281 results about "Test input" patented technology

The invention proposes systems and methods for testing on-device machine learning models such as network models of a device based on an access to normal input and output channels, wherein a structure of a model is designed through training such that the model produces distinctive outputs to a given set of test inputs only so long as its internal structure remains in a desired state. Models may be rendered susceptible to such testing via model pre-training with training inputs designed to train the model into an appropriate structure and / or to cause the model to produce a distinctive output if later presented with a certain set of test inputs. If the structure of the model remains within allowable limits, the device will produce a predictable output when tested. If not, the device may be enforced to return to a pre-trained model.

Existing techniques for automated generation of test data for testing web applications need detailed requirement documents. The present disclosure receives a plurality of textual documents to extract context. Rephrasing the extracted context by implementing a plurality of rules and passing extracted context along with a first set of prompts to Large Language Model (LLM). Generating program, validator and first set of constraints for extracted context and generating test data by running the generated program. Assigning ranking to test data and selecting the test data with highest ranking. Statically refining the generated program by calling a mathematical library function on the highest ranked test data to generate structural information and modifying language of the second set of prompts passed to the LLM. Dynamically refining the generated program by passing feedback generated by executing the highest ranked test data on a web application and refining the response obtained.

The invention relates to a fuzzy test Kubernete-based three-party component vulnerability mining method, which comprises the following steps of: firstly, automatically identifying a user controllable function by combining a large language model based on an official document, a configuration list and source code information of a three-party component, and constructing a keyword dictionary to assist in large-scale identification of a potential target function; then analyzing context information of the target function and an interaction relation of the target function in a Kubernetes cluster, constructing a front dependency library required by fuzzy testing, and automatically generating a fuzzy testing driving program; integrating a fuzzy test driving program with a three-party component source code, performing code instrumentation, pre-compiling and corpus optimization, generating test input according to a set variation strategy, dynamically monitoring a coverage rate and an abnormal behavior, and continuously optimizing a test corpus; and finally, based on the crash behavior of the fuzzy test record, analyzing a trigger path of the vulnerability, performing vulnerability verification in a local Kubernetes cluster environment, and finally generating a vulnerability analysis report.

An example system includes: a first large language model; a memory storing instructions and one or more processors operable to execute the instruction to: receive a natural language text string from a client device, where the natural language text string comprises a formal rule expressed in natural language; input the natural language text string and a control string into a large language model (LLM), where the control string is configured to cause the LLM to generate a formal expression representing the formal rule of the natural language text string; output, by the large language model, the formal expression to an output device; and evaluate a consistency of the formal expression using a test input.

A method including receiving a user input from a user device. The method also includes generating test inputs including the user input and modified inputs. The user input is processed with a rephrasing model to form the modified inputs. The method also includes executing a test model to generate test outputs, including an original test output and modified test outputs, from processing the test inputs. The method also includes generating similarity scores by performing similarity comparisons among the test outputs. The method also includes determining a model confidence from the similarity scores. The method also includes routing the user input responsive to the model confidence satisfying or failing to satisfy a confidence threshold.

The invention discloses a performance test method and device of a storage device, an electronic device and a storage medium, and relates to the technical field of data storage, and the performance test method comprises the steps of controlling the storage device to execute data operation in response to test input; wherein the data operation comprises read operation and / or write operation; in the process of executing the data operation by the storage device, obtaining performance data of the storage device through the performance test model; wherein the performance data comprises performance data of a data link of the storage device and performance data of the storage controller, the storage controller is electrically connected with the storage device through the data link, the data link is used for transmitting data between the storage controller and the storage device, and the performance test model comprises at least one performance monitoring tool. The at least one performance monitoring tool is used for performing performance monitoring on the data link and the storage controller; and generating a performance test report of the storage device according to the performance data. The technical effect of improving the performance test efficiency of the storage device can be achieved.

The present application provides a method and apparatus for batch generation of test cases, and a computing device. The method comprises: acquiring a function to be tested of a target software product; acquiring an action factor and a data factor corresponding to said function; the action factor is used to describe at least one of the following types of information in a test case: a pre-condition, a test step, and an expected result; the data factor comprises at least one test input parameter corresponding to said function, and multiple values of each test input parameter; combining the plurality of values of the at least one test input parameter, and obtaining multiple data combination results; on the basis of the action factor and the multiple data combination results, generating a plurality of corresponding test cases in batches; and displaying the generated multiple test cases to a user. The described method can not only increase the efficiency of generating test cases, but also avoid the problem of missed testing.

The invention provides a test script generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining test input information containing demand information and test data, carrying out the structural processing of the test input information into a parameter dictionary, and determining a target feature based on the test input information to match a target test template constructed based on a specific grammar template engine, and finally, binding the parameter dictionary with the target test template, and dynamically rendering to generate a script file conforming to a test rule. According to the method, the test input information is subjected to structured processing to form the parameter dictionary, the target template is accurately matched and dynamically rendered, and the script generation efficiency and quality are greatly improved.

The invention provides a method and a device for generating test cases in batches and computing equipment. The method comprises the following steps: acquiring a to-be-tested function of a target software product; an action factor and a data factor corresponding to the to-be-tested function are obtained, the action factor is used for describing at least one of the following information in a test case: a precondition, a test step and an expected result, and the data factor comprises at least one test input parameter corresponding to the to-be-tested function and a plurality of values of each test input parameter; combining the plurality of values of the at least one test input parameter to obtain a plurality of data combination results; generating a plurality of corresponding test cases in batches according to the action factors and the plurality of data combination results; and displaying the plurality of generated test cases to the user. The method not only can improve the generation efficiency of the test case, but also can avoid the problem of missing test.

The invention provides an automatic testing method and system based on an Internet of Things model, electronic equipment and a storage medium, and aims to solve the problems of low efficiency and incomplete test of compiling a model test case. The method comprises the following steps: exporting a specific format file of a product model on an Internet of Things platform, and analyzing the file to obtain a product model test case; extracting each field content under attributes, events and service function types; respectively storing the extracted field contents according to events, attributes and services; and automatically generating test input data for each test item in combination with the data type and the parameter value range in the field content through an effective equivalence class and boundary value method, and storing the automatically generated test input data in the test input data item of the corresponding test case file to complete the automatic generation of the test case. The test case can be automatically generated, the test efficiency is improved, and the timeliness, stability and reliability of the test case are effectively improved.

The present invention provides a high-reliability unit test automatic generation method and apparatus based on a conversational large language model. First, projects are parsed and analyzed to extract context information, then the testing necessity of a method to be tested is determined, and different test direction lists are generated on the basis of the division of testing purposes. After initial test cases are generated and repaired, input data searching is performed on passed test cases to improve test input data. Subsequently, mutation testing is performed on all test cases, and test cases are regenerated for failed mutations to improve assertion quality. Finally, successful test cases are reduced and merged to avoid overlapping. The present invention is superior to existing methods in terms of coverage, test data integrity, assertion quality, and the like, and has stable performance across projects of different scales and fields. The generated test cases have significant advantages in readability, understandability, maintainability, test input data integrity, assertion quality, and volume.

According to various embodiments, a method for testing a computer program is described, comprising performing several test iterations, wherein for each test iteration a reference test input and a mutation for the reference test input are selected according to a selection strategy (201), and a test input for the computer program is generated by applying the selected mutation to the selected reference test input, wherein for each test iteration a test run of the computer program is performed with the respective generated test input for the computer program, wherein for each test iteration a test coverage increase (209) is determined which is achieved by the test run, and wherein the selection strategy (201) is trained by means of reinforcement learning, wherein the test coverage increases (209) are used as rewards (204).

The invention belongs to the technical field of computer information processing, and particularly discloses an internet of things equipment firmware-oriented fuzz test input optimization device, which comprises a static analysis module used for carrying out firmware analysis through an API interface, identifying an input transmission point of bare computer firmware, and realizing automatic identification through a custom script; the dynamic simulation module is used for identifying an input transfer point of the firmware of the real-time operating system and realizing a length inference mechanism and a final input transfer algorithm; the input routing mapping module is used for identifying and dynamically monitoring a data retrieval point, an input processing point and an availability check instruction in firmware to obtain a mapping result; the input transmission point identification module is used for automatically identifying an input transmission point of the firmware based on the mapping result; and the sensing module is used for dynamically determining the minimum length and the maximum length of the input data of the input transmission point, optimizing the input transmission point of the firmware fuzz test, and dynamically calculating the length of the reserved data. According to the invention, the fuzz testing efficiency can be improved.

Containerized orchestration of secure socket layer virtual private network benchmarking is disclosed. A test portal can accommodate developing test event information (TEI) based on test input information. Test input information can be used to generate event images, e.g., containers, that can be readily scaled, mutated, etc., via a containerization environment instance. One or more event image can be groups into a pod. An event image can be a new container, a previously used container, or a permutation of a container. Event image(s) can be retrieved from external sources, e.g., a library, a commercial vendor of event images, etc. TEI can be based on a pod(s) and communicated to a device-under-test (DUT). DUT performance can be measured, and results can be accessed by an entity, e.g., a test engineer, a results analysis engine, etc. Results can be employed to direct subsequent DUT testing.

The invention discloses an automatic test input generation method based on dynamic page splitting, and belongs to the technical field of dynamic webpages. In order to effectively verify the semantic consistency of a page in a complex Web application supported by multiple languages, the method comprises the following steps: acquiring a dynamic webpage to be tested, and analyzing a document object model structure of the dynamic webpage based on a Cypress framework; the monitored dynamic content elements are classified and marked in real time by using the real-time DOM operation capability of Cypress; on the basis of the structural information of the page components, in combination with integration of Cypress and GraphQL API, dynamically obtaining test data from a background, and automatically generating a test input template matched with the current state of the page; the method is applied to dynamic page testing in real time; generating a test report; a natural language processing module is introduced, and text content in the page is analyzed; and optimizing a page splitting strategy, an input generation rule and GraphQL query configuration.

The invention belongs to the technical field of automatic testing, and particularly relates to a web automatic testing method and system.The method comprises the steps of automatic script recording and dynamic adaptation, layered framework collaborative testing and data verification and continuous integration testing and test report generation. Wherein automatic script recording is dynamically adapted: recording a page operation script through a playwright tool and applying the page operation script to a UI layer, and when the page UI changes, recording the script again and replacing the original script without element-by-element positioning and code modification; in the hierarchical framework collaborative testing and data verification, a data access layer inputs test data, a UI layer executes page operation and captures display data, and a business logic layer calls an API interface to obtain real data and compares the real data with the display data for verification. According to the method, a web automatic test framework is constructed in combination with the advantages of playwright, a structured engineering project is more suitable for project operation and maintenance, and a sustainable integrated test environment is constructed by utilizing jenkins, so that test input, execution and output are automated.

The invention relates to the technical field of kernel fuzz testing, and provides a fuzz test input generation method based on characteristic unification and related equipment, the method comprises the following steps: generating a function call graph and a plurality of control flow graphs of a target system, and integrating the function call graph and all the control flow graphs to obtain an inter-program control flow graph; constructing a kernel code calling chain based on the inter-program control flow diagram; carrying out characteristic unified representation modeling based on the kernel configuration file and the kernel code call chain of the target system, and generating a configuration code relation mapping table; extracting related information of the characteristic related interface function according to the kernel code call chain, and performing grammar conversion based on the related information to generate a system call test protocol template of the characteristic related interface function; and generating fuzzy test input of the target system based on the system call test protocol template and the configuration code relation mapping table. According to the method provided by the invention, the correlation between kernel characteristic configuration and fuzzy test input can be improved.

The invention discloses a read-write function test method and device for a storage chip, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining a test requirement, input by a user, of a to-be-tested storage chip, the test requirements at least comprise the following information: storage unit matrix parameters and data writing mode requirements of the to-be-tested storage chip; embedding the test requirement into a preset cue word template to obtain a test cue word; inputting the test cue word into a trained test case generation model to obtain a target test case; and calling the target test case to execute a read-write function test process. According to the scheme, the test case can be automatically generated according to the test requirements, various test scenes can be quickly adapted, the time and the economic cost for manually compiling the test case are remarkably reduced, and the test efficiency is greatly improved.

A semiconductor device includes a plurality of test circuits connected between a test input pin and a test output pin, and a TAP controller connected to a plurality of test control pins and configured to define a data transmission path between the test input pin and the test output pin in response to test control signals received by the plurality of test control pins, the data transmission path including at least one of the plurality of test circuits.

The invention relates to the technical field of software testing, and discloses an LLM and SMT solver-based test case generation method, which is characterized in that a search type test is taken as a core drive, when coverage stagnation occurs, an execution path is subjected to semantic analysis by means of an LLM, a logic expression for describing a target path condition is generated, and in consideration of the limitation of the LLM in logical reasoning, a test case is generated; the system further introduces an SMT solver to perform verification and reasoning on a logic expression output by the LLM, finally feasible test input is generated, and the test efficiency and the coverage rate are improved. According to the method, the efficiency and precision of path coverage are improved, good adaptability and universality are achieved, the method is suitable for various languages and complex program structures, and the intelligent level and engineering practical value of automatic testing are effectively improved. The problem of insufficient accuracy of a large language model in complex path constraint reasoning is effectively relieved.

The invention provides a virtual machine vulnerability mining method and device, equipment and a storage medium, and the method comprises the steps that a plurality of execution tracks of a plurality of input programs input into a virtual machine when the input programs are executed by the virtual machine are obtained, each input program corresponds to one execution track, and the execution tracks are no-vulnerability tracks when the virtual machine executes; according to the trajectory similarity of the execution trajectories, N target execution trajectories with the similarity meeting a preset condition are obtained from the execution trajectories, a target input program corresponding to each target execution trajectory is obtained, and N is a natural number; and according to each target execution track and the corresponding target input program, obtaining a test input program for mining virtual machine vulnerabilities through a large language model. According to the method provided by the invention, the technical effect of deeply mining the vulnerabilities of the virtual machine while ensuring the grammatical structure is realized.

A network function configuration and test generation framework are configured to generate a test script indicative of a configuration and test cases for verifying a virtual function implemented in a virtualized computing environment. A test input is received that encodes information for verifying a network function. The information includes identification of a test tool, a target testbed for testing the network function, and network information including parameters for network conditions to be operational during testing of the network function. A data parser translates and formats the test input. The formatted test input in input to a topology discoverer configured to identify a network topology of the testbed and which network functions deployed on the testbed are emulated and which network functions deployed on the testbed are real. A configuration generator generates a configuration file usable to configure applicable network functions and their functionalities.

The invention relates to the technical field of software development, and discloses a code testing method and device, computer equipment and a storage medium. The code testing method comprises the following steps: determining test input information and a target test type through an obtained target test request; determining a to-be-tested code and a test scheme file according to the test input information, and preprocessing the to-be-tested code and the test scheme file to obtain a target knowledge graph corresponding to the target test request; searching a target test design task matched with the target test type from a preset test task list; determining a task cue word, a task analysis model and a task output template according to the target test design task, and obtaining a task output result according to the task cue word, the target knowledge graph, the task analysis model and the task output template; when it is confirmed that the task output result is the target test case, test processing on the to-be-tested code is executed according to the target test case, and a target test result is obtained. According to the invention, the interaction complexity is reduced, and the test efficiency and the test accuracy are improved.

A method of updating a sequence model for meta-continual learning, the method including generating an episode comprising a training dataset and a test dataset, updating an internal state of the sequence model by performing a forward pass of the training dataset on the sequence model, wherein the internal state is updated based on a parameter of the sequence model, generating an output corresponding to a test input included in the test dataset by performing a forward pass of the test input on the sequence model based on the updated internal state and the parameter, determining a difference between the output corresponding to the test input and a target test result corresponding to the test input as a meta-loss, and updating the parameter of the sequence model based on the meta-loss.

A network function configuration and test generation framework are configured to generate a test script indicative of a configuration and test cases for verifying a virtual function implemented in a virtualized computing environment. A test input is received that encodes information for verifying a network function. The information includes identification of a test tool, a target testbed for testing the network function, and network information including parameters for network conditions to be operational during testing of the network function. A data parser translates and formats the test input. The formatted test input in input to a topology discoverer configured to identify a network topology of the testbed and which network functions deployed on the testbed are emulated and which network functions deployed on the testbed are real. A configuration generator generates a configuration file usable to configure applicable network functions and their functionalities.

The invention discloses a vehicle gauge semiconductor chip scanning detection device and a control method. The device adopts a laminated design and comprises a test substrate, a test circuit layer and a chip mounting layer. The chip installation layer is provided with a plurality of to-be-tested chip installation grooves, and each groove is internally provided with a test input pad and a test output pad which are electrically connected with the test circuit layer, thereby facilitating the quick and accurate connection of to-be-tested chips. The test circuit layer is responsible for signal transmission and guidance, and ensures that a test signal can be accurately sent and a response from a chip is received. Particularly, a heating unit and a vibration unit are integrated on the test substrate, so that the extreme temperature and high-vibration environment in the actual operation of the automobile can be simulated. According to the scheme, simultaneous detection of multiple chips is realized, the test efficiency is improved, the test result is closer to reality, and the test accuracy and reliability are enhanced.

A computing platform may receive test input information comprising input information to validate code for an application. The computing platform may test the code by inputting the test input information into the application and executing the code to produce initial test results. The computing platform may store the test input information / initial test results in a table. The computing platform may identify that subsequent testing should be initiated. The computing platform may automatically identify the test input information and the corresponding initial test results using the stored table. The computing platform may automatically test the application by inputting the test input information into the application and executing the application to produce automated test results. The computing platform may compare the automated test results to the initial test results. Based on identifying that the automated test results match the initial test results, the computing platform may deploy the application.

The invention discloses a method and a system for automatically repairing context learning code vulnerabilities of a large language model, and aims to solve the problems that invalid patches are easily generated, vulnerability causes are misunderstood and verification is lacked in existing LLM zero sample repairing, and a traditional method depends on annotated data or test input. The method comprises the following steps: processing a vulnerability code-patch pair containing a plurality of different CWE types and a target vulnerability code, extracting vulnerability-related codes and constructing an example pool; an adaptive example is selected through comprehensive comparison of semantics, lexical and structural similarity; generating prompt words in combination with related codes and adaptive examples, driving LLMs to generate candidate patches, and outputting effective patches after verification of multiple LLMs; the system comprises a code processing module, an example selection module and a patch generation and verification module. According to the method, LLM and a large amount of annotated data do not need to be finely adjusted, the repair efficiency and accuracy are improved, the labor cost is reduced, multi-language extension is supported, and industrialization potential is achieved.

A rewrite control step includes causing, in response to a rewrite instruction of the response coefficient from an external system, a storage to execute a rewrite process of rewriting the response coefficient from a first response coefficient to a second response coefficient. An update control step includes causing a circuit unit to execute an update process of updating the circuit unit from the first response coefficient to the second response coefficient. A generation control step includes, after the update control step, causing a digital circuit unit to execute a generation process of generating a test output signal according to a test input signal input to a MEMS element or an analog circuit unit. A determination step includes executing a determination process of determining whether or not the test output signal falls within an allowable range specified by the test input signal.

This application belongs to the field of computer information processing technology and specifically discloses a fuzz testing input optimization device for IoT device firmware. The device includes a static analysis module for analyzing firmware through an API interface, identifying input transfer points of bare metal firmware, and realizing automatic identification through a custom script; a dynamic simulation module for identifying input transfer points of real-time operating system firmware, and implementing a length inference mechanism and a final input transfer algorithm; an input routing mapping module for identifying and dynamically monitoring data retrieval points, input processing points, and availability check instructions in the firmware to obtain mapping results; an input transfer point identification module for automatically identifying the firmware's input transfer points based on the mapping results; and a perception module for dynamically determining the minimum and maximum lengths of input data at the input transfer points, optimizing the input transfer points of firmware fuzz testing, and dynamically calculating the retained data length. This application can improve the efficiency of fuzz testing.