56 results about "Test suite" patented technology

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 integrated circuits, and provides a verification platform, a verification method, electronic equipment and a medium. The verification platform comprises: an application layer bus function level model comprising a customized interface for a to-be-tested design for interacting with the to-be-tested design, the to-be-tested design being a PCIe protocol docking and protocol compliance test suite, and the to-be-tested design being a PCIe protocol docking and protocol compliance test suite; the PCIe protocol docking and protocol compliance test suite supports a verification demand of a physical layer intellectual property core and a verification demand of protocol consistency of a PCIe controller and a physical layer subsystem level; and the to-be-tested design interacts with the application layer bus function level model through the customized interface. Thus, the integration and through-flow debugging cost of subsystem protocol consistency verification is reduced, the verification period of a docking test of PCIe subsystem protocol standardization is shortened, and the completeness of docking verification of a full quantization protocol of a PCIe subsystem and the platform reuse efficiency are improved.

The invention relates to the technical field of deep learning testing, in particular to a deep learning test sample generation method utilizing a reinforcement learning optimization genetic algorithm, which comprises the following steps: constructing different types of seed data sets based on an image data set, and determining and initializing an initial population; obtaining a deep learning model, executing a genetic algorithm, and optimizing the initialized initial population; combining reinforcement learning to dynamically optimize genetic algorithm parameters; and performing iterative optimization on the optimized initial population through the optimized genetic algorithm to generate a new population, screening a data sample with a prediction error, and obtaining a test suite. The deep learning model and the genetic algorithm are integrated, the genetic algorithm is executed, reinforcement learning is introduced to dynamically optimize operation parameters of the genetic algorithm, the problems of local optimum and parameter sensitivity are solved, generation of a test suite with smaller distribution difference is promoted, the convergence speed of fitness can be effectively improved, and the robustness of the deep learning model is improved.

The invention relates to the technical field of software engineering and automatic testing, and discloses a self-adaptive multi-dimensional risk testing strategy design method and system and a storage medium. The method comprises the following steps: triggering a risk assessment service through a CI / CD assembly line, preprocessing original data obtained by a plurality of heterogeneous data sources, and generating a risk feature vector; outputting a multi-dimensional risk index by using the risk assessment model, fusing the multi-dimensional risk index and the dynamic weight thereof based on an adaptive weighted fusion model, and calculating a comprehensive risk score; dynamically updating a risk score threshold value used for dividing the risk level, automatically generating a test strategy matched with the current risk level according to a preset mapping rule of the risk level and the test strategy, and calling the test suite to automatically execute the test strategy; and collecting a test execution result and online operation data as training data, and regularly optimizing parameters of the risk assessment model. The test strategy can be dynamically adjusted according to the real-time risk situation.

A diversified validation test suite application (DVTSA) for a generative AI powered tool or large language model (LLM) tool includes at least first, second, and third control logics. The first control logic receives a seed test input or user input, analyzes the seed test input or user input, and extracts key elements for variations. The second control logic performs a coverage measurement of outputs of the first control logic. The third control logic causes a human validator to evaluate outputs of the second control logic relative to predefined coverage metrics and selectively and continuously iterate to cause outputs of the second control logic to increase LLM tool input robustness and output robustness from a first level to a second level greater than the first level, in both production and pre-production LLM tool processes.

The invention relates to an OCamml unit test system, which adopts a layered architecture design and comprises a test driving layer, a test case regulation layer and a test suite function layer. The test driving layer automatically manages compiling, execution and result archiving of OCAML test codes through Makefile scripts; the test case regulation layer defines and organizes test cases based on a standardized template, and each test case annotates and declarates information such as a unique identifier and an airworthiness demand number through structured metadata; the test suite function layer comprises an assertion module, a report generation module, a data conversion module and a test auxiliary module. Through the layered architecture design, standardized management and automatic execution of the test process are realized, the standardization and traceability of the test activity are ensured, a complete demand-test-verification traceability evidence chain is provided for airworthiness certification, and the strict requirement of the high-security field for unit test is met.

The application provides an artificial intelligence-based software test case automatic generation system and method, relates to the technical field of software testing, and comprises the following modules: a project management data acquisition module that collects code metadata and project management data; a dynamic risk weight allocation module that inputs the two types of data into a risk prediction model, outputs a risk heat map, and allocates the dynamic risk weight of the input parameters; a basic test suite generation module that generates a basic test suite according to the input parameters and the dynamic risk weight; and a software test algorithm execution module that starts a software test algorithm and iteratively evolves to generate a test case set. The application solves the technical problem that the existing software test case generation mode is difficult to effectively integrate software related data to identify high-risk links, and the generated test cases are insufficient in covering key areas and low in accuracy, achieves the technical effect of accurately identifying high-risk links of software, and the generated test cases are more targeted, thereby improving software testing efficiency and software quality evaluation accuracy.

This invention provides a method and system for evaluating the compatibility of power grid business applications with processor architectures. The method includes: determining a power grid business scenario-based test suite based on the type of the power grid business application to be evaluated. The test suite includes a power industry protocol test script for verifying the processor architecture's power protocols, a real-time control performance test script for evaluating the processor architecture's control performance, and a power grid data consistency verification script for verifying the processor architecture's timing synchronization. On a multi-platform test environment cluster, an automated task scheduling engine is used to execute compatibility tests of the power grid business application on each processor architecture within the multi-platform test environment cluster, based on the power grid business scenario-based test suite. A rule engine is used to generate a compatibility rating level based on multi-dimensional test indicators collected during the testing process, achieving a comprehensive evaluation of the compatibility of the power grid business application with the processor architecture in terms of functional adaptation, performance, and data consistency.

Systems and methods for checking the correctness of a computer program with at least one incorrect instruction inserted into at least one of a plurality of branches of the computer program. At least one prover generates a counterexample of computer program correctness in order to switch focus from a failed proof of the correctness of the computer program to a failed test of the correctness of the computer program.

The invention provides a continuous integration test balance method and device based on dynamic quality evaluation and a medium, and belongs to the technical field of vehicles. The method includes monitoring a code submission event, capturing changes in the code submission event, forming change set data, and determining a quality assessment indicator from a plurality of data sources. And determining a comprehensive risk coefficient of current code integration by utilizing the set risk assessment model according to the quality assessment index and the change set data. And dynamically deciding a corresponding test execution strategy according to the comprehensive risk coefficient, driving a corresponding test suite according to the corresponding test execution strategy, and distributing a corresponding test task to obtain a test execution result. The efficiency of continuous integration testing is improved, and testing resources are optimized.

A system accesses a code module that includes one or more units of code and instructs a machine learning model to generate a test suite based on a specification for the code module. The test suite includes tests for testing the code module to verify that one or more units of code successfully execute in accordance with the specification. The specification includes preconditions that precede successful execution of the one or more units of code and postconditions that exist following successful execution of the one or more units of code. The machine learning model generates the test suite. The system receives the test suite from the machine learning model. The system stores and / or transmits the test suite for use in testing the code module.

This invention discloses a method and system for generating static code checkers based on a large language model. The method involves: acquiring rule descriptions and a test suite containing positive and negative examples; generating detection logic corresponding to the rule descriptions and their corresponding checker specifications using the large language model; retrieving the corresponding API context based on the detection logic; generating candidate checker code based on the checker specifications, rule descriptions, test suites, and the retrieved API context; validating the candidate checker code based on the test suites and relevant rule descriptions; and obtaining the final checker code that passes the verification. The method proposed in this invention significantly reduces the technical threshold and development cost of customizing checkers for static analysis frameworks, efficiently generating checker code with high accuracy, low false positive rate, and low false negative rate, and possesses good scalability.

The invention provides a board card performance test method, device and equipment, and the method comprises the steps: obtaining a standard data file generated by testing a board card, and analyzing the standard data file to obtain a target test suite name and a measurement data set; determining a target board card type to which the target test suite represented by the target test suite name belongs; determining a target configuration file corresponding to the target board card type based on an associated file representing the corresponding relationship between the board card type and the configuration file; analyzing the target configuration file to obtain a performance standard value and a data analysis mode corresponding to the target test suite name; and determining a performance test result of the board card based on the measurement data set and the performance standard value through a data analysis mode. Therefore, the performance test efficiency of the board card can be improved.

Aspects of the invention include mutating each neural network of a portion of a first array of neural networks, wherein each neural network of the first array of neural networks is configured to select a respective sequence of test cases for testing a computing infrastructure. Causing each neural network of a second array of neural networks to select a respective sequence of test cases for testing the computing infrastructure. Generating a child neural network by performing a crossover operation between a mutated neural network of the portion of the first array and a neural network of the second array of neural networks, the child neural network generating a new sequence of test cases for testing the computing infrastructure.

Conventional testing of artificial intelligence (AI) agents is time-consuming and insufficient. Accordingly, embodiments provide an automated testing and validation framework for the tools utilized by AI agents. The framework may leverage artificial intelligence, including a classifier for classifying tools, a generative model that creates test cases based on the tool classification, an optimization model that maximizes test coverage while minimizing computation cost, an edge-detection model that identifies edge test cases, and / or an execution engine that manages execution of the test suite. The framework may be integrated into a continuous integration and continuous deployment (CI / CI) pipeline.

Dynamic reconfiguration of microservice test environments Processing within a computing environment is facilitated by determining a set of microservices required to execute a microservice test suite within a test environment, where the test environment is an active microservice test environment having multiple microservices. The processing also includes determining that the test environment lacks sufficient resources to execute the microservice test suite using the set of microservices, and dynamically removing at least one microservice from the test suite that is not required to execute the microservice test suite to free test environment resources for executing the microservice test suite using the set of microservices. The processing also includes starting execution of the microservice test suite within the test environment based on the dynamically removing.

To provide an olfactometric tool that prevents a flavor component from being adsorbed to an unintended site.SOLUTION: An olfactometric tool has a substrate and a cover that covers the surface of the substrate. The surface of the substrate is provided with a recess. The recess has, inside it, a capsule containing a flavor component. The surface of the recess is provided with a first adhesive layer. The cover has a second adhesive layer on a face facing the recess. In the same capsule, its surface is at least partially bonded to the first adhesive layer and the second adhesive layer.SELECTED DRAWING: Figure 1

Processing within a data processing environment is enabled by identifying a set of microservices required to run a microservice test suite in a test environment, where the test environment is an active microservice test environment with a plurality of microservices. The process also includes determining that the test environment lacks sufficient resources to run the microservice test suite with the identified set of microservices, and dynamically removing at least one microservice from the plurality of microservices that is not required to run the microservice test suite, in order to free up test environment resources and allow the microservice test suite to run. The process also includes initiating the execution of the microservice test suite in the test environment based on this dynamic removal.

The invention relates to a test system for measuring the chemosensory perception of a test subject. The test system comprises at least one test kit (5) in which a plurality of stimulus carriers (6) are introduced in a carrier material which is located in an item of packaging. Each item of packaging is provided with a machine-readable code by means of which the stimulus carrier located in the item of packaging can be clearly identified. The test system also comprises a user interface (3) and a data-processing device (1). The user interface allows: the machine-readable codes to be read in; colours and questions to be displayed to the test subject on a screen (4); and responses from the test subject to be detected. The data processing device (1) can be linked to the user interface (3) via a network and is designed such that, after receiving a machine-readable code via a linked user interface (3), the data processing device: identifies the associated stimulus carrier (6); displays a colour and one or more questions to the test subject on the screen (4) via the user interface (3); and stores the responses of the test subject detected by the user interface (3) so as to be associated with the stimulus carrier (6) and the test subject. The chemosensory stimuli are presented in the test system in combination with different defined colours, as a result of which the conscious recognition of the presented chemosensory stimulus is facilitated or made more difficult.

The application provides a high-speed random number generator based on a conservative chaotic system and a generation method thereof. The generated random number is subjected to two commonly used random number test standards, namely, a test suite NIST SP800-22 of The National Institute of Standards and Technology and another test suite TestU01, and experimental test results can prove that the generated random bit sequence passes the randomness test. The throughput of the high-speed random number generator can reach 19 Gbps.

The application discloses a hardware-in-the-loop automatic testing method, a Robot- Framework testing framework and a loop simulation platform, and relates to the technical field of testing methods. The hardware-in-the-loop automatic testing method comprises the following steps: based on the Robot- Framework testing framework, a test suite of a measured controller of a vehicle is created, wherein the test suite corresponds to a subfunction of the measured controller; based on the Robot- Framework testing framework, an execution action of the test suite is called; based on the Robot- Framework testing framework, a test report of the measured controller is generated according to the execution action of the test suite; and the test report of the measured controller is output. The method can effectively improve the testing efficiency of the measured controller and the development efficiency of the test suite, and the generated test report has a clear structure, is easy to understand and has high readability.

The invention provides an EDA software smoking test method, computer equipment, a program product and a storage medium, and the method comprises the steps: monitoring a target code in real time, and triggering a test assembly line through a network hook when the target code is updated; under the test assembly line, the updated target code is automatically compiled, and an executable file is obtained; a smoking test suite is automatically operated on the executable file, a test result report and a test intermediate report are output, and the smoking test suite comprises a basic function test, a performance benchmark test and a static analysis test; and analyzing the test result report and the test intermediate report according to a predefined cue word template and a large language model, generating a natural language abstract report, and performing hierarchical notification. According to the scheme, the test can be executed immediately after the code is updated, and meanwhile, the test data is automatically collected and stored, so that code changes are monitored in real time, software functions and performance are prevented from being negatively influenced, problem response is accelerated, and research and development efficiency is improved.

A method performed by a processing system including at least one processor includes monitoring a software repository for code changes, detecting a code change in a software instance that is stored in the software repository, generating a container image of the software instance in response to the detecting, creating a container for the container image, wherein the container encapsulates software needed to run a test suite on the software instance, configuring a testing platform for a development environment used to create the software instance, executing the test suite for the software instance by running the software encapsulated in the container on the testing platform, and publishing a test output of the test suite as a human-readable scorecard for the software instance.

In some implementations, the techniques described herein relate to a method including: executing, by a processor on a collection of source code, a test suite comprising a plurality of tests; clustering, by the processor based on a result of executing the test suite, the plurality of tests into a plurality of test clusters; designating, by the processor for a particular test cluster within the plurality of test clusters, a cluster-representative test; and executing, by the processor on a subsequent collection of source code, a culled test suite, the culled test suite comprising a subset of the plurality of tests including the particular test cluster's cluster-representative test.

The disclosure relates to system and method for smart test execution in a network test automation platform. The method includes retrieving set of parameter values for each of plurality of test cases in test suite corresponding to set of risk parameters based on latest release of product code. The method further includes calculating risk index for each of plurality of test cases based on set of parameter values and predefined set of weightage scores. The method further includes determining execution order of plurality of test cases based on calculated risk index. The method further includes executing set of priority test cases from plurality of test cases based on execution order and predefined threshold risk escape value. The method further includes dynamically rendering in real-time, one or more live charts based on current risk escape value upon execution of the set of priority test cases via GUI.

The invention discloses a test suite generation method and device, electronic equipment and a storage medium, belongs to the technical field of software engineering, and is used for solving the problem that a test suite generated by a related test suite generation technology is poor in quality. The method comprises the steps of obtaining a plurality of test cases, and performing feature extraction on the test cases to obtain input features, execution features and coverage features of the test cases; the coverage feature is determined based on code coverage information of the test case and function demand coverage information; constructing a feature vector of the test case based on the input feature, the execution feature and the coverage feature; based on the feature vectors of the test cases, grouping the test cases through an improved clustering algorithm to obtain a plurality of test case sets; and for each test case set, based on the feature vector, performing redundant test case identification to obtain an identification result, and based on the identification result and the test case set, generating a test suite.

A diversified validation test suite application (DVTSA) for a generative artificial intelligence (AI) driven tool or a large language model (LLM) tool includes at least a first control logic, a second control logic, and a third control logic. The first control logic receives a seed test input or a user input, analyzes the seed test input or the user input, and extracts varying key elements. The second control logic performs an overlay measurement of the output of the first control logic. The third control logic causes the human verifier to evaluate an output of the second control logic with respect to a predefined coverage metric, and selectively and continuously iterating such that the output of the second control logic increases LLM tool input robustness and output robustness from a first level to a second level greater than the first level during production and pre-production LLM tools.

This invention discloses a multi-agent-driven automatic software vulnerability repair method and system, relating to the fields of software security and automated program repair. The method includes: an offline phase, utilizing a blind distillation strategy to construct a multi-view structured index to eliminate data distribution bias between training and inference; an online phase, firstly, a diagnostic agent extracts vulnerability facts using static analysis tools to generate enhanced queries; subsequently, a multi-view routing retrieval and structure-aware soft voting mechanism are employed to obtain high-precision repair examples; finally, an iterative repair agent generates and corrects code within a feedback loop between the compiler and test suite. This invention solves the problems of look-ahead bias, high retrieval noise, and low open-loop generation accuracy in traditional retrieval enhancement generation, significantly improving the accuracy and robustness of software vulnerability repair.

The invention provides a software test case automatic generation system and method based on artificial intelligence, and relates to the technical field of software testing, and the system comprises a project management data acquisition module which collects code metadata and project management data; the dynamic risk weight distribution module inputs the two types of data into a risk prediction model, outputs a risk thermodynamic diagram and distributes dynamic risk weights of input parameters; the basic test suite generation module generates a basic test suite according to the input parameters and the dynamic risk weights; and the software test algorithm execution module starts a software test algorithm, and iteratively evolves to generate a test case set. According to the method and the device, the technical problems that an existing software test case generation mode is difficult to effectively integrate software related data to identify a high-risk link, and the generated case is insufficient in coverage of a key area and low in accuracy are solved, accurate identification of the software high-risk link is achieved, the generated test case is more targeted, and the test efficiency is improved. And the software testing efficiency and the software quality evaluation accuracy are improved.