288 results about "System under test" patented technology

A method of operating an IDS for a device includes performing a fuzzing operation on a software program being executed on a system under test, the software program corresponding to a deployed software program on the device monitored by the IDS and the system under test being configured to emulate at least one system of the device, the fuzzing operation including supplying fuzzing inputs to the software program, monitoring outputs of the software program, and detecting, based on the outputs, a vulnerability to intrusion in the software program caused by supplying the fuzzing inputs to software program. The method further includes generating and storing a vulnerability entry corresponding to the detected vulnerability, the vulnerability entry including information identifying the detected vulnerability, and updating, based on the vulnerability entry, at least one of a component of the IDS and a code portion of the deployed software program.

The invention provides a system performance testing method and system based on user behavior simulation, and relates to the technical field of system testing, and the method comprises the steps: obtaining service nodes of a tested system and service logic describing jump paths between the service nodes; generating a user behavior sequence for describing a sequence of accessing each service node by a user by combining a random walk algorithm with heuristic weight and random disturbance on the basis of service logic; generating a dynamic flow peak curve conforming to the natural behavior of the user by taking the maximum bearing flow of the tested system as a constraint; scheduling a user behavior sequence according to the dynamic flow peak curve to carry out pressure tests of multiple test types on the tested system; collecting performance data in the test process; abrupt data in the performance data are extracted through a sliding window; and outputting the performance data and the corresponding mutation data as a performance test result of the tested system. Accurate and efficient system testing can be carried out through data conforming to actual behaviors of a user under the condition that historical data does not exist.

One example method for training a machine learning model using fuzz test data comprises: at a test system: performing, using test configuration information, a plurality of fuzz testing sessions involving one or more systems under test (SUT); obtaining fuzz test data from one or more sources, wherein the fuzz test data includes test traffic data and SUT performance data associated with the plurality of fuzz testing sessions; training, using the fuzz test data and one or more machine learning algorithms, a machine learning model for receiving as input traffic data involving a respective SUT and SUT performance data and providing as output a stress state value indicating the likelihood of the respective SUT crashing or failing; and storing, in a machine learning model data store, the trained machine learning model for subsequent use by the test system or a SUT analyzer.

The invention relates to the technical field of chip verification, in particular to a verification method of a low-power-consumption narrowband communication chip based on a UVM (Universal Verification Model), which comprises the following steps: firstly, constructing a verification platform based on a UVM framework; based on the verification platform and the test function points, the environment configuration module configures the reference model and the input component; then, the to-be-tested system starts to run, and corresponding data or interrupt response is generated; at the moment, a monitor in the verification platform is inserted into a monitoring node, and the running state and data output of the to-be-tested system are captured in real time; and then, comparing the captured data with a calculation result of the reference model, recording the result, reading an interrupt register or other indication registers of the system to be tested at the same time, and comparing the result with an expected result to judge the correctness. In this way, the technical problems that in the prior art, a chip verification method is insufficient in function coverage rate, lacks system-level random verification and is low in verification efficiency are solved.

The embodiment of the invention provides an RAID fault simulation method and device, relates to the technical field of computer system structure storage and can also be used for the financial field, and the method comprises the steps that an RAID array of a corresponding level is constructed based on the preset number of RAID array hard disks and a hard disk list forming an RAID; performing parameter control on the constructed RAID array to construct a corresponding fault model, wherein the fault type of the fault model comprises at least one of a hard disk type fault, a control and configuration type fault, a logic fault and a read-write slow fault; determining a corresponding fault injection instruction and a fault recovery instruction according to the fault model, and inputting the instructions to a tested system, so that the tested system performs fault simulation; the RAID fault simulation method and device can solve the problems that the current RAID fault simulation cost is high, and hardware dependence, professional software dependence and manual operation dependence exist.

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 discloses a distributed testing method and system based on micro-service architecture. The method comprises the steps of generating a test task and a fault injection task according to a test plan; distributing each test case to a plurality of test agents according to the test task, so that the plurality of test agents respectively execute the corresponding test cases, and the plurality of test agents and the plurality of micro-service instances of the tested system run on a plurality of service nodes in a one-to-one manner; injecting a corresponding fault in an execution environment of the tested system according to the fault injection task; and collecting and summarizing log data related to the test, and outputting a test result and the influence of the fault on the test result according to the log data. According to the embodiment of the invention, a complete test process from test plan generation to test result analysis of the tested system is supported, high cooperation of fault injection and test execution is realized through accurate control of fault injection, and meanwhile, the distributed test system constructed based on the micro-service architecture can be adaptively expanded according to the scale of the tested system.

The invention relates to a pressure test anomaly detection method and device, computer equipment and a storage medium. The method comprises the following steps: performing a pressure test on a tested system to obtain pressure test data and monitoring index data of the tested system; inputting the pressure test data into a trained pressure test data prediction model, predicting the pressure test data of the tested system in a preset time period, and obtaining a pressure test data prediction result; based on a pre-constructed dependency relationship graph, determining a dependency relationship and an edge weight between the pressure test data prediction result and the monitoring index data; determining monitoring index prediction data according to the monitoring index data, the dependency relationship and the edge weight; and according to the monitoring index prediction data, predicting whether the detected system is abnormal in a preset time period, and obtaining an abnormal detection prediction result. The method is beneficial to improving the reliability of pressure test anomaly detection.

The invention relates to an end-edge-cloud full-link intelligent fault diagnosis platform based on a virtual rack, and the platform comprises a virtual rack system which is used for carrying out the dynamic modeling and fault type modeling of a tested system or part, and simulating the response characteristics of the tested system or part under a set boundary condition and a fault state, converting the response characteristic into an electrical signal; the edge calculation system is used for carrying out preprocessing and feature extraction on the collected electric signals; a user of the cloud intelligent fault early warning system carries out fault diagnosis, reliability analysis or residual life estimation according to the features extracted by the edge computing system. According to the platform, an end-edge-cloud full-link technology is verified based on a virtual rack model, so that the experiment cost can be remarkably reduced, and the early-stage test and deployment efficiency of a diagnosis algorithm can be improved; a modularized structure is adopted, algorithms and models in the modules can be modified and replaced according to actual needs, communication modes among the modules can be configured according to needs, and the system has good expandability and adaptability.

The invention is applicable to the technical field of interface testing, and provides an artificial intelligence-based interface testing optimization method, which comprises the following steps: constructing a multi-dimensional interface knowledge graph of a tested system, the multi-dimensional interface knowledge graph comprising but not limited to static structure information, business semantic information and running state feature information; based on the multi-dimensional interface knowledge graph, the driving state and the action space, deploying a test agent based on reinforcement learning; in the test agent, according to a preset multi-target reward function, executing a target-oriented exploration action, and generating a dynamic test case; according to the method, the twinborn test environment of the tested system is constructed for the test agent to perform exploratory test and generate the test case set, the value density of the test cases generated by the method is high, defects can be effectively found, the test cases are associated with interface service semantics and operation rules, and the reliability of the test cases is improved.

The invention provides a testing method, a server and a storage medium, and relates to the technical field of testing. The test method is applied to a server, a control module and a tested system module are deployed on the server, communication connection is established between the control module and the tested system module, the current operation environment of the server is a pre-silicon environment, and the test method can comprise the following steps: adopting the control module to test a target main program of a test tool through the target main program; test task information is sent to the tested system module, and the main program is used for shielding the peripheral interface and providing simulation test data; the tested system module is adopted to perform test operation based on the test task information to obtain a test result, and the test result is sent to the control module; and receiving a test result by using a control module. The control module deployed on the server based on the pre-silicon environment and the tested system model are tested, so that the dependence on an external control terminal is thoroughly eliminated, the server of the pre-silicon environment is independently operated and tested, and a result is obtained.

The subject matter described herein includes methods, systems, and computer readable media for improving remote direct memory access (RDMA) performance. A method for improving RDMA performance occurs at an RDMA node utilizing a user space and a kernel space for executing software. The method includes posting, by an application executing in the user space, an RDMA work request including a data element indicating a plurality of RDMA requests associated with the RDMA work request to be generated by software executing in the kernel space; and generating and sending, by the software executing in the kernel space, the plurality of RDMA requests to or via a system under test (SUT).

Techniques for assigning scenario-based tests to test assets are described. In an example, a scenario-based test operable to test a key performance indicator (KPI) of a System Under Test (SUT), a component behavior exhibited by a first component of the SUT, and a scenario characteristic are received. Based on the component behavior and the scenario characteristic, a first plurality of behavior models associated with the component behavior are identified. Based on the scenario characteristic a characteristic value is extracted from the scenario-based test. Each behavior model of the first plurality of behavior models is executed using the characteristic value to generate a first plurality of predicted behavior outcomes. Based on the first plurality of predicted behavior outcomes, a first test asset type from a plurality of test asset types is selected and the scenario-based test is transmitted to a test asset of the first test asset type.

The invention provides a method and a device for monitoring system operation, electronic equipment and a storage medium, and belongs to the technical field of software development, the method comprises the following steps: learning a normal operation mode and an abnormal feature of a system through log files of a plurality of equipment based on a plurality of abnormal detection algorithms; obtaining a data processing model for determining abnormal information of the log file; when the operation state of the to-be-tested system needs to be determined, obtaining a log file generated in the operation process of the to-be-tested system; and performing anomaly detection on the log file of the to-be-detected system through the data processing model to obtain an anomaly detection result of the to-be-detected system. Through the scheme, the data processing model is constructed, so that the process of monitoring the running state of the to-be-tested system can be intelligent and automatic, the accuracy and timeliness of monitoring the running state of the system are improved, manual intervention is not needed, the workload of operation and maintenance personnel is reduced, and the operation and maintenance efficiency and response speed are improved.

One method occurs at an impairment test system. The method includes receiving test configuration information for configuring a testing environment comprising an emulated switching fabric environment implemented using at least one switching application-specific integrated circuit (ASIC), wherein the test configuration information includes testing environment target state information; configuring, using the test configuration information, an impairment test session involving a system under test (SUT) and the emulated switching fabric environment; wherein configuring the impairment test session includes configuring at least one impairment controller for impairing the testing environment during the impairment test session; and initiating the impairment test session, wherein initiating the impairment test session includes generating and sending, using at least one traffic generator, test traffic via the emulated switching fabric environment and effecting at least one impairment of the testing environment during the impairment test session using a feedback control loop and the at least one impairment controller.

A system for creating a digital twin of a system under test may comprise processing circuitry and memory, with instructions stored thereon which, when performed by the processing circuitry cause the processing circuitry to receive multiple inputs with information corresponding to the system-under-test and provide the multiple inputs as a data input to a large language model. The processing circuitry may further configure an experiment with the large language model based on the multiple inputs, configure a process flow for the experiment, using data output generated from the large language model, and generate a training data set using the experiment and the process flow. The training data set may be used by the processing circuitry to create or configure the digital twin.

Provided are a video transmission delay measurement method testing an under-test system having an under-test system input port and an under-test system output port and comprising the following steps: generating an original test video and outputting the original test video to the under-test system input port of the under-test system, wherein the original test video has a central time corresponding to a video generating time at which the original test video is generated; receiving a played test video from the under-test system output port of the under-test system, wherein the played test video corresponds to the original test video and the played test video has the central time corresponding to the video generating time at which the original test video is generated; obtaining the central time of the played test video; calculating a time difference between a current time and the central time of the under-test system.

The invention discloses a method and device for recommending a test case, and the method comprises the steps: obtaining a change function for code change in response to the monitored code change of a tested system; determining a to-be-recommended regression test case for testing code change according to the multi-dimensional information, the multi-dimensional information comprises a dependency relationship and a mapping relationship of a change function obtained in advance, function semantic information of the change function, function semantic information of a dependency function, a historical execution record of the regression test case, and any multiple items in importance attributes of the regression test case; the dependency function refers to a function having a dependency relationship with the change function, and the mapping relationship comprises a regression test case and a function which can be tested by the regression test case; and performing regression testing on the tested system according to the to-be-recommended regression test case.

The invention discloses a vulnerability analysis method and device, electronic equipment and a computer program product, and relates to the technical field of artificial intelligence and information security. The method comprises the following steps: acquiring system feature information of a tested system, wherein the system feature information at least comprises basic system information, code feature information and business logic information; performing information arrangement on the basic system information, the code feature information and the business logic information based on the cue word template to obtain a system description text; the system description text is input into a vulnerability analysis model for task decomposition, a to-be-tested task list is obtained, and the to-be-tested task list comprises at least one to-be-tested task and a vulnerability analysis scheme corresponding to each to-be-tested task; and sequentially executing the vulnerability analysis scheme corresponding to each to-be-tested task to obtain a vulnerability analysis result of the tested system. According to the method, the vulnerability analysis efficiency can be improved, the manual analysis cost is saved, and vulnerability analysis is automatically performed.

The invention discloses a software and hardware adaptive test method and system for a PLC system, and relates to the PLC and programmable control technology, and the method comprises the steps: analyzing a tested PLC system configuration and test sequence description file, and generating a configuration parameter sequence of the tested PLC system; determining predicted result data of each test item, and generating a test data set; traversing each minimum executable dynamic test unit in the dynamic test unit set, generating a test program according to the test data set, setting a tested point in the test program, and recording a variable set; determining an execution sequence of the minimum executable dynamic test unit; generating a minimum executable dynamic test unit scheduling strategy; all minimum executable dynamic test units are loaded, and initialization of a dynamic test state machine is completed; and according to the execution sequence, executing the minimum executable dynamic test units one by one in sequence. The test efficiency, flexibility and adaptability can be improved, and the flexibility and portability of the test system are greatly improved.

The invention belongs to the technical field of automatic driving test, and particularly relates to an automatic driving vehicle key test scene generation method based on a large language model. The method comprises the following steps: 1, converting accident video data into semantic text description by a general large model, and establishing an accident video text library; 2, performing parameter fine adjustment on the open source large model by using the accident video text library, so that the large model has key features for identifying accident data; 3, inputting the design operation domain element conditions of the tested system into the fine-tuned large model, and generating a scene type meeting the test requirements according to the characteristics of the tested system; and 4, according to the scene type and the scene key elements output by the large model, performing parameter combination on the key elements by using a combination test method to generate key scene parameters. The invention solves the problem that the existing scene generation method is difficult to comprehensively consider the natural driving accident data characteristics and the design operation domain constraint of the tested system.

A system, method, and computer-readable medium for performing a data center management and monitoring operation. The data center management and monitoring operation includes: receiving a plurality of system under test (SUT) test plans, each SUT test plan comprising a plurality of SUT test cases; analyzing the plurality of SUT test cases, the analyzing comprising generating a phase component resolution score for each of the plurality of SUT test cases; generating a continuous schedule for the SUT test plan, the generating the continuous schedule taking into account the phase component resolution score for each of the plurality of SUT test cases; and, continuously orchestrating the SUT test plan based upon the continuous schedule for the SUT test plan.

The invention discloses a high-concurrency pressure test method based on elastic expansion and contraction and a related device. The method comprises the following steps: determining a test target; designing a test scene; configuring an elastic telescopic environment; under the designed test scene, a pressure test task is executed on the to-be-tested system, in the process of executing the pressure test task, performance indexes of the to-be-tested system are detected, resource configuration of the test scene is automatically adjusted according to the detected performance indexes of the to-be-tested system and a preset elastic scaling strategy, and a test result is obtained; the test result is compared with the test target, the high-concurrency pressure test of the to-be-tested system is completed, and the method and the related device can improve the accuracy and the reliability of the test.

The invention provides a distributed performance test method and system based on dynamic load prediction, and the method comprises the steps: periodically collecting the resource utilization data of a current slave node as the data of a current time point, and predicting the resource utilization data of a next time point; judging whether the resource utilization data at the next time point is abnormal or not based on a dynamic threshold verification algorithm, and starting a process migration mechanism based on a priority migration strategy; a bidirectional buried point probe is deployed at the slave node and the tested system, a density clustering algorithm is adopted to analyze and cluster the associated flow and resource data, and an abnormal problem mode is identified according to a clustering result; and displaying the relationship between the pressure request quantity of each test interface and the resource index as well as the abnormal problem mode in real time, and forming a full-link diagnosis view of pressure input-system response. According to the method, the response speed of the distributed performance test and the slave node resource scheduling efficiency are greatly improved, second-level time alignment is carried out on the flow data and the resource data, and bottom layer data support is provided for upstream evaluation analysis.

The present invention discloses a performance testing method and system for large amounts of industrial software big data. Among them, the performance testing method includes deploying an API request capture tool; simulating user access to the system under test, collecting API request information to generate a performance testing script; deploying a script management center tool for importing test cases, parsing the imported test cases, storing the parsed content in a use case maintenance table, and connecting to the database through a data testing script; deploying a test management center tool, including a message management center module for sending message tasks to the script management center, sending tasks for performance testing of large amounts of data; the performance testing script calls the data testing script, and the data testing script inserts test data into the database based on the status flag of the use case maintenance table, and the generated test data is used for performance testing of large amounts of data; for different business scenarios of industrial software, quickly create test data with large amounts of data.

Method for testing a system. A computer-implemented method for testing a system, in particular a computer program, a hardware system or an embedded system, is presented. Here, a first part of an execution structure for the system is examined using static analysis in terms of whether the system runs error-free when executing an execution path corresponding to the first part of the execution structure. If an error-free execution is determined in the static analysis for the first part of the execution structure, an examination of the system is carried out using dynamic analysis, which skips the execution path corresponding to the first part of the execution structure.

To provide a test precondition data derivation apparatus capable of easily deriving test precondition data corresponding to a test case.SOLUTION: Teacher data storage means stores teacher data used when generating, by machine learning, a model for deriving test precondition data that is either an SQL statement for registering data in a database of a system under test before execution of a test, or a file to be stored in a predetermined folder of the system under test before execution of the test, or both. Machine learning means generates a model for deriving the test precondition data by machine learning using the teacher data. Test precondition data derivation means derives test precondition data corresponding to a test case by applying the input test case to the model.SELECTED DRAWING: Figure 4

The embodiment of the invention relates to the technical field of test strategy adjustment, in particular to a system test strategy adjustment method based on a lavor curve, and the method comprises the steps: evaluating an ideal benefit score and an ideal time node of a tested system; determining test defects of the tested system, and respectively evaluating repair time required for repairing each test defect; based on the severity of each test defect, obtaining a plurality of repair groups, respectively calculating the total repair time for repairing each repair group, substituting the total repair time into a lavor curve formula, and determining the actual benefit score of each repair group in combination with the ideal benefit score and the ideal time node; respectively calculating an absolute value of a difference value between the actual benefit score and the ideal benefit score of each repair group as an adjustment evaluation value of each repair group, and determining the repair group with the minimum adjustment evaluation value as a target repair group; and repairing each test defect in the target repair group to complete system test strategy adjustment. The method makes up the blank of test strategy adjustment.

Various methods and processes, apparatuses / systems, and media for systemically and dynamically capturing digital experiences are disclosed. This disclosure presents methods and apparatuses for implementing a platform, language, cloud, and database agnostic automatic digital experiences capturing module configured to systemically and dynamically capture digital experiences to support guided training, process and procedures, and job aids. Customer journeys and corresponding validation steps are defined; test scenarios and defines test execution parameters are built. These definitions and configurations built are called by a scheduler, on a recurring basis, which executes the test scenarios against a system under test; and captures the resulting screenshots, videos, and interactions including highlights on image of user required selections. Results are then published and captured into an Experience database. A narrative author adds instructional text or context narratives which are linked to test scenario steps to the database.

The present invention discloses a method for identifying a graphics rendering mechanism based on rendering pipeline alignment. The method pre-executes a benchmark test program in CPU mode and GPU mode respectively, completes pre-alignment of the key stage processing interval and the stable working interval in the two modes to form a first sampling interval to achieve alignment of the rendering pipeline. The second sampling interval is formed based on the pre-alignment of frame complexity. The target sampling interval is obtained by finding the intersection of the first sampling interval and the second sampling interval. During actual testing, CPU load data of each of the two modes is obtained, the CPU load data is sampled according to the target sampling interval, and the correlation between the two sampling data is calculated to determine the graphics rendering mechanism of the tested system. The unified rendering action ensures that the sampling data covers the complete rendering cycle. The reasonable distribution of data density under different load scenarios is ensured by analyzing the frame complexity. The combination of the two improves the representativeness and statistical effectiveness of the data, and effectively improves the accuracy of identifying the graphics rendering mechanism.