, unmanned aerial vehicle control, education and scientific research, and particularly relates to a variation-based physical simulation engine defect detection method. Potential defects of core modules of collision detection, mechanical calculation, dynamics and the like are efficiently found through a variation test method. According to the method, a feature library is constructed through static library API analysis and AST / IR code analysis, test code expansion is performed by utilizing a large model, a test driver adaptive to a specific data type is generated, a physical semantic constraint mutation seed is combined, a test is executed through libFuzzer, defect and coverage rate data are analyzed, a use case selection strategy is optimized, and the test efficiency is improved. Defects of core modules such as collision detection and dynamic solution are efficiently detected, and the test pertinence and efficiency are improved.

The invention belongs to the technical field of vehicle software testing, and particularly relates to a test case generation method and system based on big language model and rule collaboration, and the method comprises the steps: analyzing a demand document, and mapping the demand document to a fault ontology model to generate a structured test demand; generating a plurality of groups of fault verification conditions based on equivalence class division and a boundary value analysis method by utilizing a large language model; a multi-dimensional mixed retrieval strategy is adopted to retrieve a reference case from a historical case library; in combination with the verification condition, the reference case and the standardized information base, preposed environment settings, serialized operation instructions and expected responses of the test case are generated in stages; and finally, the case quality is ensured through cooperative verification of a large language model and a rule engine. The problems that a traditional method depends on manpower, the coverage rate is low, errors are prone to occurring, and historical knowledge is difficult to reuse are effectively solved, and the automation degree, accuracy and integrity of BMS test case generation are remarkably improved.

The invention provides a test case generation method and device, equipment, a storage medium and a program product, and relates to the technical field of software testing. The method comprises the following steps: constructing a dependency graph comprising nodes represented by API functions and edges representing a dependency relationship between two connected API functions according to an API specification document; determining a target scene rule matched with the received test task, and performing random walk on the dependency graph according to a scene preference weight provided by the target scene rule and used for representing the degree of correlation between the API function and the target scene rule to obtain an API call sequence; and generating a test case for testing the GPU driver according to the API call sequence. According to the method, the target scene rule matched with the test task is determined, and the scene preference weight which is provided by the target scene rule and represents the correlation degree of the API function and the target scene is utilized to guide random walk on the dependency graph constructed based on the API specification document, so that the generated API calling sequence better meets the requirements of the test scene.

There is provided a system and method of automatic software testing. The method includes obtaining an input including software code of a software program and metadata, and feeding the input to a machine learning model to generate a test suite usable for testing the program. The test suite comprises a set of tests meeting a predefined condition. The test suite is generated by generating at least one question related to at least one of: expected intents of one or more sections of the software code, or tests for testing the sections, and presenting the at least one question to a user; upon receiving feedback from the user, analyzing the feedback with respect to the predefined condition, and determining whether to generate at least one new question; and, in response to an affirmative determination, repeating the above process with respect to the new question, until the predefined condition is met.

The invention relates to the technical field of software testing, and discloses an agent-based multi-dimensional test sample automatic generation method and system.The agent-based multi-dimensional test sample automatic generation method comprises the steps that code changes are captured, interface definitions are extracted, and multi-source data are fused for consistency verification; constructing a multi-dimensional dependency graph and analyzing the change influence to obtain an associated interface list; generating a multi-dimensional test scene; performing parameter traceability and consistency derivation, and initializing test data and environment to obtain a complete test data set; generating a use case code, and performing hierarchical optimization and executable verification; test arrangement optimization and automatic execution are carried out, and faults are monitored and diagnosed in real time; carrying out multi-dimensional analysis and defect identification, dynamically evaluating the value of a test case and optimizing a test strategy to obtain a comprehensive test report; according to the invention, full-process automation of test case generation is realized, the test efficiency and quality are improved, and the test maintenance cost is reduced.

The invention relates to the technical field of software testing, and discloses a large-model-based REST-Like API dependency analysis and test case generation method, which comprises the following steps of: obtaining API interface description information, performing semantic analysis on the API interface description information by utilizing a large language model, and generating structured semantic representation so as to identify an operation type, dependency resources and operation resources of each API; an API-resource dependency graph is constructed based on the representation, the dependency graph comprises resource nodes and API nodes, and the operation and dependency relationship between the API and resources is represented through two kinds of directed edges of a dependency edge and an operation edge; on the basis of the graph, a test case is automatically generated through resource initialization template pre-generation and recursive dependency analysis, and the core of the test case is an API calling sequence used for testing the target API. According to the method, through semantic understanding of a large model, the problem that a traditional test tool loses efficacy in dependence analysis on a non-standard REST-Like API is solved.

The invention relates to a test system based on a multi-modal knowledge base, belongs to the technical field of software testing, and solves the problems of insufficient reuse of historical project data and expert knowledge, lack of professional domain knowledge, illusion, low test case adoption rate and low practicability. The system comprises a knowledge base construction module used for constructing a hierarchical multi-mode professional field knowledge base; the preprocessing module is used for forming a standardized project development document and a software source code file of a to-be-tested project; the test case generation module is used for generating a test case and a software test description thereof based on the multi-modal professional domain knowledge base, the project development document of the project to be tested and the software source code file; and the test execution module prepares execution conditions according to the test case and the software test description thereof, and then executes the software test steps in sequence according to the input and operation description steps described by the test process to obtain an execution result of the test case. And knowledge-based and deep structured multiplexing of test assets is realized, and the test quality and efficiency are improved.

The invention relates to a vehicle end software test case generation method and device, equipment and a storage medium. The method comprises the steps of obtaining test demand description information matched with a test request in response to the test request for the vehicle-side software; obtaining vehicle end test case data matched with the test demand description information from a pre-constructed vehicle end test case knowledge base, and obtaining vehicle signal constraints matched with the test demand description information from a pre-constructed vehicle signal knowledge graph; and on the basis of the vehicle-side test case data, the vehicle signal constraint and the test demand description information, constructing a cue word, inputting the cue word into a pre-constructed large language model, and generating a target test case for the vehicle-side software through the large language model. By adopting the method, the accuracy of the generated vehicle end software test case can be improved.

The invention discloses a software performance test system, method and device in a high-concurrency scene, and relates to the field of software testing, the system comprises a scene intelligent construction module used for generating a concurrency scene through a behavior tree modeling and time sequence prediction algorithm according to service demand parameters and historical traffic data; the distributed test environment scheduling module is used for constructing a distributed test environment by adopting a container arrangement technology; the virtual user load generation module is used for simulating concurrent user requests by adopting a coroutine pool technology; the real-time performance data acquisition module is used for acquiring multi-source performance data of the tested software in real time; the intelligent performance analysis module is used for calculating a core performance index according to the multi-source performance data and carrying out anomaly detection and trend prediction; and the performance bottleneck positioning module is used for mining a dependency relationship among the core performance indexes and generating a bottleneck positioning report and an optimization suggestion document. According to the method, the accuracy, the efficiency and the stability of software performance testing in a high-concurrency scene are improved.

The invention belongs to the field of software testing and intelligent automation, and provides a multi-mode case integrated testing method, device, equipment and medium, and the method comprises the steps: obtaining user operation video data, and extracting the user operation video data to obtain a key frame; performing multi-modal visual analysis on the key frame to obtain a multi-modal visual analysis result, and determining a first draft test case according to the multi-modal visual analysis result; the first draft test case is tested through an MCP, a case test result is obtained, and the test comprises structured testing, page understanding, recovery and callback processing through a main agent and a plurality of sub-agents; and performing equipment control and operation processing on the cross-platform equipment by adopting the MCP according to the case test result to obtain a cross-platform test report. The cross-platform multi-mode case testing method has the advantages that the testing efficiency and accuracy of the cross-platform multi-mode case are improved, and testing interference is reduced.

The invention discloses a log analysis method and device, equipment, a medium and a program product, and relates to the technical field of software testing, and the method comprises the steps: obtaining failure log data of a failure test case, and success log data of a success test case corresponding to the failure test case; extracting target content related to the test failure in the failure log data through a preset large language model; performing semantic comparison on log data corresponding to the test steps in the failure log data and the success log data through a preset large language model to obtain difference information; analyzing the failure log data based on a target cue word through a preset large language model to obtain a failure reason of the failed test case, the target cue word being obtained by combining a first cue word and a second cue word, and generating the first cue word by embedding a target content into a first cue word template; and embedding the difference information into a second cue word template to generate a second cue word. The log analysis efficiency is improved.

The invention discloses an AI test case intelligent optimization verification method and system, and the method specifically comprises the steps: employing a causal discovery algorithm to analyze an implicit dependency relationship for an entity association structure in a test case knowledge graph, recognizing redundant test cases and invalid test paths, and obtaining a causal dependency network; on the basis of a causal dependence network, traversing uncovered entity relationship paths in the test case knowledge graph, and generating supplementary test case suggestions; dynamically adjusting analysis strategy parameters according to historical optimization data through a self-adaptive engine to obtain an optimization test case set; and based on the optimized test case set, generating an input parameter variation sample by using a fuzzy test engine, and expanding a test boundary. According to the method, the black box test efficiency can be remarkably improved, the test coverage rate and accuracy are improved, meanwhile, the workload of a test engineer is relieved, the user experience is improved, and an efficient and intelligent solution is provided for the field of software test.

The invention discloses a software and hardware co-simulation field recovery method and device, equipment and a medium. Comprising the steps of defining a recording starting point and a storage time point of a simulation site; from the recording starting point to the storage time point, capturing transaction flow and causal metadata from the hardware simulation environment of the simulation site to the software test platform, and meanwhile, storing a state snapshot of the verified design in the hardware simulation environment; and when a recovery instruction is received, performing simulation field recovery based on the transaction flow, the causal metadata and the state snapshot. By defining the time range of data capture, a clear boundary is delimited for subsequent data storage, and pertinence and integrity of data records are ensured. A basis is provided for semantic reconstruction of a software test platform by capturing a transaction flow and causal metadata, a state snapshot ensures that the verified design hardware state can be accurately restored, and complete storage of recovery data on the two sides of software and hardware is achieved. And the system does not need to be restarted from the starting point during recovery, so that a redundancy initialization process is avoided, and the simulation debugging efficiency is improved.

The invention discloses a fuzzy test verification method and system for a password service interface, and belongs to the technical field of password service technologies and software testing. The method comprises the following steps: firstly, constructing a password service interface feature library, and extracting key feature information; designing a multi-dimensional fuzzy test variation strategy based on the password service interface feature library, and generating a fuzzy test case set covering grammar exception, semantic exception and encryption scene exception; realizing communication adaptation between a test case and a target password service interface through a dynamic adaptation module, executing a fuzzy test, and collecting information such as response data and an operation state of the interface in real time; and finally, carrying out deep analysis on the acquired information through an anomaly detection and analysis module, positioning a vulnerability type and a trigger path, and generating a standardized test report. According to the method, different types of password service interfaces can be accurately adapted, the test coverage and the vulnerability discovery efficiency are remarkably improved, and reliable support is provided for security verification of the password service interfaces.

The invention discloses a method and device for comprehensively optimizing test resources, electronic equipment and a storage medium, and belongs to the technical field of software test.The method comprises the steps that a test case set is classified through a high-dimensional data clustering algorithm, and a plurality of test case classes are obtained; selecting representative test cases from each test case class based on an improved greedy algorithm to form a target test case subset; performing priority ranking on the target test case subset by adopting a firefly group optimization algorithm to obtain a ranking result; and optimizing distribution and scheduling of test resources according to the sorting result so as to preferentially execute high-priority test cases and realize resource load balancing. By combining high-dimensional data clustering, an improved greedy algorithm and a firefly group optimization algorithm, the problem of large-scale test case management in the interconnection and interworking test of heterogeneous AC and AP equipment is solved, comprehensive optimization management of test resources is realized, and the test efficiency and the system quality are remarkably improved.

The invention discloses a simulation system for industrial control software testing, which relates to the technical field of industrial control software testing and comprises the steps of uniformly configuring testing tasks, scheduling testing processes, managing testing cases, constructing a programmable virtual industrial control environment, packaging an industrial communication protocol and providing a standardized interface. The problems of protocol difference and interface compatibility between different devices are solved; and defining and combining complex test scenes of multi-device collaborative logic, abnormal working conditions and network disturbance. Complete separation of real hardware equipment is realized, the construction cost and the maintenance complexity of a test environment are remarkably reduced, the coverage range and the authenticity of the test are improved, the stability and the fault-tolerant capability of control software under extreme conditions are verified, the whole scheme realizes configurable, quantifiable and repeatable test processes, and the test efficiency is improved. And powerful support is provided for high-efficiency and high-credibility verification of industrial control software.

The invention relates to an exception handling method and device for software testing, a storage medium and electronic equipment. The method comprises the following steps: in response to a monitored abnormal event in a test process, obtaining abnormal information of the abnormal event; determining an exception type and associated service information of the exception event according to the exception information; generating at least one candidate processing operation based on a knowledge graph library according to the exception type and the associated service information; generating a target processing operation according to the at least one candidate processing operation; executing the target processing operation; and after executing the target processing operation, generating and outputting a diagnosis report corresponding to the abnormal event.

The invention discloses an optimization sensitive test system and method for an AI compiler, and belongs to the technical field of software testing. The method mainly comprises the following steps: firstly, automatically extracting an IR sub-graph mode capable of triggering specific optimization Pass from an existing test of a compiler through dynamic instrumentation; secondly, through a strategy of multiplexing compatible nodes or creating new nodes, the optimization modes are intelligently synthesized into a diversified seed calculation graph context, and a large number of test cases capable of effectively triggering compiler optimization are generated; and finally, efficiently revealing the defects of the AI compiler in the optimization stage by taking crash detection and reasoning consistency comparison as test predictions. The defect that an existing random generation or grammar generation method is insufficient in test coverage rate in the optimization stage is overcome, the deep optimization defect in the AI compiler can be automatically and efficiently detected, and the method has the high error detection rate and good universality.

The invention provides an automatic external connection regression testing method and device, equipment and a storage medium, and belongs to the field of computer software tests.The method comprises the steps that historical external data are extracted from an external connection database in batches based on an external connection regression testing request input by a client interaction page; converting the historical external data into basic test data according to a preset rule based on test demand information input by the client interaction page; updating the basic test data into test sample data matched with the current interface of the external connection component; calling a logic code of the external connection component based on the JSP, analyzing the test sample data and forwarding the test sample data to the service component; and comparing a business result generated by the service component with an expected result, and generating a test report. Through the method provided by the invention, the limitation of a traditional scheme can be overcome, and the efficiency and quality of external connection regression testing are improved in an automatic testing mode.

This invention discloses an automatic browser memory leak location system, method, and medium based on the MCP protocol, belonging to the field of software testing technology. The system includes an MCP communication module, an automated testing module, a heap snapshot acquisition module, an incremental analysis module, a reference chain analysis module, a SourceMap location module, an intelligent repair module, and an IDE visualization module. This invention establishes a standardized connection between the IDE and the browser debugging port through the MCP protocol, achieving fully automated control; it employs a multi-dimensional fingerprint algorithm based on constructed stack frames and attribute structures to solve the problem of cross-snapshot object matching; it utilizes the dominator tree algorithm to compress the reference chain and combines it with the SourceMap index to achieve source code-level location and AI repair suggestion generation. This invention significantly improves the efficiency and accuracy of memory leak location, solving the problems of fragmented debugging links and the inability to accurately map source code.

The invention provides a software testing method and device, electronic equipment and a storage medium. The method comprises the steps that a source code file of to-be-tested software is compiled based on a WebAssembly compiler to obtain a compiling result and first debugging information, and the first debugging information represents the mapping relation between code lines in the source code file and instructions in the compiling result; the compiling result is converted into a to-be-tested binary file based on a format conversion relation, second debugging information is determined according to the first debugging information, the compiling result and the to-be-tested binary file, the format conversion relation is used for converting the WebAssembly binary file into a binary file in a preset format, and the format conversion relation is used for converting the WebAssembly binary file into the to-be-tested binary file. The second debugging information represents a mapping relation between a code line in the source code file and an instruction in the to-be-tested binary file; and running the to-be-tested binary file in a virtual machine used for running the binary file in the preset format, and debugging the to-be-tested binary file based on the second debugging information in the virtual machine.

The invention relates to the technical field of software testing, and discloses an AB experiment method, device, system and equipment, a storage medium and a product. According to the scheme, the AB experiment rule can be obtained based on the current call application program, the AB experiment rule comprises the shunting rule and the experiment configuration, the communication connection with the interaction object is established, shunting is carried out based on the shunting rule, the shunting result is obtained, and the experiment data is reported according to the experiment configuration and the shunting result after the communication is finished, so that the AB experiment is completed. According to the scheme provided by the embodiment of the invention, the AB experiment rule can be issued to the client in advance, the AB experiment rule is executed on site when the service meets the condition, the shunting result is quickly obtained, the AB experiment shunting efficiency and the subsequent experiment accuracy are remarkably improved, in addition, the shunting rule needing to be used does not need to be supported by an experiment platform and only needs to be supported by the client, and the workload of the client is reduced. And the method also has better expansibility.

This invention discloses a method and apparatus for program debugging, relating to the field of software testing technology. One specific embodiment of the method includes: in response to receiving a local debugging request from a program, obtaining online recording data corresponding to the program from a pre-recorded data file; and running the program by replaying the online recording data to perform local debugging. This embodiment implements a method for local program debugging, ensuring consistency between the local debugging environment and the online environment through online data recording and replay processing, thereby enabling efficient and low-cost local debugging of program code.

The invention relates to the technical field of computers, and discloses a test case generation method and device, equipment and a medium, and the method comprises the steps: receiving a software test demand through a demand analysis agent, extracting a test point of the software test demand, and constructing a target knowledge graph according to the test point; generating an initial test case based on the test target knowledge graph through the case generation agent; executing the initial test case through the execution verification agent to obtain a test result; the test result comprises validity judgment and judgment basis of the initial test case; and in response to the test result, judging that the initial test case is an invalid test case, optimizing strategy parameters of each agent in the agent system based on the judgment basis through the evolutionary learning agent, and generating a new test case. The application can respond to the change of the test demand in real time, automatically adjust the test strategy, and improve the test quality and efficiency.

The invention provides a software defect test method based on a large model, and belongs to the technical field of software defect test.The method comprises the steps that the software test running condition is extracted, and defect feature vectors are obtained through the large model; constructing a characterization matrix through potential characterization analysis, and obtaining a defect combination through gain analysis; comparing the difference with the source code after the test to obtain a defect scene set, constructing a scene matrix, mapping the scene matrix with a representation matrix, and fusing defect combinations to obtain a defect representation vector; defects are positioned accordingly, and repair suggestions are generated in combination with a historical repair scheme of large model mining. According to the invention, accurate defect analysis and positioning are realized, the test efficiency is improved, and the missing detection rate and the false detection rate of defects are reduced.

The application provides a virtual machine processing method, device and equipment for software testing tasks, and relates to the field of virtual machines.The method comprises the following steps: obtaining a software testing task list and a template testing virtual machine; configuring a resource package of at least one software testing task on the template testing virtual machine according to a plurality of software testing tasks in the software testing task list, to obtain a plurality of virtual machines; performing dependency processing on the plurality of software testing tasks currently being tested by the plurality of virtual machines respectively; determining the running state of the current virtual machine in the dependency processing of the software testing task by the resource package of at least one software testing task after the current virtual machine is started according to a start task; and destroying the current virtual machine and starting the next virtual machine according to the running state until the plurality of software testing tasks are tested.The scheme of the application realizes the automatic management of the whole running cycle of the virtual machine, and improves the efficiency of virtual machine management and the utilization rate of network resources.

The embodiment of the invention provides a multi-module automatic testing method and device, electronic equipment and a storage medium, a to-be-tested target function module and a corresponding association module are obtained, and the association module is called in the operation process of the target function module and can influence the operation result of the target function module; according to the execution logic of the target function module and the association module, generating a contract file of the association module, the contract file being used for representing an expected operation result corresponding to the target function module when the association module executes at least one execution action; and when the test of the target function module is executed, obtaining a test result of the target function module by configuring the contract file. By generating the contract file corresponding to the association module and configuring the contract file when the target function module is tested, the test case corresponding to the association module is automatically obtained and executed, so that the efficiency and accuracy of software testing are improved.