11 results about "Test adequacy" patented technology

The application provides a kind of information system software-oriented test sufficiency evaluation method and system, comprising: identifying evaluation elements for test sufficiency evaluation;Based on the system feature state in evaluation element, information system software state diagram is constructed;Test sequence is generated by traversing information system software state diagram, and test sequence set is constructed based on test sequence;Test sufficiency evaluation index is calculated;Test sufficiency fuzzy comprehensive evaluation parameters are set;According to test sufficiency evaluation index, the membership vector of test sufficiency basic evaluation index is calculated;Test sufficiency fuzzy comprehensive evaluation result is calculated.The test sufficiency evaluation method based on system feature state and fuzzy comprehensive evaluation method can meet the characteristics of large scale, multiple users, various functions and high complexity of information system software, and can comprehensively quantify the sufficiency and completeness of information system software test from multiple angles.

The invention discloses a test method for software containing a deep neural network, which comprises the following steps of: firstly, analyzing software characteristics of the software containing the deep neural network, and designing a mutation operator according to the software characteristics; and then applying the mutation operator to software containing the deep neural network for mutation to generate a software variant. And then analyzing and judging whether the current test case set meets the test sufficiency requirement or not by using the software variant. If yes, the current test case set is directly used for conducting software testing on the software containing the deep neural network. And if not, generating a new test sample based on the metamorphic test technology and the generative adversarial network model to expand the test case set. And finally, performing software testing on the software containing the deep neural network by using the expanded test case set, and analyzing and judging whether the expanded test case set meets the test sufficiency requirement or not. If yes, proving that the software is fully tested, and ending the test. And if not, repeating the steps to continue to expand the test case set.

This invention provides a robustness testing method and system for the kernel interface of an airborne real-time operating system. It employs a multi-dimensional equivalence class partitioning model for parameter classification, systematically enumerating six categories—legal values, boundary values, illegal values, null pointers, extreme values, and permission exceptions—based on two dimensions: data type and semantic role. This avoids the problem of overlooking exception categories in manual, experience-based design. Orthogonal lists are used for parameter combination, ensuring that any equivalence class combination between any two parameters is tested at least once. At the test execution level, four mechanisms are deployed: watchdog deadlock detection, memory sentinel out-of-bounds detection, kernel panic callback capture, and non-volatile progress marker restart detection, making result determination independent of the API return value itself. The structured test report generated by this invention includes coverage statistics, detailed abnormal test cases, and defect location information, serving as objective evidence of DO-178C robustness testing activities and meeting the airworthiness certification requirements for test sufficiency and traceability.

The invention discloses a long method-oriented unit test case generation method, which comprises the following steps of: firstly, identifying a to-be-tested target conforming to the characteristics of a long method in codes, and generating a decomposition and reconstruction suggestion of the long method by utilizing a large language model; secondly, a static analysis rule is designed, triple verification of variable action range, return value consistency and control flow integrity is carried out on the reconstruction scheme, and potential invalid suggestions are eliminated; and finally, constructing a context sensing prompt template for a reconstruction method generated according to the final suggestion so as to generate an initial test case, and after compilation detection and execution verification, implementing multiple rounds of iterative repair on a failed case so as to improve the performability. According to the method, the technical problems of low coverage rate and insufficient passing rate in long-method unit test generation are effectively solved, and the test sufficiency and reliability of a complex code structure are remarkably improved.

This invention discloses a robot control software testing method integrating DQN and PSO. First, based on the path association characteristics in the robot control software, paths are divided into a high-association path set and a low-association path set. Then, for the low-association path set, a suitable sample set is updated, and the parameters of the DQN network are fine-tuned to achieve reuse of the DQN model for high-association paths by low-association paths. Finally, based on the PSO algorithm, starting with an initial population with high contribution, a test case set covering the target path is generated. This method, through the differentiation of path association characteristics and the efficient reuse of the DQN model, significantly improves testing efficiency while ensuring the sufficiency of robot control software testing, such as path coverage integrity and control accuracy compliance, providing technical support for the reliability verification of robot control strategy software.

This invention discloses a test case selection method for deep reinforcement learning software based on state level coverage. This method is the first to propose a coverage criterion measurement design for deep reinforcement learning software and to select test cases based on state coverage criteria. By defining a primary exploration space, discretizing the continuous state space, designing state coverage criteria based on state boundaries and k-multiple regions, and using these coverage criteria to guide test case selection, this method improves testing efficiency and more effectively exposes erroneous behaviors in deep reinforcement learning software. This method can serve as a metric for measuring the adequacy of deep reinforcement learning software testing and can also guide test case selection, thus contributing to improved reliability and security of deep reinforcement learning software.

The invention provides a model-driven algorithm test method and system, a computer and a storage medium, and the method comprises the following steps: carrying out the standardization processing of a non-standard algorithm demand, and generating a structured algorithm demand document; based on the standardized algorithm requirement document, utilizing a large language model to generate test data information meeting the test sufficiency requirement; analyzing an algorithm source code structure corresponding to the algorithm demand, and constructing a meta-model of the measured algorithm; loading the meta-model of the measured algorithm, importing test data information, completing transformation from the meta-model to an instantiated test model, and forming a test case set; converting the test case set into executable test scripts in batches based on an extensible style sheet conversion language template; and executing the test script in the algorithm test engine. Through the model driving test technology and the algorithm test engine, automatic verification of the test case is completed, and the test result is collected, so that the intelligence and automation level of algorithm test is effectively improved.

This invention provides a software system testing method, apparatus, device, and readable storage medium. The software system testing method includes: acquiring test service options triggered by controls on the software system test interface, and performing corresponding tests based on the test service options; acquiring process data during the test based on CAN message trigger events or status trigger events; processing the process data to obtain data processing results; linking the software system test interface window environment variables to the data processing results; and displaying the functional test results and CAN interface test results of each piece of equipment software based on the data processing results. This invention ensures sufficient testing, displays the system test results of each piece of equipment software in real time on the software system test interface on a host computer, improves testing efficiency and quality, and thus enhances the reliability of the equipment software after testing.

The application discloses a kind of equipment software digitization closed loop system test method and device, it is related to equipment software digitization test field, this method includes creating digitization virtual machine and soft bus in digitization test environment, to be tested equipment software and test piece of different kinds based on different hardware platform are commonly operated in a digitization test environment to carry out system test;Based on the digitization virtual machine created, provide digitization operating environment for the equipment software system to be tested;Based on the interface of the soft bus created, the communication data of the equipment software to be tested and test piece are encapsulated and are transferred through soft bus;Establish control mechanism, and based on the control mechanism established, the unified clock control of each test node in digitization test environment is carried out, control the business operation rate and communication transmission rate of each test node.The application can effectively solve the problem that physical test environment resource is limited, and it is difficult to guarantee the test sufficiency.

The application provides a model-driven algorithm test method, system, computer and storage medium, which comprises the following steps: normalizing non-standard algorithm requirements to generate a structured algorithm requirement document; based on the normalized algorithm requirement document, using a large language model to generate test data information meeting the test sufficiency requirement; parsing the algorithm source code structure corresponding to the algorithm requirement to construct a meta-model of the algorithm under test; loading the meta-model of the algorithm under test and importing the test data information to complete the conversion from the meta-model to an instantiated test model, forming a test case set; based on an extensible stylesheet transformation language template, batch converting the test case set into an executable test script; and executing the test script in an algorithm test engine. Through the model-driven test technology and the algorithm test engine, the automatic verification of the test case is completed and the test result is collected, thereby effectively improving the intelligentization and automation level of the algorithm test.

The application relates to a test sufficiency measurement method based on input feature diversity. Firstly, images in a training set are segmented and a pixel region set is generated; secondly, pixels in the set are clustered to select pixel features, important scores of all the pixel features are calculated, and an important feature set is formed; thirdly, important features of different test data are calculated and marked; then, the test set is artificially clustered according to the important features, a centroid set is calculated and generated; then, the distance between the centroids is calculated by using the Euclidean distance; finally, the centroid set is converted into a multi-dimensional space graph, the diversity of the test set based on distance entropy is calculated, and the sufficiency of the test set is effectively evaluated. The application aims at the evaluation problem of the sufficiency of deep learning software, through analyzing the feature diversity of the test set, the sufficiency and interpretability of software testing can be improved, and the method for guaranteeing the quality of deep learning software is provided.