18 results about "Path coverage" patented technology

The invention discloses a logic replication decision-making method and device, equipment and a storage medium. Comprising the following steps: extracting key parameters of each copy unit, and calculating the revenue density of each copy unit according to the key parameters; generating a copy unit list based on the revenue density, and selecting candidate combinations from the copy unit list; and calculating a cost value of each candidate combination to determine an optimal copy unit combination. The key parameters of the copy unit are extracted through the script tool, the key parameters comprise the multiplexing frequency, the time sequence margin lifting amount and the resource overhead, and errors and low efficiency caused by manual extraction are avoided. The replication value is quantified through a revenue density function, a list is generated, then the area cost and the time sequence loss are fused, the comprehensive optimization cost of the candidate combinations is quantified, the dimension difference of different dimension indexes is eliminated, and the advantages and disadvantages of the candidate combinations have the comparable basis. The dynamic balance of the area and the time sequence is realized, the problem that the copy area is out of control or the critical path coverage is incomplete is avoided, and the logic copy optimization effect is guaranteed.

The invention discloses a deep learning auxiliary test case generation method for parallel program path coverage, and aims to improve the test efficiency of parallel program path coverage. The method comprises the following specific steps: (1) sampling a test input set conforming to uniform distribution characteristics, obtaining a parallel program execution path of each test input, and forming a representative training sample set; (2) training a deep learning model by using the representative training sample set, estimating a candidate test case of the target path by means of the model, and judging whether the candidate case can cover the target path or not; and (3) updating the deep learning model based on the candidate test cases not covering the target paths and the execution paths of the candidate test cases, and repeatedly iterating application and updating of the deep learning model until a test case set covering all the target paths is generated.

The invention relates to the technical field of information security, in particular to a sequential cipher security analysis method for a composite structure, which comprises the following steps of: acquiring round function parameters of a target cipher algorithm, nonlinear component definition and a key scheduling rule, and generating a standardized configuration data set; dividing the complete algorithm into a plurality of independent low wheel structures along the boundary of the nonlinear component according to the wheel function parameters, and constructing a directed graph data model containing state transition constraints to compress a state space; calling a password component vulnerability feature library to quantify a sub-structure active S box distribution abnormal value and a linear diffusion layer vulnerability path, and generating a risk weight priority ranking list; and inputting the state sequence data into the target application environment simulation platform, and generating a reproducible vulnerability report when the output deviation exceeds a security threshold. Through hierarchical compression, risk guidance and multi-environment verification, the technical defect of insufficient path coverage of a high-round-number lightweight algorithm in a resource-limited scene is solved.

The application discloses a path coverage test method based on agent-assisted variable multi-objective optimization, aiming at improving the effectiveness and efficiency of test case generation. The specific steps are as follows: (1) a variable multi-objective optimization model of the test case generation problem of the parallel program path coverage is established and updated, which is used for guiding the generation of the test case with high effectiveness; (2) after a certain number of iterations of solving the variable multi-objective optimization model based on an intelligent optimization algorithm, each difficult-to-cover target sub-path is determined, and a sample set is formed, laying a foundation for improving the test efficiency of the difficult-to-cover target sub-path; (3) based on the formed sample set, the agent model corresponding to each difficult-to-cover target sub-path is managed, and the superior evolution individual is selected, which is used for actually executing the parallel program, and the execution times and costs of the program are greatly reduced.

The invention discloses a local weighted linear regression assisted parallel program path coverage test method, and aims to improve the efficiency and effectiveness of test case generation. The method comprises the following specific steps: (1) generating a certain number of program inputs for executing parallel programs to obtain a target function value set, and defining sample input and output pairs to form a multi-output sample set; (2) calculating a Gaussian distance between a population evolution individual and sample input for estimating each-dimensional target function value component to obtain a target function estimation value; and (3) selecting a representative evolution individual for actually executing a parallel program, and judging whether an evolution individual of which the target function value component is 1 exists or not, namely, whether a test case covering a target path is generated or not.

This invention provides a fuzzy testing mutation scheduling optimization method based on the mayfly algorithm, belonging to the field of fuzzy testing. It includes: Step 1, constructing a lightweight mayfly population for each seed in the fuzzy testing process, where individuals in the population represent a probability distribution of a set of mutation operators; Step 2, selecting mutation operators for each seed in the queue according to a uniform probability distribution, performing mutation, and simultaneously collecting path coverage and crash detection as feedback information, and calculating fitness scores through normalization and weighting; Step 3, simulating diverse behavioral patterns of the mayfly population based on individual fitness scores, dynamically updating the probability distribution of mutation operators, and optimizing the probability distribution; Step 4, finally applying the optimized probability distribution to the next round of seed mutation to generate more effective test cases, and repeatedly executing the above steps to achieve continuous optimization of the mutation scheduling strategy, improving fuzzy testing coverage and vulnerability discovery capabilities.

This invention relates to a symbolic execution vulnerability detection method based on the real runtime context of WebAssembly, belonging to the field of software technology. The method includes: 1) Real runtime context clustering and classification: based on multi-dimensional clustering dimensions and recursive similarity matching, massive amounts of real runtime states are classified and redundantly removed, retaining representative context samples; 2) Symbolic context generation: the representative clustered contexts are modeled and standardized using multi-type parameters to construct an initial symbolic context pool, providing input for symbolic execution; 3) Dynamic pruning of symbolic execution: based on path coverage-oriented context sorting and dynamic pruning strategies, symbolic execution and vulnerability detection are implemented on the WebAssembly binary code. This invention achieves accurate and efficient detection of WebAssembly program vulnerabilities by integrating the real runtime context of WebAssembly in a browser environment with a symbolic execution framework, solving the dual problems of distorted results caused by analyzing only WebAssembly code and low efficiency caused by analyzing the entire real context in traditional methods.

The application discloses a test case generation method based on reinforcement learning, which is used for solving the problems of strong manual dependence and low coverage rate of the existing state machine graph test case generation method. The method comprises the following steps: obtaining a state machine graph of a system to be tested, extracting states, transition conditions and assignment operations, and parsing a test path into an operation node queue; assigning a wide value interval to an initial input variable; constructing a reinforcement learning environment model based on dynamic promotion, encoding node progress, variable interval and constraint into a state space, and narrowing the interval boundary into an action space; training an intelligent agent by using deep reinforcement learning, dynamically narrowing the variable interval in node promotion, and obtaining an optimal narrowing strategy; and solving a new test path by using the trained intelligent agent, and outputting a variable feasible region and a representative test case satisfying the constraints of the whole path. The application can be widely applied to software system testing of state machine modeling, and improves path coverage rate and test case generation efficiency.

The invention belongs to the field of taint path searching, and particularly relates to a taint path searching method, system and device and a storage medium, and the method comprises the steps: marking all Source basic blocks and Sink basic blocks according to a marking rule, and generating a Source point list; creating a search instance, marking a Source point variable as a preset state, adding a hash value of the Source point variable into a path hash chain, analyzing an instruction in a basic block to extract a variable identifier for pollution state analysis, and performing path splitting according to an updated dynamic hash table and a global variable pollution abstract table; according to the global variable pollution abstract table and the path hash chain, performing search optimization on the search instance and the split instance to obtain an optimized instance; and verifying the optimization instance to obtain a taint path search result. The method has the effects of effectively reducing the space-time complexity of the path space and providing a feasible optimized path for static taint analysis on the premise of ensuring core vulnerability path coverage.

The invention discloses a test case generation method and device, equipment and a computer storage medium. The method comprises the steps of performing static analysis on a to-be-tested program to obtain program structure information; executing an initial test based on the program structure information, and collecting initial coverage information; selecting a target path from the to-be-covered path set based on a dynamic evaluation mechanism and the initial coverage information; performing symbolization processing on the target path, generating a path constraint and performing constraint solution; when constraint solving succeeds, generating a test case covering the target path; and when the constraint solution fails, generating a guide signal based on the unsuccessfully solved part, and executing a guide type search based on the guide signal to generate a test case covering the target path. According to the method, the problems that complex branches are difficult to cover due to low random test efficiency, symbolic execution path explosion and constraint solution are difficult, a search technology is easy to fall into local optimum, and initial exploration efficiency is low can be solved, and test case generation efficiency and path coverage capability are improved.

The application belongs to the field of taint path search, and particularly relates to a taint path search method, system, device and storage medium, the method comprising: according to a marking rule, marking all Source basic blocks and Sink basic blocks to generate a Source point list; creating a search instance, marking a Source point variable as a preset state, adding a hash value of the Source point variable to a path hash chain, analyzing a variable identifier in an instruction in a basic block to perform pollution state analysis, and performing path splitting according to an updated dynamic hash table and a global variable pollution abstract table; according to the global variable pollution abstract table and the path hash chain, performing search optimization on the search instance and the split instance to obtain an optimized instance; and verifying the optimized instance to obtain a taint path search result. The application can effectively reduce the space-time complexity of a path space under the premise of guaranteeing core vulnerability path coverage, and can provide a feasible optimized path effect for static taint analysis.

The application discloses a path coverage test data generation method and system based on a reinforcement learning selection strategy, and relates to the technical field of software testing. The method solves the problems of long calculation time, low generation efficiency, unstable exploration process and redundant test data in the existing test data generation of large-scale path coverage or complex programs. The method comprises the following steps: acquiring all executable paths and numbers of a program to be tested; defining the executable paths as agent states and data selection as agent actions, and all states forming a whole state space; generating multiple groups of data according to an initial state obtained by an agent from initial data, and generating new data when the whole state space is not covered; selecting the data by a reinforcement learning selection strategy combining Q learning and a greedy method, defining an action reward function for the path coverage, and retaining the data with the maximum reward value as the new test data; and continuously updating until the test data covers the whole state space. The application is applied to the field of software product testing.

The invention discloses a path semantic perception-based large language model test case generation method, which comprises the following steps of: analyzing a to-be-tested item by utilizing a static analysis technology, and constructing an item-level dependency call graph to extract code elements and dependency relationships thereof; obtaining a dependency entity of the function to be tested according to the project dependency graph, and analyzing an execution path of the dependency entity; generating semantic intention description of each path by utilizing a large language model based on a thinking chain path analysis method, and generating test cases consistent with path behaviors according to the semantic intention description; importing the generated test case into a test framework for execution, obtaining a path coverage report and identifying an uncovered path; and according to a coverage feedback result, generating a supplementary test case for the uncovered path by using a large language model, and realizing iterative optimization of the test case. According to the method, path-level refined test case generation can be realized, the test coverage rate and the generation accuracy are improved, manual intervention is reduced, and the efficiency and the intelligent level of automatic testing are 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.

The application discloses a test case generation method based on a genetic algorithm and local search of reinforcement learning, and comprises the following steps: performing static analysis on a program to be tested, and constructing a control flow graph and a data flow graph; enumerating executable paths, analyzing the dependency relationship between the paths and input variables, and constructing a path dimension matrix; randomly generating an initial population, and calculating fitness based on branch distance; performing selection, crossover and mutation operations of the genetic algorithm under the constraint of the path dimension matrix; selecting elite individuals, introducing local search of reinforcement learning, and adjusting variables on relevant dimensions through the local search of reinforcement learning to reduce branch distance; and outputting test cases covering target paths when a termination condition is met. The application identifies key variables through the path dimension matrix, focuses on a search space, finely adjusts elite individuals through local search of reinforcement learning, overcomes the problem of slow convergence in the later stage of the genetic algorithm, realizes the cooperation of global exploration and local optimization, and improves the efficiency and success rate of path coverage testing of a complex program.

The invention provides a method for detecting a Java deserialization vulnerability. Comprising the steps that Java program semantic information is extracted through static analysis, and a code attribute graph is constructed; processing field storage and loading statements by adopting an edge pruning optimization strategy, removing redundant data stream edges, and generating direct connection edges to simplify the topological structure of the call graph; dynamically evaluating pollution propagation weights of method parameters and return values based on a controllability analysis algorithm, and implementing path reachability dynamic pruning; a complete attack chain from a deserialization entrance to a danger method is quickly positioned through a bottom-up reverse traversal mechanism of a graph database. According to the method, the path explosion problem is inhibited through an edge pruning strategy, the call chain tracking precision is improved in combination with context sensitive analysis, the detection efficiency and the path coverage rate are balanced by utilizing controllable dynamic pruning, and the method is suitable for the network threat that serialized data is easy to steal, intercept and tamper in an unsafe network environment. A high-precision and low-false-alarm detection scheme is provided, and collaborative optimization of detection efficiency and reliability is realized.

The application relates to an industrial agent-based test case generation method and system, comprising the following steps: obtaining an industrial dataset and constructing a system dependency model; generating a path coverage test sequence through a first agent; generating a boundary value test dataset through a second agent; generating abnormal test scenario information through a third agent; and generating a test case set based on the path coverage test sequence, the boundary value test dataset and the abnormal test scenario information. In summary, the application realizes multi-dimensional coverage of an industrial control system test scenario, realizes automation of test case generation, improves generation efficiency and enhances the comprehensiveness of test coverage, especially the coverage of boundary conditions, abnormal scenarios and concurrent paths, reduces manual dependence, and improves the reliability and consistency of tests by constructing a system dependency model and using multi-agent collaborative analysis of process logic paths, boundary conditions and dependency relationships.

An improved interactive graphics tool including an algorithm and technique to efficiently and elegantly render smooth lines to build or develop interactive graphics is disclosed. The new algorithm is configured to render an anti-aliased coverage mask for any path, using a single GPU shader program and a single GPU draw call. The present invention is a solution to prior two-dimensional graphics, by providing an automated process to stroke the entire path with tessellation, by rendering to a floating point “coverage count” buffer to tessellate an antialiasing with triangles running orthogonally from the center.