24 results about "Black-box testing" patented technology

The invention discloses a black-box test zero-day vulnerability analysis method and system based on multi-dimensional data, and aims to solve the problems that a traditional black-box test means is weak in unknown vulnerability recognition capability, high in false alarm rate, lack of path modeling and verification mechanisms and the like. The method comprises the following steps: constructing a cross-time window behavior graph by acquiring multi-dimensional heterogeneous information such as network input, system call, log information and abnormal signals; on the basis, potential abnormal paths are identified through structure entropy change and graph structure mutation analysis, path vector representation is constructed by combining graph representation learning and a path embedding method, and path-level risk modeling and mode clustering analysis are achieved; furthermore, vulnerability confirmation is carried out on the suspicious path through multiple verification mechanisms such as attack replay, fuzzy testing and sensitive function combination identification. The system has a multi-module cooperation capability, can realize automatic mining, verification and visual tracing of zero-day vulnerabilities in a source-source-free environment, and has good universality and expansibility.

The invention discloses a report system testing method and device and electronic equipment. The method relates to the field of big data, and comprises the following steps: inputting test data into a to-be-tested report system to obtain a first data processing result; comparing the first data processing result of the test data with the first standard data processing result to obtain a first test result; obtaining a sub-data processing result of the test data in each detection node in a preset processing path in the to-be-tested report system, and obtaining a second data processing result; obtaining a second standard data processing result set of the test data in the preset processing path, and determining a second test result of the test data according to the second standard data processing result set and the second data processing result; and determining a target test result of the to-be-tested report system according to the first test result and the second test result. Through the method and the device, the problem of relatively low efficiency of testing the report system by adopting a black box test method in related technologies is solved.

The invention discloses an AI-driven full-scene intelligent test platform which comprises the following steps: performing static scanning on a target code under the triggering of a code submission event through a code static scanning module to generate a scanning result, and then inputting the scanning result into a pre-trained first AI model to obtain code quality evaluation data. And generating a white box test case through a white box case generation module. In addition, a black box test case is generated through a black box case generation module. An automatic script generation module inputs the black box test case into a pre-trained fourth AI model to generate an automatic test script; and finally, running the white box test case and the automatic test script to obtain an original test report. And inputting the original test report into a pre-trained fifth AI model through a result analysis module, and generating defect distribution data and test suggestion data. The full-scene test coverage of the software life cycle is realized, the test of each link of the software is comprehensively and systematically covered, and the test efficiency is improved.

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

The application discloses an application testing method and device and electronic equipment, and relates to the field of data security. The method comprises the following steps: testing predetermined code based on a static demand rule to obtain a static demand test result; when the static demand test result is passed, testing the predetermined code based on a structure test rule to obtain a structure test result; when the structure test result is passed, matching the predetermined code based on a predetermined vulnerability library to obtain a vulnerability test result; when the vulnerability test result is passed, performing smoke testing on the predetermined code based on a smoke testing script to obtain a smoke test result; when the smoke test result is passed, testing the predetermined code based on a black-box test script to obtain a black-box test result; and determining a target test result of the predetermined application according to the black-box test result. The application solves the technical problem of low test efficiency in the prior art, in which the steps are complicated and mixed before the application is put online.

The invention belongs to the technical field of software testing, and discloses a black box testing method, device and equipment for in-vehicle software and a storage medium. The method comprises the following steps: acquiring a to-be-tested vehicle-mounted machine application; comparing the to-be-tested vehicle machine application with a historical vehicle machine application to generate a change method set; a preset method library is inquired according to method items in the change method set, test cases corresponding to the method items are screened out, and the test cases cover the method items during operation; and completing a regression test according to the test case. By means of the mode, under the condition that an executor does not need to carry out white box code analysis, the corresponding regression case is matched for the code change content, and the purposes of accurately classifying the test range and improving the version test efficiency are achieved.

The invention belongs to the technical field of network security, and particularly relates to a Web application firewall vulnerability detection method based on load variation injection. Comprising the following steps: constructing a unified attack grammar rule base covering multiple attack types; analyzing the original attack load into an abstract syntax tree; carrying out semantic reservation load variation based on the syntax tree to generate a semantic equivalent variation load; performing protection rule detection on the Web application firewall, analyzing a detection result and performing pruning operation on the syntax tree; a Monte Carlo tree search algorithm is utilized, a rule detection mechanism is combined, and a variation path with a high success rate is dynamically evaluated and preferentially tested so as to efficiently generate an antagonistic load capable of bypassing WAF detection; according to the method, the problems of attack failure, low black box test efficiency and insufficient coverage of a single attack type caused by semantic destruction in the prior art are solved, and the WAF rule base vulnerability can be systematically evaluated.

The application provides a method and system for switch testing using digital twin technology, including the following steps: using digital twin technology to construct a digital twin model of the switch, including a hardware model, a software model and a network topology structure model of the switch; based on the digital twin model, designing a function test scene of the switch, including a VLAN configuration, a cross-switch link aggregation and a multi-path scheduling function test scene; using an automated test tool to write an automated test script and inserting a test case into the automated test script; running the automated test script, performing black box testing on the digital twin model, and recording the execution result of the test case; comparing the execution result with an expected result, determining whether the test case passes, and for a test case that does not pass, performing error tracking and positioning, locating and repairing problems in the switch. Various different workloads and scenes are simulated in the digital model, thereby improving test coverage and test effect.

Disclosed is a system and method of black box testing a cybersecurity system. An attack chain or an element of the attack chain is decomposed into constituent primitives. Primitive codes for the primitives are generated by a generative artificial intelligence (AI) model. The primitive codes are assembled into a pseudo-malware. A malware scanning engine is black box tested against the pseudo-malware to determine whether the malware scanning engine can detect the pseudo-malware.

Training and / or utilization of machine learning model(s) (e.g., neural network model(s)) in automatically generating test case(s) for source code. Techniques disclosed herein can be utilized in generating test case(s) for unit test testing (or other white-box testing) and / or for functional testing (or other black-box testing).

The application relates to a DNS server security parameter black box testing method and system based on a double-end measurement mode. The method comprises the following steps: determining a to-be-tested parameter and modeling; registering a domain name and setting a test client and a test authority end; the test client initiates a test to a target resolver, triggers a domain name resolution behavior of the target resolver; the target resolver interacts with the test authority end, influences a state of the target resolver through a dynamic response of the test authority end, triggers a behavior of the target resolver, collects behavior information of the target resolver from the test client and the test authority end, and estimates a corresponding security parameter range of an actual behavior performance of the target resolver according to a to-be-tested parameter test model. The application can evaluate the target security in a simple and efficient manner by inferring the security parameter of the target resolver only through the communication behavior of the target resolver in the domain name resolution process, without additional information such as the source code or configuration file of the target resolver.

The invention discloses a compatibility test method and system for an artificial intelligence chip matched device, and relates to the field of artificial intelligence chip test.The method comprises the steps that firstly, original hardware scanning information is obtained through a hardware abstraction layer, and an equipment capacity mapping graph is constructed; and then forcibly executing state switching of the AI chip in a pressure injection process by generating a measurement vector set containing a frequency gradient. Then, by calculating an electrical qualitative change stability index, the complex physical layer signal drift is converted into a measurable stability scoring matrix. In the process, a dynamic mapping relation among power supply fluctuation, an error rate and a signal integrity parameter is established, and the defect that a traditional black box test lacks a linkage mechanism is overcome. And finally, a critical region with mismatched parameters is locked through failure boundary analysis, so that an accurate compatibility data support is provided for stable operation of the high-performance AI system under a dynamic load.

The invention relates to a black box code analysis and test case generation method and device and a storage medium, which are applied to the technical field of software testing, and the method comprises the following steps: transparentizing an originally invisible internal logic state by non-intrusively obtaining a variable change track and an execution path in a black box code; therefore, testers can accurately position intermediate logic errors, and the problem that the internal state cannot be observed in a traditional black box test is solved. And secondly, by systematically restoring the logic structure and the execution path of the code, the uncovered logic branches can be accurately identified, and the complementary test case can be intelligently and reversely deduced and generated, so that the defects that the traditional black box test depends on experience and is incomplete in coverage are overcome, and the test depth and efficiency are remarkably improved. And thirdly, a system tracking interface is adopted for monitoring, byte codes of target codes do not need to be modified, and the possible influence of an intrusive method on the program performance and stability is avoided.

The application discloses a kind of neural network-based Internet of Things equipment vulnerability mining method, it is related to Internet of Things equipment vulnerability detection technical field, including the following steps: step 1, collection Internet of Things equipment message sequence and pre-processing and annotation;Step 2, determine a message from all messages to be mutated for variation operation;Step 3, determine the variable segment of the message to be mutated, and execute multiple variation strategies;Step 4, fuzzy testing is carried out on Internet of Things equipment using the variable segment, and the feedback information of the fuzzy testing module is obtained by classifying the response of Internet of Things equipment.The application can generate more effective variable sequences through the variable sequence selection based on attention mechanism, and the device response classification based on improved clustering algorithm can reduce the number of mutations, and compared with several popular black-box testing tools, the number of mined vulnerabilities and the number of discovered program coverage paths are improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an automatic model compliance examination method and system based on black box test multi-scene coverage. According to the scheme, an input space is defined by constructing an intelligent sampling mechanism fusing business semantics, a key area is sampled in a high-density mode, a structured algorithm feature library containing a basic algorithm, a variety and an industry special template is established, and a model algorithm type is determined by adopting a double-track recognition mechanism fusing candidate screening, symbolic regression and AI analysis. And based on a reversible risk scoring model, the risk is quantified from the information retention degree, the mapping complexity and the confusion degree in a three-dimensional manner, and finally a structured compliance review report is generated. According to the method, the black box model deployed in the trusted execution environment can be subjected to automatic, quantifiable and traceable compliance review under the condition of no source code access, and the method is particularly suitable for the field of key information infrastructures with high compliance requirements such as electric power.

The invention discloses a black box testing method and system for an infrared image target detection algorithm, and belongs to the technical field of computer vision and algorithm testing. According to the method, the comprehensiveness, robustness and generalization of the detection algorithm are tested on the premise that the internal structure and code logic of the algorithm are not obtained. The method comprises the following steps: firstly, on the basis of an original data set with normal gray level, generating aspect data required by a test by using a condition coverage and metamorphic test metamorphic relation method and a mode of collecting other data sets; secondly, inputting the generated data set into an infrared image target detection algorithm, and drawing an ROC curve, a PR curve and a confusion matrix through an IoU value between an algorithm prediction frame and a real frame; then, according to a performance test result, evaluation indexes such as Precise, Recall and F1 values are calculated, the test performance of the algorithm on a corresponding data set is obtained, and the comprehensiveness, reliability, generalization and efficiency of the algorithm are verified through a test result.

The invention provides an interface test case generation method and system based on a dual-channel relational graph convolutional network model and a Bert language model, and aims to improve the recognition precision of an interface dependency relationship and the generation quality of a test script in a black box test. According to the method, an OpenAPI specification file is used as input, structured meta-information is extracted through data preprocessing, and a two-channel collaborative recognition mechanism is constructed: a structural channel adopts topological dependence between relational graph convolutional network modeling interfaces, and a semantic channel analyzes semantic association in natural language description of the interfaces based on a Chinese big language model; then, two channel results are fused through direction consistency verification and a multi-round screening strategy, and a directed dependency graph is generated; on the basis, a Python test script adaptive to the HttpRunner framework is automatically generated in combination with two strategies of data variation (such as missing fields and type errors) and scene path splicing (such as key path combination and random walk); and finally, evaluating a case result and outputting a test report by executing a feedback module.

The application relates to the technical field of vehicles, in particular to a method and device for detecting information security production consistency of a vehicle and electronic equipment, wherein the method comprises the following steps: receiving a target component injected with first key information, and performing black box testing on the target component to obtain a black box testing result; if the black box testing result meets first preset testing conditions, performing white box testing on the target component to generate a white box testing result of the target component; and if the white box testing result meets second preset conditions, generating security testing information of the target component. Thus, the method can verify whether a debugging port of a component is effectively closed, thereby guaranteeing the production consistency of the information security of the component.

The invention provides an automatic black box testing method and a related device, and relates to the technical field of software, and the method comprises the following steps: obtaining a test task text of a to-be-tested application; analyzing the test task text through a large language model to obtain a test scheme of the to-be-tested application, the test scheme being composed of a plurality of test steps, and each test step being described by a natural language; and sequentially executing each test step in the test scheme on the to-be-tested application in a visual perception mode, and verifying an execution result of each test step in the test scheme. According to the method, a rigid script is replaced by dynamic planning and understanding ability of a large language model, highly-intelligent automatic testing is achieved, in addition, dependence on a code selector is replaced by visual perception, the problem of vulnerability of a traditional script is solved, testing continuity is guaranteed, and black box testing efficiency and accuracy are improved.

The invention relates to the technical field of software testing, and provides a YOLO-based interface recognition and automatic testing method in a multi-system environment, which comprises the following steps of: performing recognition detection on a test software interface screenshot obtained through desktop screenshots by utilizing a pre-trained YOLO model; an RPA or ADB tool is adopted to realize automatic operation; performing a black box test, and verifying whether output results of the YOLO model under various inputs conform to expectation from the perspective of a user without paying attention to internal implementation details of a tool; carrying out white-box testing, and carrying out fine-grained testing on the YOLO model, the RPA operation script and the ADB tool command; and compiling a test script, and automatically executing identification and operation test. Through efficient and accurate recognition, powerful automatic operation, excellent real-time performance and a reliable exception handling mechanism, the software testing efficiency and accuracy are remarkably improved, and the method has wide application prospects and important technical value.

The application discloses a face privacy protection method based on convex optimization and general adversarial disturbance, and has the characteristics that the method comprises the following steps: 1) constructing a special disturbance of an identity k; 2) constructing a general disturbance of an overall data set; and 3) constructing a special mask. The method can construct a special mask for each identity, and the protection effect of the face privacy of each identity is better, and the migration on a black box test model is stronger.

The application provides a test case CBB design method, comprising: designing a data dictionary according to product specification, adopting a black box test design method according to a CBB idea to design a logic test case, referencing the data dictionary to test data, and realizing separation of application data and logic; a physical case generation method is provided, logic test cases are selected according to scene requirements, and sub-cases are obtained by respectively processing network scene, device type and test data by using different test coverage technologies; different test coverage technologies are used on the sub-cases to obtain final physical cases; a test evaluation method is provided, and the association information in the design method and the generation method is used for test evaluation. The application solves the problem of case normalization, and solves the problems of completing risk-based test execution within a limited test period and evaluating test execution results.

This application discloses an automated testing method, apparatus, electronic device, and storage medium for an outbound calling system. The method includes, before testing, constructing a role attribute pool for testing and determining a role correction model, a dialogue model, and a referee model; at the start of testing, generating an initial virtual customer role based on the role attribute pool; during testing, correcting the initial virtual customer role using the role correction model to obtain a complete virtual customer role, and generating dialogue records based on the dialogue model; at the end of testing, scoring the dialogue records according to the referee model and outputting the test results. This application enables black-box testing that highly simulates real users and achieves closed-loop intelligent evaluation.

This invention relates to a method, apparatus, and storage medium for black-box code analysis and test case generation, applied in the field of software testing technology. It includes: non-intrusive acquisition of variable change trajectories and execution paths within black-box code, making previously invisible internal logic states transparent, enabling testers to accurately locate intermediate logic errors, and solving the problem of traditional black-box testing's inability to observe internal states. Secondly, by systematically reconstructing the logical structure and execution path of the code, this application can accurately identify uncovered logical branches and intelligently reverse-engineer and generate supplementary test cases, overcoming the shortcomings of traditional black-box testing, such as reliance on experience and incomplete coverage, significantly improving the depth and efficiency of testing. Thirdly, this application uses a system tracing interface for monitoring, without modifying the target code's bytecode, avoiding the potential impact of intrusive methods on program performance and stability.