64 results about "Path coverage" patented technology

The invention particularly relates to a unit test case rapid generation method based on an AI large model. According to the unit test case rapid generation method based on the AI large model, structure information is extracted through a static AST, an execution path is tracked through dynamic instrumentation, and code analysis is achieved; an analysis result is converted into cue words through AI, a multi-scene use case is generated through reasoning, and the multi-scene use case is matched with a language framework; counting row / branch / path coverage, identifying gaps and optimizing loop logic; hot spots and paths are displayed through a visual report, IDE one-key repair and manual correction of a feedback model are supported, intelligent retrieval and reuse of historical cases are realized by relying on FAISS, and the generation accuracy is continuously improved. According to the unit test case rapid generation method based on the AI large model, high-coverage-rate test case generation is achieved through the AI large model, the test case generation efficiency is remarkably improved, the test period is shortened, the software quality is improved, the workload of manually compiling and maintaining the test case is reduced, and the development cost is reduced.

The invention relates to a test case generation method and system based on an industrial agent. The method comprises the steps that an industrial data set is acquired, and a system dependency model is constructed; generating a path coverage test sequence through the first agent; generating a boundary value test data set through the second agent; generating abnormal test scene information through a third agent; and generating a test case set based on the path coverage test sequence, the boundary value test data set and the abnormal test scene information. In conclusion, by constructing the system dependency model and utilizing the multi-agent collaborative analysis process logic path, boundary condition and dependency relationship, the multi-surface coverage of the industrial control system test scene is realized, the automation of the test case generation is realized, the effects of improving the generation efficiency and enhancing the comprehensiveness of the test coverage are achieved, and the method is suitable for industrial control system test. Especially for the coverage of boundary conditions, abnormal scenes and concurrent paths, the manual dependence is reduced, and the reliability and consistency of the test are improved.

The invention discloses a software source code security vulnerability detection method based on an artificial intelligence large model, and belongs to the technical field of information security and source code detection, and the method comprises the following steps: carrying out user registration and login, uploading a file, judging whether static analysis is carried out or not, judging whether dynamic analysis is carried out or not, introducing the AI large model, and generating a detection report; according to the method, through multi-level technology integration and a self-evolution mechanism, normal form innovation of vulnerability detection from a single view angle to global perspective is realized. According to the system, on the basis of unified intermediate representation, a closed-loop architecture of static rule screening, dynamic behavior verification and AI semantic reasoning is constructed, and the core contradiction of high false alarm rate, insufficient path coverage and semantic understanding deficiency of a traditional method is effectively solved.

The invention belongs to the technical field of target area information collection, and particularly relates to a heterogeneous agent path planning method. The path planning method comprises the following steps of S1, performing gridding decomposition on a target area, and establishing an adjacency relation of grids; s2, deriving an alternative path scheme through an adjacent search strategy of probability weighted random selection, the alternative path scheme comprising an alternative path of each agent, and the alternative paths of all agents covering a target area; and S3, carrying out dynamic reconstruction on the alternative path scheme by adopting a local dynamic reconstruction algorithm, and updating the alternative path scheme based on a dynamic reconstruction result. According to the method, efficient matching of the intelligent agent and the task is achieved through the adjacency search strategy of probability weighted random selection, the alternative path is dynamically reconstructed to shorten the time cost, and the problems that traditional path planning is extensive in resource allocation, low in randomness efficiency and inflexible in optimization are solved.

The invention discloses an optimized node selection method based on a BGP-iSec protocol partial deployment scene, which comprises the following steps of: in a first stage, calculating three static topological characteristics of nodes according to a key node selection algorithm, weighting the three static topological characteristics and AS node levels in proportion, calculating a mixed weight of the nodes, and selecting the three static topological characteristics according to the mixed weight; preliminarily determining candidate nodes; and in the second stage, a greedy algorithm is adopted, the node with the maximum gain for the current path coverage rate is gradually selected from the candidate nodes according to the priority and added into the to-be-deployed set until the path coverage rate is stable or the number of the nodes reaches the specified deployment cost. According to the method, the key AS node selection strategy is formulated, so that the defense effect of the path manipulation / prefix hijacking attack is optimized under the limited deployment cost.

The invention relates to the technical field of software engineering, and discloses a path coverage test data generation method based on an SA-BKA hybrid optimization mechanism, and the method comprises the steps: generating a control flow diagram through a program, recognizing each decision node through the control flow diagram, and deducing a set of all possible execution path sets as a target path set according to the decision nodes; the paths are grouped through path similarity, different optimization strategies are selected for the paths with different similarities, a simulated annealing algorithm is adopted for the groups with high path similarity, and an adaptive disturbance strategy is introduced, so that the algorithm diversity is enhanced; for low-similarity groups, a BKA algorithm is adopted, Levy flight and a dynamic step length adjustment strategy are fused, and meanwhile balance of the search process is achieved through dynamic step length adjustment. Compared with the prior art, the method has higher search efficiency under the condition of different population scales, and verifies the validity in path coverage test data generation.

The invention provides an unreachable code identification method and device, electronic equipment and a storage medium, and is suitable for the fields of financial science and technology, medical service and software code detection. The method comprises the steps of obtaining a target business code file; performing grammar analysis on the target service code file to obtain a target grammar structure tree; by traversing the target grammar structure tree, identifying an unreachable code segment on a static level as a first candidate unreachable code segment; running the business code file in a preset running scene, obtaining an effective code path in the running process in real time, and determining a code segment which is not covered by the effective code path in the target business code file as a second candidate unreachable code segment; and performing comparative analysis on the first candidate unreachable code segment and the second candidate unreachable code segment, and determining a target unreachable code segment in combination with a preset unreachable judgment rule. According to the invention, the accuracy and the recognition rate of unreachable code recognition can be improved.

The invention discloses a test case automatic generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-tested program; analyzing the to-be-tested program to generate an initial population; determining a fitness function; performing iterative optimization on the initial population based on the fitness function to obtain an optimal individual; and taking the optimal individual as a target test case for the to-be-tested program. According to the technical scheme, the boundary value use case generated based on the program control flow diagram is fused with the candidate use case to generate the initial population, the coverage rate of critical paths in the population can be effectively increased, the proportion of redundant use cases can be effectively reduced, and under the same test target, the scale of the use case set is reduced, and the test execution time is shortened. The hierarchical fitness function design reduces the proportion of high-time-consumption operation cases and improves the test efficiency. All stages of operation of the genetic algorithm are combined with all stages of operation of the artificial bee colony algorithm, and algorithm efficiency is improved.

The invention relates to a UUV heterogeneous platform global task planning method and system based on full path coverage, and belongs to the technical field of underwater target exploration. According to the method, a three-dimensional environment is reduced to a two-dimensional grid plane, regions are divided according to the number of UUVs, real-time obstacle avoidance and bow-shaped path planning are realized in combination with an improved artificial potential field algorithm, and collaborative operation of A-type (detection disposal type) and B-type (detection-only type) heterogeneous UUVs is supported. The system has a dynamic scheduling mechanism, when the B-type UUV finds a target, the latest idle A-type UUV is scheduled for processing, and task duration and load balancing are optimized while full-area coverage is guaranteed. Simulation examples show that the method is high in planning efficiency, smooth in path, stable in total task time and suitable for detection tasks in complex underwater environments.

The invention relates to the technical field of software testing, and discloses an LLM and SMT solver-based test case generation method, which is characterized in that a search type test is taken as a core drive, when coverage stagnation occurs, an execution path is subjected to semantic analysis by means of an LLM, a logic expression for describing a target path condition is generated, and in consideration of the limitation of the LLM in logical reasoning, a test case is generated; the system further introduces an SMT solver to perform verification and reasoning on a logic expression output by the LLM, finally feasible test input is generated, and the test efficiency and the coverage rate are improved. According to the method, the efficiency and precision of path coverage are improved, good adaptability and universality are achieved, the method is suitable for various languages and complex program structures, and the intelligent level and engineering practical value of automatic testing are effectively improved. The problem of insufficient accuracy of a large language model in complex path constraint reasoning is effectively relieved.

The invention belongs to the technical field of software testing, is suitable for the medical field and the financial field, and discloses a service flow chart-based test path generation method, device and equipment and a medium, and the method comprises the steps: obtaining a service flow chart of a service scene, and extracting nodes of the service flow chart and path topology information between the nodes; on the basis of the service importance weight and the historical execution probability of each node, key test paths in the path topology information are identified, and priorities of the key test paths are sorted; constructing a path coverage rate prediction model based on the dynamically adjusted priority of the key path and the historical execution probability of the key path, and predicting the coverage rate of each key path in the business flow chart based on the constructed path coverage rate prediction model; and configuring a test case set based on the predicted coverage rate of each key path in the business flow chart, and dynamically adjusting the configured test case set based on a multi-objective optimization algorithm. According to the invention, the test efficiency of the business scene is improved.

The invention provides a mobile application GUI automatic testing method and system based on hierarchical reinforcement learning, and belongs to the technical field of mobile application testing. A real hardware device or a virtual machine is used for building a testing environment and configuring an automatic exploration framework, a to-be-tested application program is initialized, GUI layer information and control layer information are extracted respectively, and the GUI layer information and the control layer information are extracted respectively; the method comprises the following steps: acquiring a GUI layer and a control layer, converting the GUI layer and the control layer into a fine-grained state vector convenient for subsequent reinforcement learning processing, exploring the GUI layer and the control layer by using hierarchical reinforcement learning in combination with a reward function, processing interaction between the two layers of networks, and performing conversion by using an FSM when an exception occurs. Respectively obtaining a code coverage rate and a crash detection result by utilizing the code coverage rate detection framework and the log information of Appium; the problem that an existing mobile application GUI automatic testing method does not consider rough GUI information granularity and too single exploration level, and consequently exploration path coverage is not comprehensive is solved.

The invention provides a fuzzy test method for driving seed scheduling based on secondary hybrid clustering analysis. Comprising the following steps: step 1, loading and sorting seed files, generating a path coverage bitmap by using an afl-showmap tool, performing de-duplication, quantifying a seed execution path, and constructing a bitmap matrix; 2, performing initial seed classification according to the path similarity by using OPTICS clustering; if necessary, the classification of the noise seeds is further refined through K-Means clustering; and step 3, calculating a rare score according to a result so as to select the most representative seed, allocating appropriate energy to each seed, and dynamically updating a new seed so as to maximize path diversity.

The invention discloses a logic drive test case generation method based on a large language model, and belongs to the technical field of software testing. The method comprises the following steps: extracting key information from a code to be tested; based on the key information, a logic driving thinking chain prompt template is adopted to guide a large language model to generate a test case; running the test case and calculating a coverage rate; and judging whether the test case needs to be repaired or not based on the test case operation result and the coverage rate, if yes, repairing the test case by adopting a layered repairing strategy, and if not, outputting the test case. According to the method, a big language model is systematically guided to generate the test case through a logic driving thinking chain, fusion path coverage, condition combination coverage, boundary value analysis and an equivalence class division strategy, and the problems of path missing and incomplete condition combination when a traditional LLM generates the test case are thoroughly solved; and the test case is repaired by adopting a layered repair strategy, so that the manual intervention demand is obviously reduced.

The invention discloses a method and a device for updating a network attack path diagram, terminal equipment and a storage medium, and belongs to the field of information security, and the method is the method for updating the network attack path diagram. Therefore, more comprehensive path coverage is realized; and the potential target attack path is automatically generated according to the connection relationship between the threat node and each path node in the network attack path graph, so that the path searching efficiency is improved, and the query omission of the potential attack path can be reduced. Therefore, the updating efficiency of the network attack path diagram can be improved, the path related to the attack behavior can be updated more accurately and comprehensively, and delay and omission of manual troubleshooting are avoided. By implementing the invention, the problems of incomplete path coverage and low updating efficiency of the network attack path diagram in the prior art can be solved.

The invention discloses an intelligent question answering system and method based on generative AI, and belongs to the technical field of natural language processing. The system comprises a problem input interface, an extensible classification matching module (including a dynamic threshold decision tree and semantic extension verification), a strategic output engine, a strategy cooperation controller (a main-auxiliary strategy hard binding mechanism) and an output control layer. According to the method, through semantic feature analysis, adaptive classification mapping, strategy cooperation control and conflict solving processes, the problems of fuzzy classification, strategy cooperation deficiency and classification system stiffness in the prior art are solved. The innovation points comprise a dynamic threshold decision tree (reference value = alpha * (1-beta * ln (classification number)), alpha belongs to [0.48, 0.52], beta belongs to [0.08, 0.12]), a strategy collaboration mechanism bound with RBAC authority, and a JSON template classification expansion interface. Through 100,000-level real data tests, the key action missing rate is reduced by 29.8%, the executable path coverage rate is improved by 37.1%, and the method is suitable for multi-strategy collaborative scenes such as law compliance, supply chain management and medical diagnosis.

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 present invention relates to the field of software testing technology, and discloses a test case generation method based on LLM and SMT solver. With search-type testing as the core driver, when coverage stagnation occurs, the execution path is semantically analyzed with the help of LLM, and a logical expression describing the target path condition is generated. Taking into account the limitations of LLM in logical reasoning, the system further introduces an SMT solver to verify and reason the logical expression output by LLM, and finally generates feasible test inputs, thereby improving test efficiency and coverage. The entire process forms a closed-loop optimization process that coordinates semantic understanding and logical solving. This method not only improves the efficiency and accuracy of path coverage, but also has good adaptability and versatility. It is applicable to multiple languages ​​and complex program structures, effectively improving the intelligence level and engineering practical value of automated testing. It effectively alleviates the problem of insufficient accuracy of large language models in complex path constraint reasoning.

The invention discloses a near-end strategy enhanced ant colony optimization path coverage method, and belongs to the technical field of intelligent path planning. Core mechanisms such as a dynamic hybrid decision mechanism, a feedback pheromone updating strategy, a path redundancy suppression method and pheromone concentration double-threshold constraint are introduced, so that the overall coverage rate and convergence rate of the path are remarkably improved, repeated access behaviors are effectively suppressed, the robustness and the adaptivity of the algorithm are enhanced, and the method is suitable for large-scale popularization and application. Therefore, a high-quality planning path which is comprehensive in coverage, simple in path and rapid in response is provided for applications such as an autonomous mobile robot, an unmanned inspection system and intelligent cleaning equipment, and finally efficient and reliable full-path coverage autonomous operation is achieved.

The invention discloses a fuzz test seed generation method based on a diffusion generation model, and aims to solve the problem that a traditional seed selection strategy is often low in efficiency and unstable in effect during fuzz test. According to the method, valuable input files are collected through parallel fuzzy testing to serve as a training data set, and after data preprocessing, the input files are input into a diffusion generation model to learn data features, and diversified seed files are generated. The seed files can quickly trigger the path of the target application program, so that the efficiency and coverage rate of vulnerability detection are remarkably improved. Experiments show that the seed file newly generated by the model improves the branch coverage rate and the path coverage rate of the fuzz test, and the efficiency of the parallel fuzz test is improved to a certain extent.

The invention relates to the technical field of internet software development and testing, in particular to a testing device for internet software development, which comprises a network construction subsystem for forming a multi-layer logic chain network by calculating the difference degree between adjacent logic chains and identifying key logic nodes in combination with a dynamic threshold value; the path selection subsystem defines a transition probability of random sampling based on a multi-level logic chain network, and generates a plurality of test paths through Monte Carlo simulation; the weight of each path is determined by the number of logic nodes covered by the paths, and selecting a plurality of paths with the highest weight as preliminary test paths; the report generation subsystem constructs a positive example pair and a negative example pair in the multi-level logic chain network; optimizing the test path to enable the path to cover key nodes in the logic chain network; and performing weighted aggregation on the test paths in the multi-level logic chain network to generate a final test report. According to the invention, the automatic test path generation based on the actual operation characteristics of the software is realized.

The present invention proposes a path coverage method based on a retrieval enhancement mechanism. The method includes: obtaining a pre-collected data set, constructing a policy model based on the Decision Transformer architecture, regarding the robot as an agent, using the policy model to predict the action distribution of the agent to obtain the action distribution predicted by the policy model at the current moment, based on the pre-collected data set, sequentially processing the input trajectory through the FH mechanism and the pre-trained language model to obtain a set of key-value pairs, retrieving similar sub-trajectories from the set of key-value pairs based on the current input sub-trajectory to obtain a set of retrieved similar sub-trajectories. By introducing an external memory bank and adding a similar trajectory retrieval mechanism, the present invention can quickly construct a path planning strategy in the initial stage of training the model, effectively reduce the effect of redundant coverage path planning, significantly reduce the computational resources consumed in training, and improve the decision-making ability of the model in a sparse reward environment.

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.

The invention provides a path coverage method based on a retrieval enhancement mechanism, and the method comprises the steps: obtaining a pre-collected data set, constructing a strategy model based on a Decision Transform architecture, taking a robot as an intelligent agent, predicting the motion distribution of the intelligent agent through the strategy model, obtaining the predicted motion distribution of the strategy model at the current moment, and obtaining a path coverage result of the robot. The method comprises the following steps: sequentially processing an input track through an FH mechanism and a pre-training language model on the basis of a pre-collected data set to obtain a key value pair set, and retrieving similar sub-tracks from the key value pair set on the basis of a current input sub-track to obtain a retrieved similar sub-track set. According to the method, an external memory library is introduced, a similar trajectory retrieval mechanism is added, a path planning strategy is rapidly constructed at the initial stage of the training model, the effect of redundant coverage path planning is effectively reduced, computing resources consumed by training are obviously reduced, and the decision-making ability of the model in a sparse reward environment is improved.

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 invention discloses a software testing technology platform based on big data interconnection, and belongs to the technical field of software testing, the software testing technology platform comprises an acquisition terminal, an operation terminal, an AI central control box and a testing terminal device, the acquisition terminal, the operation terminal, the AI central control box and the testing terminal device are in control connection through electric signals, the software testing comprises the following steps: S1, inputting a to-be-tested software module product; s2, a test module is adjusted, and multi-source data acquisition and interconnection are carried out; s3, test feature extraction and test case generation; s4, testing execution and monitoring (single item and mixing); and S5, generating a test result and analyzing the test result. Manual intervention is reduced through an automatic test and self-optimization strategy, edge scenes which are difficult to reach by a traditional method are covered through a data-driven test strategy, the test case generation time is reduced by more than 50%, and the defect detection rate is improved by 30%, so that the path coverage rate is improved while the efficiency is improved.

The invention discloses an automatic power generation control function automatic test method and system, and belongs to the field of power system active power automatic control. The method comprises the steps that the automatic power generation control system is deconstructed in a layered mode according to technical specifications and is divided into a data interface layer, a control algorithm layer and a data display layer, and service functions of all the layers are defined; a service special test is carried out, a unit test adopts a white box method, and judgment, condition and path coverage is realized according to a service function design document; integrating a black box technology for testing, verifying a service function interface and input and output; a joint test scheme is designed based on each level of service association, and different service combinations are tested; and finally, carrying out system-level integration test, abnormity anti-error strategy independent test, external system abnormity test and system destructive test in sequence, and if the test is not passed, carrying out regression test by taking the failure step as a starting point until all items pass. All modules of the automatic power generation control function are tested in all directions, and reliable operation of the automatic power generation function is guaranteed.