31 results about "Mutation testing" patented technology

The invention discloses a software test case generation method and system based on a large language model, and relates to the technical field of software testing, and the system comprises a to-be-tested module detection subsystem which is used for constructing a to-be-tested module set; the test case generation subsystem is used for constructing a prompt template according to the to-be-tested module and generating a test case by utilizing a program generation model based on an adaptive focus context generation mechanism and the prompt template; the correction verification subsystem is used for performing multi-stage correction processing on the test case, eliminating errors and generating an initial test suite; and the test case supplement subsystem is used for carrying out coverage rate analysis and variation test on the initial test suite, constructing a model input text according to a result, and generating a supplement test case by utilizing a program generation model so as to update the initial test suite and obtain a final test suite. By combining the functional annotation of the to-be-tested module with the logical reasoning ability of the LLM, misjudgment caused by assertion errors can be reduced, and the accuracy of test feedback is ensured.

The invention discloses a variation test method and device based on an intelligent automation script. The method comprises the following steps: extracting a statement execution sequence and a branch dependency relationship in an original source code by using a context-aware syntax tree, and generating a control flow diagram and a data flow diagram; performing code semantic analysis on the multi-dimensional code features by using a large language model, identifying potential defect types and variation rules, predicting the test efficiency of variants in combination with a deep Q learning model, and generating to-be-processed variants; performing grammar check and equivalence analysis on the to-be-processed variants to obtain effective variants, and testing the effective variants; variation test indexes are calculated, code weak points are positioned, and test blind areas are identified according to survival variants, so that a visual analysis report is generated; and iteratively generating a new test case according to the current survival variant and the visual analysis report, and testing the current survival variant by using the new test case. According to the invention, the variation test efficiency can be improved.

The invention discloses an API-oriented dynamic fuzz testing and vulnerability detection method, and belongs to the technical field of software security testing. The method comprises the following steps: extracting a dependency relationship, an input parameter, an output response and context state data of API calling, generating an initial API dependency graph, dynamically updating by capturing API state change in real time, forming an API state graph, and executing boundary-oriented variation based on parameter constraint characteristics, so as to obtain an API state graph; generating a variation test parameter, calling an API (Application Program Interface) of the variation test parameter to monitor a process memory behavior and response metadata, and generating a multi-dimensional abnormal signal; and performing mode matching on the abnormal signal and the vulnerability feature knowledge base, outputting a vulnerability type label and generating a path tracing report. According to the method, a context state sensing dynamic graph modeling technology is adopted, and a boundary-oriented intelligent variation strategy and multi-source abnormal behavior collaborative analysis are combined, so that precise vulnerability triggering, intelligent vulnerability judgment and complex scene coverage can be realized.

The invention relates to the technical field of research and development management, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an anomaly analysis method, device, equipment and medium based on a variation test. Generating a test case through the semantic perception model; selecting a variation strategy by using a state-driven model according to the service state, and generating a variation test case based on the access control strategy; executing the variation test case in a distributed environment, and collecting system resource index data and request execution monitoring data; and inputting the collected data into the abnormal prediction model, and outputting an abnormal behavior prediction result. Through combination of semantic perception generation, state-driven selection and anomaly prediction, coverage of a test case on protocol dependence and a service state is improved, and the accuracy of anomaly detection of the object storage system is remarkably improved.

The invention belongs to the field of software testing and defect detection, focuses on reliability verification of a physical simulation engine, is suitable for scenes depending on physical simulation, such as robot simulation, automatic driving testing, unmanned aerial vehicle control, education and scientific research, and particularly relates to a variation-based physical simulation engine defect detection method. Potential defects of core modules of collision detection, mechanical calculation, dynamics and the like are efficiently found through a variation test method. According to the method, a feature library is constructed through static library API analysis and AST / IR code analysis, test code expansion is performed by utilizing a large model, a test driver adaptive to a specific data type is generated, a physical semantic constraint mutation seed is combined, a test is executed through libFuzzer, defect and coverage rate data are analyzed, a use case selection strategy is optimized, and the test efficiency is improved. Defects of core modules such as collision detection and dynamic solution are efficiently detected, and the test pertinence and efficiency are improved.

The present invention discloses an optimization method for industrial control protocol fuzz testing based on coverage guidance. This method combines mutation testing on the basis of traditional coverage-guided fuzz testing. By comparing the output results of the program under test and the mutated PUT, high-quality test cases are screened. At the same time, the concept of coverage-guided tracking is introduced. By encoding the current coverage boundary in the binary file of the program under test, self-reporting can be performed when new coverage is generated in the test case, eliminating unnecessary tracking of the coverage-guided fuzzer, improving the fuzz testing efficiency, and greatly enhancing the vulnerability detection ability and testing efficiency of coverage-guided fuzz testing.

The invention discloses a multi-coverage-rate-guided voice intelligent model test data generation method and system. The method comprises the steps that a to-be-tested voice intelligent model and a seed test data set are input; the coverage rate of the voice intelligent model to be tested under different neural network coverage rate indexes is analyzed; randomly selecting one or more variation strategies to perform variation on the seed test data; defining a strong variation number constraint condition, preliminarily filtering variation data, calculating four neural network coverage rates of the voice intelligent model to be tested by using the filtered variation data, and selecting variation data of which the coverage rate of the model to be tested is improved compared with that before variation to form variation data sets corresponding to different neural network coverage rate criteria; and outputting a coverage rate calculation result of the to-be-tested voice intelligent model on the seed test data set and the selected variation test data set. The diversity of test data is improved, and the sufficiency of voice intelligent model test is ensured.

The invention discloses an industrial protocol fuzzy testing method and system based on Transform and particle swarm optimization, and belongs to the field of industrial control system safety testing. The method comprises the following steps: acquiring industrial protocol communication data and preprocessing the industrial protocol communication data; inputting the processed data into a Transformer model fusing relative position attention and frequency spectrum mixed attention to perform training and test case generation; adaptively adjusting the selection probability of a mutation operator by using a particle swarm optimization algorithm, and performing intelligent mutation on the generated test case; and sending the variation test case to the industrial control equipment for execution, monitoring the response state of the equipment in real time, and dynamically optimizing the variation strategy according to a feedback result to realize closed-loop test. According to the method, the compliant and efficient test case can be automatically generated, and the coverage rate and the detection efficiency of industrial protocol vulnerability mining are remarkably improved.

The present invention relates to a method, device, electronic device and medium for detecting Web cache poisoning vulnerabilities. The method comprises: obtaining a cache key of a Web cache server; generating a standard request to be verified based on the cache key of the Web cache server, and performing multiple mutation tests on the standard request to be verified to obtain multiple mutated attack requests; verifying the multiple mutated attack requests to obtain a verification result, and judging the verification result based on a preset authentication condition. If the verification result meets the preset authentication condition, the attack request is judged to be an exploitable poisoned request. Thus, by generating cache poisoning attack test cases covering various forms for different parts of the request, various types of cache poisoning attack vulnerabilities in the URL can be comprehensively and systematically detected, avoiding multiple potential cache poisoning attacks and new cache poisoning attacks, and helping security personnel to better detect and protect against cache poisoning attacks on their own websites.

The present invention discloses a method for testing software for intelligent equipment that combines a convolutional neural network with a genetic algorithm, aiming to transform the mutation test problem into a traditional coverage path test problem and generate effective test cases. First, the coverage difficulty of the mutation branch is calculated, a correlation matrix is constructed and a mutation branch correlation graph is generated, and then an executable path set is generated; then a mathematical model for generating multi-task test cases is constructed for multiple paths; then a convolutional neural network model based on incremental learning is constructed, and high-fitness individuals are predicted as the initial population of a multi-population genetic method, and finally, with the help of a multi-population genetic method enhanced by an incremental learning convolutional neural network, test cases with defect detection capabilities are efficiently generated. The present invention generates test cases with high defect detection capabilities, effectively improving the efficiency and quality of intelligent equipment software testing.

In mutation testing, source code is mutated at various positions, and test suites are run against the original object code and each version of the mutated object code, to determine the quality of test suites against arbitrary changes in the object code. The present disclosure provides a mutation test manager configured to initialize multiple computing threads configuring a computing host to perform parallel computation; mutate class files within context of each computing thread; recompile mutated class files independently in each respective computing thread to generate heterogeneous mutants; and execute pending unit tests against heterogeneous mutants independently in each respective computing thread. Consequently, the mutation testing process is decoupled from computational bottlenecks which would result from linear, sequential generation, compilation, and testing of each mutation, especially in the context of JVM® programming languages configured to generate class-rich object code.

The invention is applicable to the technical field of software testing, and provides a mutation testing method based on a co-location coverage relationship, which comprises the following steps of: applying a mutation operator to a variable original statement of a tested program to generate a mutation statement, and constructing a corresponding mutation branch, on the premise of keeping the semantics of the original program unchanged, all variation branches are integrated to obtain a new tested program, then the tested program is executed by using a test case set, the coverage information of the test case on the original statement and the variation branches is collected, and whether a co-location coverage relationship exists between the original statement and the variation branches is judged according to the coverage information; on the basis of the coverage relation, it can be directly determined that a test case covers a variable original statement and also covers a variable branch of the variable original statement, and for variants which cannot be judged according to the in-situ coverage relation, weak variation testing is executed, and a variation score is calculated in combination with the actual killing condition. According to the method, the variation test execution amount is remarkably reduced, the test efficiency is improved, the resource consumption is reduced, and meanwhile, high variation scores and test quality are kept.

The application provides a code testing method, a code testing device and a computer storage medium. The code testing method comprises the following steps: performing code mutation testing on a to-be-tested code to generate a target test case; mapping an initial compilation command maintained by a target test framework based on a compiler command mapping rule to obtain a target compilation instruction; calling a corresponding compiler based on the target compilation instruction to compile the to-be-tested code and the target test case to obtain a target execution file; and executing the target execution file in a virtual hardware model integrated in the target test framework and generating a test report corresponding to the to-be-tested code based on an execution result. The code testing efficiency is improved by the code testing method.

The present invention relates to the field of software testing, and in particular to a mutation operator simplification method and system for a deep learning model. The present invention uses each mutation operator to mutate the original model, obtains the hidden state sequence of each test sample in the original model and each variant during the training process, and after dimensionality reduction of each hidden state vector, clusters all the reduced hidden state vectors, regards each cluster cluster as an abstract state, and obtains the abstract state trace of each test sample in the corresponding model; for each original abstract state trace in the set of original abstract state traces, calculates the average value of the distance between it and each mutated abstract state trace in the set of mutated abstract state traces of each variant, obtains the mean difference between the abstract state traces of the original deep learning model and each variant, and determines the mutation operator to be simplified. The present invention reduces the execution cost of mutation testing and improves the effectiveness of evaluating the quality of image or text test case sets.

The invention discloses a software test method based on a knowledge graph and a neural network, and aims to convert a variation test problem into a traditional coverage path test problem according to the performability of variation branches and program paths and efficiently generate a test case with defect detection capability in a multi-task parallel mode. The method comprises the following steps: statically analyzing execution correlation between variation branches and program paths, and dividing the variation branches with the same execution paths into the same group; then, establishing a variation test case based on path coverage for the multiple groups of variation branches to generate a multi-task optimization model; and finally, solving the model by utilizing a multi-population genetic algorithm, and efficiently generating a test case with defect detection capability by adopting a multi-task parallel mode. According to the method, the variation branches are grouped according to the paths to which the variation branches belong, and a traditional mature path test method is adopted, so that the software test efficiency is improved, and the test case with high defect detection capability is generated.

The application discloses a deep learning framework mutation testing method based on developer experience, which adopts a mutation operator and a constraint condition generation model based on the professional knowledge of developers to simulate the common operation of developers in the development process and detect more diversified defects in multiple stages (such as model training and inference) of the life cycle of a deep learning model. The method comprises two parts: first, model mutation, which is used to generate mutants; second, defect detection, which is used to perform defect detection. The model mutation comprises the following steps: first, the application adopts two deep Q networks to select a mutation operator and a seed model; then, the seed model is mutated under the constraint of criteria, and the Q network is updated according to the reward calculated by the current target evaluation; then, it filters out the mutants that violate any constraint, and adds the legal mutants to the generated model pool. The process is iterated until the mutation is completed. The defect detection comprises the following steps: first, the application detects the defects exposed in the mutants and records the defect reports. Then, based on the reward, the application selects part of the models and further executes them to detect defects in resource scheduling, execution crash, model performance and output precision. Through the method of the application, meaningless models in defect detection can be filtered, diversified defects can be detected in the execution stage of the model, the effectiveness of defect detection is improved, and the method has important application value and popularization prospect.

The invention discloses an automatic vulnerability mining method and device based on a three-dimensional dobby machine. The automatic vulnerability mining method comprises the following steps that the number of a test case set and the number of file buckets are obtained; determining the maximum value and the average value of the sizes of the test cases in the test case set; determining an interval corresponding to each file bucket according to the number of the file buckets and the maximum value and the average value of the sizes of the test cases in the test case set; determining a file bucket corresponding to each test case according to the size of each test case in the test case set; adopting a Thompson sampling algorithm to determine the optimal arms of a stacking frequency Huger machine and a mutation operator Huger machine in each multi-arm Huger machine model; performing variation operation of stacking times corresponding to the optimal arm of the tiger machine on the test case in the file bucket to obtain a variation test case; and inputting the variation test case into the to-be-tested program for running. The fuzzy testing efficiency can be improved, and the vulnerability mining capability is improved.

This invention discloses a method and apparatus for testing the defense capabilities of a Web Application Firewall (WAF) based on protocol parsing differences, relating to the field of network security technology. The method constructs protocol mutation verification test cases, generates mutation test messages containing non-standard protocol features and verification payloads, and sends them to the backend via both the test link and the baseline link. By comparing the response fingerprint with the expected result, it accurately determines whether the WAF is in a state of successful interception, bypassed, or repaired and bypassed. This invention enables a comprehensive and automated evaluation of the WAF's protocol-layer defense capabilities.

The invention discloses an implementation method of a variation test framework for a distributed system, which comprises the following steps of: selecting a variation operator required by variation test from a variation operator library after a tester selects a system to be tested; then, the variation test framework generates variants on the basis of a source code of a system to be tested on the basis of the selected variation operator, and some invalid variants or equivalent variants are screened out through a method of combining static analysis and dynamic execution; and then entering a variant execution stage, sequentially loading a certain variant by a variant test framework according to coverage rate information of unit test of the system to be tested and a certain priority, running a test suite of the system to be tested on the system to be tested after variation, and collecting a result. And finally, after all variants are loaded and run, the variation test framework collects and arranges data to form a readable test report, so that the quality of the test suite of the to-be-tested system is reflected to a certain extent.

An apparatus to facilitate accelerating mutation testing for simulation and formal verification is disclosed. The apparatus includes processing circuitry to define a set of mutations and a set of verification properties for an original design under test (DUT); utilize a conditional input bit to conditionally activate the set of mutations in the original DUT; generate a conditional DUT comprising the original DUT having the conditional input bit applied to the set of mutations; for each verification property, inspect a query associated with the verification property and the conditional DUT to determine whether the verification property has at least one mutation of the set of mutations that has a corresponding conditional input bit that is present; and run a verification sign-off tool that executes the verification properties that are determined to have the at least one mutation of the set of mutations with the corresponding conditional input bit that is present.

The invention provides an Ethernet / IP protocol-oriented fuzzy test method and related equipment. The fuzzy test method comprises the following steps: acquiring flow data of a tested Ethernet / IP protocol stack to obtain initial input used for generating a test case; according to the format of the Ethernet / IP protocol, performing feature extraction on the protocol data packet to obtain a protocol feature sequence in a field format; according to the structure of the Ethernet / IP protocol and the protocol feature sequence of the field format, obtaining a predefined test case for fuzzy testing; performing variation treatment; the Ethernet / IP protocol stack to be tested is tested; determining whether a vulnerability is detected or not according to the feedback state, and when the vulnerability is not detected, determining whether the coverage rate of the variation test case is increased or not; and when the coverage rate of the variation test case is increased, returning to the step of performing variation processing on the predefined test case until it is determined that the vulnerability is detected.

The present application belongs to the field of information technology, and proposes a kernel fuzz testing case generation method and device based on a shuffling algorithm, comprising: randomly taking a kernel test case p from a corpus, applying a shuffling algorithm mathematical model to the kernel test case p to generate a fuzz testing case; generating m fuzz testing cases to generate m fuzz testing cases, …, to form a fuzz testing case set; performing chi-square test on the generated fuzz testing case set; judging the result of the chi-square test, starting a test environment with a to-be-tested kernel and a specified file system, and inputting the fuzz testing case set into the test environment for execution. The present application can disrupt the order of system calls in the test case, generate new fuzz testing cases, increase the diversity of the fuzz testing cases, avoid high dependence on the original corpus, and has small time complexity of the mutated test case, without increasing the generation time.

The invention belongs to the technical field of information, and provides a shuffling algorithm-based kernel fuzz test case generation method and device, and the method comprises the steps: randomly taking out a kernel test case p from a corpus, and carrying out the mutation of the kernel test case p through a shuffling algorithm shuffling algorithm mathematical model to generate a fuzz test case; generating m fuzzy test cases to generate m fuzzy test cases,..., and forming a fuzzy test case set; performing chi-square test on the generated fuzzy test case set; and judging a chi-square test result, starting a test environment by using the kernel to be tested and a specified file system, and inputting the fuzz test case set into the test environment for execution. According to the method, the system calling sequence in the test case can be disrupted, the new fuzzy test case can be generated, the diversity of the fuzzy test case is increased, high dependence on an original corpus is avoided, the time complexity of the variation test case is small, and the generation time is not increased.

The present invention discloses a multimodal-based automatic code generation and optimization system, which relates to the field of automatic programming technology. The system includes a task analysis module, which combines the CLIPS rule engine and knowledge graph, analyzes task descriptions and constraints in combination with raw data, identifies task objectives and requirements, and outputs a task risk assessment report and a task intent set; an optimization decision module, which uses a particle swarm optimization algorithm and a path planning algorithm to select the best modal data subset, optimizes and adjusts it according to task requirements, and outputs a code generation strategy; a test evaluation module, which uses mutation testing to evaluate and improve unit testing and integration testing, evaluates the quality and performance of the code through continuous integration and continuous delivery, and outputs a code evaluation report and optimization suggestions. The present invention dynamically screens the best modal data subset and optimizes the data transmission path, thereby reducing computing resource consumption while ensuring code performance and achieving the global optimization of the code generation strategy.

The application discloses a kind of based on intelligent automation script's variation test method and device.The method includes: using context-aware syntax tree extraction in original source code sentence execution order and branch dependency, generate control flow graph and data flow graph;With large language model, code semantic analysis is carried out to multi-dimensional code features, potential defect types and variation rules are identified, the test efficiency of mutant is predicted in combination with deep Q learning model, and the mutant to be processed is generated;Grammar check and equivalence analysis are carried out to the mutant to be processed, and effective mutant is obtained, to carry out test;Variation test index is calculated and weak point of code is positioned, and test blind area is identified according to surviving mutant, and thus visual analysis report is generated;According to the current surviving mutant and visual analysis report, new test case is generated iteratively, and the current surviving mutant is tested using new test case.The application can improve the efficiency of variation test.

The embodiment of the invention provides a unit test analysis method and device, equipment and a storage medium, relates to the technical field of software testing, and obtains a test report through optimized assertion verification and variation testing to determine the effectiveness of unit test codes. The method comprises the following steps: determining a unit test code corresponding to a tested code needing to be subjected to unit test in a code file in the aviation business field; the unit test code is used for testing whether the tested code is valid; performing assertion logic test on the unit test code; under the condition that the unit test code passes the assertion logic test, modifying a logic statement of the tested code according to the mutation operator library, and determining a mutation copy of the tested code; performing variation test on the unit test code based on the variation copy; based on the variation test and the assertion logic test, determining an effectiveness evaluation result of the unit test code; and outputting a unit test analysis report based on the effectiveness evaluation result of each unit test code.

A method for assessing test adequacy of deep neural networks based on element decomposition is provided. The network testing is divided into black box testing and white box testing, of which key elements are decomposed and defined. Network parameters including a weight matrix and a bias vector are extracted. Importance values of neurons in individual layers of the deep neural network are calculated and clustered, and an importance value hot map of neurons in each layer is generated based on clustering results. Mutation testing, and index calculation and evaluation are performed.

The invention discloses an intelligent equipment software testing method by combining a convolutional neural network with a genetic algorithm, and aims to convert a variation test problem into a traditional coverage path test problem and generate an effective test case. Firstly, variation branch coverage difficulty is calculated, a relevancy matrix is constructed, a variation branch correlation graph is generated, and then an executable path set is generated; constructing a multi-task test case aiming at multiple paths to generate a mathematical model; then, a convolutional neural network model based on incremental learning is constructed, then high-fitness individuals are predicted to serve as an initial population of the multi-population genetic method, and finally, by means of the multi-population genetic method enhanced based on the convolutional neural network based on incremental learning, test cases with defect detection capacity are efficiently generated. According to the method, the test case with high defect detection capability is generated, and the software test efficiency and quality of the intelligent equipment are effectively improved.

In mutation testing, source code is mutated at various positions, and test suites are run against the original object code and each version of the mutated object code, to determine the quality of test suites against arbitrary changes in the object code. The present disclosure provides a mutation test manager configured to initialize multiple computing threads configuring a computing host to perform parallel computation; mutate class files within context of each computing thread; recompile mutated class files independently in each respective computing thread to generate heterogeneous mutants; and execute pending unit tests against heterogeneous mutants independently in each respective computing thread. Consequently, the mutation testing process is decoupled from computational bottlenecks which would result from linear, sequential generation, compilation, and testing of each mutation, especially in the context of JVM® programming languages configured to generate class-rich object code.

The present invention discloses a method for sorting metamorphic test cases for deep learning and a computer-readable medium. The method of the present invention first divides the deep learning model under test into two parts. All metamorphic test case pairs are executed on the first part of the deep learning model under test to obtain outputs, and the neuron outputs obtained for each metamorphic test case pair are converted into distribution outputs. The diversity of each metamorphic test case pair is obtained by calculating the distribution difference between the two. The execution order of the entire set of metamorphic test case pairs is obtained by sorting according to the diversity of the metamorphic test case pairs. Then, according to the obtained execution order, the metamorphic test case pairs are executed on the second part of the deep learning model under test to obtain the outputs of the source test cases and the derived test cases, so that the metamorphic test case pairs that are more likely to trigger the wrong behavior of the model under test can be executed preferentially. The present invention improves the efficiency of testers in testing deep learning software.