7 results about "Path coverage" patented technology

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 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.

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.

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.