17 results about "Mutation testing" patented technology

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 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.

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 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.

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.

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 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 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.

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.

A security detection method for reinforcement learning frameworks based on multi-level operator mutation includes: 1) Reinforcement learning source model training: using the interaction between the reinforcement learning agent and the environment to perform goal-oriented learning, obtaining an initial reinforcement learning agent model M. o ;2) For the original reinforcement learning agent model M o Targeting potential defects; designing mutation operators, and inserting mutation operators of different levels into the original reinforcement learning agent model M. o S3) Modify the test environment design in the initial reinforcement learning environment, and design a mutation score for the reinforcement learning system. The mutation score is the ratio of the number of eliminated mutants to the total number of mutants; S4) Modify the original reinforcement learning agent model M o And the mutation reinforcement learning agent model M M Game tests are conducted in different testing environments, and the generated mutation test scores are compared to determine whether the framework is a vulnerability of this type.