Testing device for Internet software development

Abstract

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.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet software development and testing, and in particular to a testing device for Internet software development. Background Art

[0002] In the field of Internet software development, testing is a key link in ensuring software quality, functional integrity, and system stability. With the rapid development of Internet technology, the complexity of software systems has grown exponentially, and applications involving multi-module interactions, distributed architectures, and high-concurrency scenarios are becoming increasingly common. However, existing testing methods and devices have shown obvious shortcomings when faced with these complex software environments. Traditional testing methods usually rely on manually written test cases or automated testing tools based on simple rules. These methods are incapable of handling nonlinear logical relationships, dynamic data flows, and multi-dimensional interactions. In addition, existing testing devices lack support for complex logical deduction and cannot effectively simulate various boundary conditions and abnormal situations in the real operating environment, resulting in low test coverage, high false positive rates, and even the omission of potential serious defects.

[0003] Prior art 1, a Chinese patent application number: 202311619736.2, discloses a system for managing the development and operation of Internet software. This system includes a software performance testing module, a software device performance evaluation module, a software network performance evaluation module, a software server processing capacity evaluation module, a software development and operation test qualification determination module, and a cloud database. While the system comprehensively considers the target software's device performance evaluation coefficient, network performance evaluation coefficient, and server processing capacity evaluation coefficient to determine whether the target software's development and operation test has passed, and combines diverse test scenarios with detailed test indicator analysis to achieve scientific and accurate testing of Internet software development and operation, providing more information and guidance for the development and testing of Internet software, ensuring that the software performs well under different conditions, thereby helping to improve the quality, reliability, and user satisfaction of Internet software, test determinations are based solely on static performance indicators such as equipment, network, and server performance, lacking analysis of the software's internal logical structure and the dynamic dependencies between modules, potentially leading to redundant test paths or insufficient coverage of key nodes.

[0004] Prior art two, Chinese patent, application number: 202210195299.5 discloses an Internet software development system, including a software development module, a network database module and a client. The software development module includes a software design module, a software testing module, a software modification module and a software trial operation module. The network database module includes a software pre-storage module, a software query module, a software download module and a software operation module. The client includes an information login module, an information screening module and a software download module. The software development module is connected to the network database and the backend database, and the client is connected to the Internet. The software developed by the software development module is uploaded to the network database module, and the client enters the network database module through the Internet to select and download the software. Although effectively integrating with the Internet can reduce the occurrence of errors in the development process, greatly improve the efficiency of software development, enhance the experience of software users, and reduce losses; however, the test module relies on a fixed process and does not combine the dynamic logical relationship during the actual operation of the software, which may cause the test scenario to be out of touch with the actual operating environment.

[0005] Prior art three, Chinese patent, application number: 202211168743.0 discloses a method for optimizing a continuous integration pipeline, which is used to optimize the existing continuous integration pipeline and solve the problems of high testing costs and low efficiency of manual debugging in the existing software development process. It includes: a method for predicting software defects using the historical change characteristics of the software warehouse; a method for locating defects based on historical code information; a method for locating defects using coverage information and method call graphs generated by testing. Although it is possible to predict defects in the software development process to save testing costs, and to locate defects after they are exposed, thereby improving the efficiency of developers in repairing software defects and improving the continuous integration process to a certain extent; however, defect prediction and location rely on historical data, and do not dynamically analyze the real-time running logic, which may lead to low efficiency in test path generation or omission of key defects.

[0006] Currently, existing technologies 1, 2, and 3 have problems such as static test paths, insufficient coverage of key logic nodes, and disconnection between test scenarios and actual operating logic. Summary of the Invention

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] One aspect of the present invention provides a testing device for Internet software development, comprising:

[0009] The network construction subsystem is used to extract the calling relationships between functional modules by monitoring the function module call sequences during software runtime, and calculate the calling probability distribution of each module. It then uses the entropy weight method to calculate the dependency strength between modules and construct the initial logic chain based on the dependency strength. By calculating the difference between adjacent logic chains and combining it with dynamic thresholds, it identifies key logic nodes and forms a multi-level logic chain network.

[0010] The path selection subsystem is used to define randomly sampled transition probabilities based on a multi-level logic chain network and generate multiple test paths through Monte Carlo simulation. The weight of each path is determined by the number of logic nodes covered by the path, and the paths with the highest weights are selected as preliminary test paths.

[0011] The report generation subsystem is used to construct positive and negative example pairs in a multi-level logic chain network; optimize the test path through the loss function so that the path covers the key nodes in the logic chain network; and perform weighted aggregation on the test paths in the multi-level logic chain network to generate the final test report.

[0012] In an optional implementation, the network construction subsystem includes:

[0013] The operation monitoring and relationship extraction module is used to monitor the function module calling behavior in real time during software runtime, capture the calling order and frequency between function modules, and form a dynamic calling sequence. Based on the dynamic calling sequence, it analyzes the actual interaction pattern between function modules, quantifies the calling probability distribution of each function module, and reveals the actual operating rules within the monitoring software.

[0014] The dependency strength modeling module is used to calculate the dependency strength between functional modules using the entropy weight method using probability distribution, transforming abstract call relationships into quantifiable logical chains. Functional modules whose dependency strength meets the preset standard form a closely related logical chain, while those that do not meet the preset standard serve as auxiliary branches to build the initial logical chain framework.

[0015] The key node identification module is used to analyze the differences between adjacent logic chains and, combined with dynamic thresholds, screen out the core branch points of the monitoring software functions corresponding to the nodes with sudden changes in the differences in the initial logic chain framework, mark them as key logic nodes, and ultimately form a multi-level logic network consisting of a trunk chain and branch chains.

[0016] In an optional implementation, running the monitoring and relationship extraction module includes:

[0017] The behavior capture and pattern extraction submodule is used to continuously observe and record the activation order and interaction frequency between functional modules. Based on the activation order, the call sequence is obtained, and the call frequency is obtained from the interaction frequency, forming a complete time series record of the calls. Based on the time series record, the collaboration mode between functional modules is analyzed, the distribution of each functional module being triggered is calculated, and the basic characteristics of the software monitoring component operation are discovered;

[0018] The association relationship quantification and structure construction submodule is used to evaluate the association degree between functional modules using weight calculation after understanding the probability distribution of each functional module being triggered, and convert the collaborative relationship of functional modules into specific numerical indicators. Functional module combinations with an association degree exceeding the set standard are identified as strong associations, forming the core path of software operation; combinations with an association degree not exceeding the set standard are used as supplementary paths, preliminarily establishing a relationship network that reflects the initial logical chain framework of the monitoring software operation.

[0019] The core element positioning and network improvement sub-module is used to identify key turning points by comparing the differences between adjacent core paths, mark the locations of key turning points, and form a hierarchical operation network that reveals the actual operation rules within the monitoring software.

[0020] In an optional implementation, the dependency strength modeling module includes:

[0021] The probability distribution data preparation submodule is used to collect the probability distribution data of function module calls and extract the transition probability matrix between each function module. The transition probability matrix records the conditional probability of calling from any function module A to function module B, forming the basic data set for dependency strength calculation.

[0022] The information uncertainty measurement submodule is used to calculate the information entropy value of the call distribution for each functional module. The information entropy value reflects the degree of uncertainty of the flow to other modules when the functional module is called; according to the entropy value calculation results of each functional module, it is converted into a weight coefficient through normalization processing;

[0023] The dependency strength comprehensive calculation submodule is used to combine the transition probability matrix and the weight coefficient to calculate the comprehensive dependency strength index between functional modules.

[0024] In an optional implementation, the key node identification module includes:

[0025] The input data preparation submodule is used to extract the call frequency distribution and dependency strength distribution of adjacent logic chains from the dynamic call sequence. The following features of the two logic chains are compared: the difference in call probability distribution, the gradient change in dependency strength, and the difference in topological structure.

[0026] The dynamic threshold adjustment submodule is used to dynamically adjust the difference determination threshold according to the overall dependency strength distribution of the current logic chain set; modules whose difference exceeds the dynamic threshold are determined to be mutation nodes;

[0027] The key logic node generation submodule is used to associate the mutation node with the module role in the initial logic chain framework. If the mutation node belongs to multiple high-difference logic chains at the same time, it is determined to be a branch convergence point; if the dependence strength of the mutation node in a single logic chain drops sharply, it is determined to be an inter-chain switching point. The node constitutes the core branch point in the multi-level logic network, connecting the main chain and the auxiliary branch chain.

[0028] In an optional embodiment, the difference in the calling probability distribution of the input data preparation submodule is: comparing the calling probability offsets of the same functional module in the two chains;

[0029] Dependency intensity gradient change: Analyze the dependency intensity difference between the same module pairs in two chains. If the difference exceeds the dynamic threshold, it is marked as a potential difference point.

[0030] Topological structure difference: Check whether the overlapping modules of the two chains are in similar positions in their respective chains. If the position offset is significant, increase the difference weight.

[0031] In an optional implementation, the key logic node generation submodule includes:

[0032] The multi-chain cross-validation unit is used to take the set of mutation nodes obtained from the dynamic threshold adjustment submodule as input and trace back the adjacent logical chain association information recorded by the input data preparation submodule. For each mutation node, it retrieves all the logical chain identifiers to which it belongs and extracts the difference matrix between the logical chains, which contains the call probability offset, dependency strength difference, and topological position offset.

[0033] The single-chain strength decay analysis unit is used to switch to single-chain analysis mode for mutation nodes that have not passed multi-chain verification. The initial logical chain framework provided by the dependency strength modeling module is activated to locate the sequence position of the mutation node in the corresponding logical chain. When the following conditions are met at the same time, it is determined to be an inter-chain switching point:

[0034] The conflict resolution and final judgment unit is used to use the topological structure difference weight as the decision factor when the mutation node simultaneously meets some characteristics of the convergence point and the switching point. If the mutation node is in a structurally symmetrical position in the majority chain, the convergence point judgment is strengthened; if the position distribution is discrete and accompanied by drastic fluctuations in the call probability, the switching point judgment is strengthened.

[0035] In an optional implementation, when a mutation node appears in three or more logical chains and the difference indicators between the logical chains exceed the adjusted dynamic threshold, the convergence point judgment condition is triggered; at this time, the dependency strength change pattern of each chain at the mutation node is checked: if the probability of the node being called in each chain remains stable, and the dependency strength distribution in the output direction shows multi-path balance, then it is confirmed that the mutation node assumes the branch convergence function.

[0036] In an optional implementation, the path selection subsystem includes:

[0037] The logic association module is used to determine the transition probability based on the dependency strength and call probability in the multi-level logic chain network. Monte Carlo simulation generates paths based on the multi-level logic chain network structure. The weight calculation relies on the key node labels in the multi-level logic chain network to form a closed loop for test path optimization.

[0038] The transition probability definition module is used to define the random sampling probability of transition between functional modules in a multi-level logic chain network based on the function module call probability distribution and dependency strength;

[0039] The path simulation module is used to simulate the possible paths of the monitoring software through probabilistic sampling and generate several test paths. The weight of each test path is dynamically determined by the number of key nodes it covers, and the preliminary test path with the greatest impact on the core logic of the monitoring software is screened out.

[0040] In an optional implementation, the report generation subsystem includes:

[0041] The path expansion module is used to expand the preliminary test paths generated by the path selection subsystem. Positive pairs consist of path segments covering key logical nodes. The selection criteria combine the key node distribution characteristics identified by the network construction subsystem and the path weights calculated by the path selection subsystem. Negative pairs come from two situations: logical chain connection areas that do not reach the dynamic threshold, or low-weight path segments in Monte Carlo simulations.

[0042] In the dual-channel feedback module, the path features of the positive example pairs are extracted as multidimensional vectors, including dimensions such as key node coverage and adjacent node differences. The missing features of the negative example pairs serve as comparison samples. The optimization process iteratively adjusts the transition probability distribution of the test path, so that the positive example feature vector gradually approaches the topological centrality distribution of the logic chain network, while the negative example feature vector is pushed away from the effective coverage area.

[0043] The path optimization module is used to consider the node criticality scores determined by the network construction subsystem, the path weight coefficients assigned by the path selection subsystem, and the coverage gap data generated by the comparison of positive and negative examples. The optimized test paths will dynamically enhance the probability of accessing high-criticality nodes while maintaining the original weight distribution. The resulting path set retains the random characteristics of the Monte Carlo simulation.

[0044] The network construction subsystem of the present invention extracts the calling relationship between software functional modules through dynamic monitoring and quantifies the module dependency strength; adopts the entropy weight method to objectively evaluate the correlation between modules; identifies key logic nodes through difference analysis and dynamic thresholds; and constructs a multi-level logic chain network that reflects the actual operation logic of the software. The path selection subsystem defines a scientific transfer probability based on the multi-level logic chain network, uses the Monte Carlo method to efficiently generate candidate test paths, and realizes the preliminary screening of test paths through path weight evaluation to ensure the coverage of test paths for key logic nodes. The report generation subsystem uses the positive and negative case construction method to verify the test effectiveness, improves the test path quality through loss function optimization, and weighted aggregates the multi-path test results to generate a test report that comprehensively reflects the software logic coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0046] Figure 1 This is a block diagram of the test device for Internet software development provided in Example 1 of the present invention;

[0047] Figure 2 This is a block diagram of the network construction subsystem provided in Example 2 of the present invention;

[0048] Figure 3 This is a block diagram of the path selection subsystem provided in Example 3 of the present invention;

[0049] Figure 4 This is a block diagram of the report generation subsystem provided in Example 11 of the present invention;

[0050] Figure 5 A block diagram of the electronic device provided by the present invention;

[0051] Figure 6 Block diagram of the computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0053] In the following, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified with "first," "second," etc., may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0054] In the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integrated one; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. In addition, unless otherwise clearly specified and limited, the term "coupling" should be understood in a broad sense. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components, or it can be understood as the electrical connection between different components in a circuit structure through a physical line that can transmit electrical signals, such as printed circuit board (PCB) copper foil or wire, so as to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in an airless / non-contact manner, such as electrical connection between two components using capacitive coupling to transmit electrical signals.

[0055] In an embodiment of the present invention, directional terms such as "up", "down", "left" and "right" may be defined including but not limited to the orientation relative to the schematic placement of the components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly according to changes in the orientation of the components in the drawings.

[0056] Example 1:

[0057] like Figure 1 As shown, an embodiment of the present invention provides a testing device for Internet software development, comprising:

[0058] The network construction subsystem is used to extract the calling relationships between functional modules by monitoring the function module call sequences during software runtime, and calculate the calling probability distribution of each module. It then uses the entropy weight method to calculate the dependency strength between modules and construct the initial logic chain based on the dependency strength. By calculating the difference between adjacent logic chains and combining it with dynamic thresholds, it identifies key logic nodes and forms a multi-level logic chain network.

[0059] The path selection subsystem is used to define randomly sampled transition probabilities based on a multi-level logic chain network and generate multiple test paths through Monte Carlo simulation. The weight of each path is determined by the number of logic nodes covered by the path, and the paths with the highest weights are selected as preliminary test paths.

[0060] The report generation subsystem is used to construct positive and negative example pairs in a multi-level logic chain network; optimize the test path through the loss function so that the path covers the key nodes in the logic chain network; and perform weighted aggregation on the test paths in the multi-level logic chain network to generate the final test report.

[0061] In the above embodiment, the network construction subsystem of this embodiment extracts the calling relationship between software functional modules through dynamic monitoring and quantifies the module dependency strength; adopts the entropy weight method to objectively evaluate the correlation between modules; identifies key logic nodes through difference analysis and dynamic thresholds; and constructs a multi-level logic chain network that reflects the actual operation logic of the software. The path selection subsystem defines a scientific transition probability based on the multi-level logic chain network, uses the Monte Carlo method to efficiently generate candidate test paths, and implements preliminary screening of test paths through path weight evaluation to ensure the coverage of test paths for key logic nodes. The report generation subsystem uses the positive and negative case construction method to verify the effectiveness of the test, improves the quality of the test path through loss function optimization, and weighted aggregates the multi-path test results to generate a test report that comprehensively reflects the software logic coverage.

[0062] In summary, this embodiment realizes the automatic test path generation based on the actual operation characteristics of the software, ensures the test coverage of key nodes through multi-level logic network modeling, improves the test efficiency by combining probabilistic statistical methods, and finally outputs a test report with quantitative basis, providing a reliable test verification means for software development.

[0063] Example 2:

[0064] like Figure 2 As shown, based on Example 1, the network construction subsystem provided by the embodiment of the present invention includes:

[0065] The operation monitoring and relationship extraction module is used to monitor the function module calling behavior in real time during software runtime, capture the calling order and frequency between function modules, and form a dynamic calling sequence. Based on the dynamic calling sequence, it analyzes the actual interaction pattern between function modules, quantifies the calling probability distribution of each function module, and reveals the actual operating rules within the monitoring software.

[0066] The dependency strength modeling module is used to calculate the dependency strength between functional modules using the entropy weight method using probability distribution, transforming abstract call relationships into quantifiable logical chains. Functional modules whose dependency strength meets the preset standard form a closely related logical chain, while those that do not meet the preset standard serve as auxiliary branches to build the initial logical chain framework.

[0067] The key node identification module is used to analyze the differences between adjacent logic chains and, combined with dynamic thresholds, screen out the core branch points of the monitoring software functions corresponding to the nodes with sudden changes in the differences in the initial logic chain framework, mark them as key logic nodes, and ultimately form a multi-level logic network consisting of a trunk chain and branch chains.

[0068] In the above-mentioned embodiments, this embodiment achieves the mapping and optimization of dynamic software runtime behavior into a structured logic network. Specifically, the following are achieved: Dynamic behavior structuring: By capturing the real-time call sequence and frequency of functional modules, discrete runtime behavior is converted into a quantifiable call probability distribution, establishing a dynamic data model of the software's internal logic. Logical dependencies are made explicit: Based on the call probability distribution, the entropy weight method is used to convert implicit call relationships between modules into explicit dependency strength indicators, distinguishing core logic chains from auxiliary branches, constructing an initial logic chain framework, and making abstract logical relationships computable. Core node focus: Through difference analysis and dynamic threshold screening, key branch points with sudden changes in the logic chain are identified, forming a multi-level network consisting of a main chain (core logic) and branch chains (secondary logic), clarifying the core paths and potential risk points of software functions. Dependency strength calculation is driven by runtime monitoring data. Dependency strength determines the hierarchy of the logic chain. Difference and dynamic thresholds are used to refine key nodes, ultimately generating a logic network with both dynamic characteristics and structural clarity, providing a precise logical framework support for test path generation.

[0069] Example 3:

[0070] Based on Example 2, the operation monitoring and relationship extraction module provided in this embodiment of the present invention includes:

[0071] The behavior capture and pattern extraction submodule is used to continuously observe and record the activation order and interaction frequency between functional modules. Based on the activation order, the call sequence is obtained, and the call frequency is obtained from the interaction frequency, forming a complete time series record of the calls. Based on the time series record, the collaboration mode between functional modules is analyzed, the distribution of each functional module being triggered is calculated, and the basic characteristics of the software monitoring component operation are discovered;

[0072] The association relationship quantification and structure construction submodule is used to evaluate the association degree between functional modules using weight calculation after understanding the probability distribution of each functional module being triggered, and convert the collaborative relationship of functional modules into specific numerical indicators. Functional module combinations with an association degree exceeding the set standard are identified as strong associations, forming the core path of software operation; combinations with an association degree not exceeding the set standard are used as supplementary paths, preliminarily establishing a relationship network that reflects the initial logical chain framework of the monitoring software operation.

[0073] The core element positioning and network improvement sub-module is used to identify key turning points by comparing the differences between adjacent core paths, mark the locations of key turning points, and form a hierarchical operation network that reveals the actual operation rules within the monitoring software.

[0074] In the above embodiments, this embodiment realizes the complete disclosure and structured presentation of the operating rules of the monitoring software through a hierarchical and progressive analysis method. A complete runtime behavior recording system is established at the basic data acquisition level, which realizes the quantitative description of the interaction characteristics of functional modules. The relationship analysis level converts the fuzzy module collaboration relationship into quantifiable correlation indicators, and distinguishes the structural hierarchy of the core path and auxiliary path. The network construction level identifies the key decision nodes that affect the operation of the system, and forms a multi-level network model that reflects the actual operation characteristics. The final output operation network has the following characteristic values: it fully presents the functional topology of the monitoring software, clearly marks the core path and supplementary path, reveals the decision logic nodes within the system, and provides a visual analysis basis for operation optimization. It realizes the complete conversion from raw operation data to structured network model, and provides a systematic analysis framework for understanding the operation mechanism of the monitoring system.

[0075] Example 4:

[0076] Based on Example 2, the dependency strength modeling module provided in this embodiment of the present invention includes:

[0077] The probability distribution data preparation submodule is used to collect the probability distribution data of function module calls and extract the transition probability matrix between each function module. The transition probability matrix records the conditional probability of calling from any function module A to function module B, forming the basic data set for dependency strength calculation.

[0078] The information uncertainty measurement submodule is used to calculate the information entropy value of the call distribution for each functional module. The information entropy value reflects the degree of uncertainty in the flow of information to other modules when the functional module is called. The higher the entropy value, the more dispersed the call targets of the module; the lower the entropy value, the more concentrated the call targets. The entropy value calculation results of each functional module are converted into weight coefficients through normalization processing. Modules with high entropy values ​​are given smaller weights because their call behavior is more random, and modules with low entropy values ​​are given larger weights because their call behavior shows obvious regularity.

[0079] The dependency strength comprehensive calculation submodule is used to combine the transfer probability matrix and the weight coefficient to calculate the comprehensive dependency strength index between functional modules. For call relationships with high transfer probability and low entropy values ​​of modules at both ends, a higher dependency strength score will be obtained; for call relationships with low transfer probability or involving high entropy modules, a lower dependency strength score will be obtained.

[0080] In the above embodiment, this embodiment constructs an analytical framework for quantitatively evaluating the call relationship between system functional modules through the collaborative work of three sub-modules: probability distribution data preparation, information uncertainty measurement, and dependency strength comprehensive calculation. The probability distribution data preparation sub-module provides basic data support, accurately characterizes the call condition probability between functional modules through the transfer probability matrix, and lays a data foundation for analysis. The information uncertainty measurement sub-module introduces information entropy as the basis for weight adjustment, distinguishes the regularity and randomness of call behavior, ensures that when calculating the dependency strength, more attention is paid to the call relationship with high certainty, and reduces the interference of random calls on the overall analysis. The dependency strength comprehensive calculation sub-module combines the outputs of the first two, performs a weighted evaluation of the call relationship between modules, and screens out high-probability and stable dependency relationships, providing objective indicators for system architecture optimization, module coupling analysis, or fault impact domain assessment. It realizes the quantitative modeling of the dependency relationship between system functional modules, helps identify key call links, and assists system design, maintenance, or reconstruction decisions.

[0081] Example 5:

[0082] Based on Example 2, the key node identification module provided by this embodiment of the present invention includes:

[0083] The input data preparation submodule is used to extract the call frequency distribution and dependency strength distribution of adjacent logic chains from the dynamic call sequence. The following features of the two logic chains are compared: the difference in call probability distribution, the gradient change in dependency strength, and the difference in topological structure.

[0084] Call probability distribution difference: compare the call probability offset of the same functional module in the two chains;

[0085] Dependency strength gradient change: Analyze the difference in dependency strength between the same module pairs in two chains. If the difference exceeds a dynamic threshold (such as the ratio of the average dependency strength within the chain), it is marked as a potential difference point;

[0086] Topological structure difference: Check whether the overlapping modules of the two chains are in similar positions in their respective chains (such as both are start nodes or end nodes). If the position offset is significant, increase the difference weight;

[0087] The dynamic threshold adjustment submodule is used to dynamically adjust the difference determination threshold according to the overall dependency strength distribution of the current logic chain set; modules whose difference exceeds the dynamic threshold are determined to be mutation nodes;

[0088] If the dependency strength differences of most adjacent logic chains are concentrated in the low range, the threshold is lowered to capture subtle changes;

[0089] If the system is in a high-fluctuation operating state (the entropy value of the call probability distribution is high), the threshold is increased to avoid noise interference;

[0090] The key logic node generation submodule is used to associate the mutation node with the module role in the initial logic chain framework. If the mutation node belongs to multiple high-difference logic chains at the same time, it is determined to be a branch convergence point; if the dependence strength of the mutation node in a single logic chain drops sharply, it is determined to be an inter-chain switching point. The node constitutes the core branch point in the multi-level logic network, connecting the main chain and the auxiliary branch chain.

[0091] In the above embodiment, this embodiment constructs a logic network structuring method based on dynamic call behavior and dependency strength analysis through the synergistic effect of three submodules: input data preparation, dynamic threshold adjustment, and key logic node generation. The input data preparation submodule extracts the call probability distribution differences, dependency strength gradient changes, and topological structure differences of adjacent logic chains from the dynamic call sequence, providing basic data for quantitative comparison analysis. The dynamic threshold adjustment submodule adaptively adjusts the difference judgment standard based on the overall dependency strength distribution of the logic chain set, ensuring that mutation nodes can be effectively identified under different operating conditions (such as low volatility or high entropy environments), balancing sensitivity and noise resistance. The key logic node generation submodule associates mutation nodes with the module roles of the initial logic chain framework, distinguishes branch convergence points and inter-chain switching points, and ultimately forms a multi-level logic network structure containing a trunk chain and branch chains. This achieves differential analysis and key node positioning of the internal logic chain of the software, providing a structured basis for system architecture optimization, core function identification, or operation path analysis.

[0092] Example 6:

[0093] Based on Example 5, the key logical node generation submodule provided in this embodiment of the present invention includes:

[0094] The multi-chain cross-validation unit is used to take the set of mutation nodes obtained from the dynamic threshold adjustment submodule as input and trace back the adjacent logical chain association information recorded by the input data preparation submodule. For each mutation node, it retrieves all the logical chain identifiers to which it belongs and extracts the difference matrix between the logical chains, which contains the call probability offset, dependency strength difference, and topological position offset.

[0095] When a mutation node appears in three or more logical chains, and the difference indicators between the logical chains all exceed the adjusted dynamic threshold, the convergence point judgment condition is triggered. At this time, the dependency strength change pattern of each chain at the mutation node is checked. If the node's call probability in each chain remains stable, and the dependency strength distribution in the output direction shows multi-path balance, then the mutation node is confirmed to have assumed the branch convergence function.

[0096] The single-chain strength decay analysis unit is used to switch to single-chain analysis mode for mutation nodes that have not passed multi-chain verification. The initial logical chain framework provided by the dependency strength modeling module is activated to locate the sequence position of the mutation node in the corresponding logical chain. When the following conditions are met at the same time, it is determined to be an inter-chain switching point:

[0097] The dependency strength value of a node in a chain decreases by more than 2 standard deviations of the average gradient change of the chain compared to its predecessor node.

[0098] The entropy value of the subsequent module call probability distribution recorded by the topological structure difference submodule suddenly increased by more than 50%;

[0099] The dynamic call sequence shows that the node subsequently has cross-chain call behavior;

[0100] The conflict resolution and final judgment unit is used to use the topological structure difference weight as the decision factor when the mutation node simultaneously meets some characteristics of the convergence point and the switching point. If the mutation node is in a structurally symmetrical position in the majority chain, the convergence point judgment is strengthened; if the position distribution is discrete and accompanied by drastic fluctuations in the call probability, the switching point judgment is strengthened.

[0101] In the above embodiment, the key logic node generation submodule of this embodiment achieves accurate classification of mutant node functions and optimized construction of logical network structure through the collaborative work of three units: multi-chain cross-validation, single-chain strength decay analysis, and conflict resolution and final judgment. Node function is accurately identified. Through the multi-chain cross-validation unit, nodes with stable call probabilities and balanced dependency strength distribution in multiple logical chains are identified, and their role as branch convergence points is clarified. Such nodes play a core role in the aggregation and distribution of system logic. Through the single-chain strength decay analysis unit, nodes with significantly reduced dependency strength and accompanied by mutations in call patterns are captured and determined to be inter-chain switching points. Such nodes reflect the dynamic jump behavior of the system logic path. Network structure optimization construction. The conflict resolution and final judgment unit solves the boundary conditions of node role determination through the decision-making mechanism of topological structure difference weights, ensuring the rationality of network structure division. The final output node classification result provides a reliable structural basis for constructing a multi-level logical network containing a main chain and branch chains, enabling the network to more accurately reflect the actual operating rules of the system. The entire decision process is rigorously based on quantitative features such as dynamic call sequences, dependency strength distribution, and topological structure differences. Multi-condition combination verification ensures the objectivity of the decision results. Adaptive mechanisms such as dynamic thresholds and statistical standard deviations enable the decision criteria to adapt to system characteristics under different operating conditions, improving the robustness of the analysis. These mechanisms enable automated identification and classification of logical node functions within the system, providing a structured network model foundation for tasks such as architecture analysis, performance optimization, and behavior prediction.

[0102] Example 7:

[0103] like Figure 3 As shown, based on Example 1, the path selection subsystem provided by this embodiment of the present invention includes:

[0104] The logic association module is used to determine the transition probability based on the dependency strength and call probability in the multi-level logic chain network. Monte Carlo simulation generates paths based on the multi-level logic chain network structure. The weight calculation relies on the key node labels in the multi-level logic chain network to form a closed loop for test path optimization.

[0105] The transition probability definition module is used to define the random sampling probability of transition between functional modules in a multi-level logic chain network based on the function module call probability distribution and dependency strength;

[0106] The path simulation module is used to simulate the possible paths of the monitoring software through random sampling probability to generate several test paths; the weight of each test path is dynamically determined by the number of key nodes it covers, and the preliminary test path with the greatest impact on the core logic of the monitoring software is screened out.

[0107] In the above-mentioned embodiment, the path selection subsystem of this embodiment constructs a test path generation and optimization system based on a multi-level logic chain network through the synergistic effect of the logic association module, the transition probability definition module, and the path simulation module. A dependency-driven path generation mechanism: The logic association module constructs a quantifiable transition probability model by analyzing the dependency strength and call probability between functional modules. Key node labels are used to dynamically calculate path weights, forming a closed-loop optimization to ensure that the generated test paths can effectively reflect the actual call relationships within the system. A probabilistic path exploration method: The transition probability definition module converts the call relationships between functional modules into random sampling probabilities, and the path simulation module performs probabilistic sampling using the Monte Carlo method. This method can efficiently explore multiple possible path combinations for monitoring software operation while preserving the core logic chain. Optimized screening of key node coverage: By dynamically calculating the number of key nodes covered by the test path and establishing a weight evaluation standard, the system automatically selects the test path set that has the greatest impact on the core logic of the monitoring software. The key node-based screening mechanism ensures that test resources are concentrated on the most core functional verification of the system.

[0108] Example 8:

[0109] Based on Example 7, the transition probability definition module provided in this embodiment of the present invention includes:

[0110] The call probability quantification submodule is used to analyze the interaction between functional modules based on a multi-level logic chain network. The dependency strength reflects the closeness between functional modules, and the call probability reflects the frequency of being triggered in actual operation. Together, they form an initial correlation matrix.

[0111] The probability distribution adjustment submodule is used to normalize the dependency strength and call probability of the initial correlation matrix to ensure comparability of data in different dimensions. Based on the hierarchical structure of the multi-level logic chain network, the probability distribution is adjusted so that the call probability of high-level modules is more likely to influence global path selection, while the dependency strength of low-level modules affects local transfers.

[0112] The dynamic probability mapping submodule is used to map the adjusted probability distribution to the transfer relationship between functional modules, forming a sampling probability matrix that reflects the static call relationship. It also introduces dynamic randomness through the Monte Carlo method. The generated test path conforms to the core logic and covers edge cases.

[0113] In the above embodiment, this embodiment converts the dependency strength and call frequency between functional modules into a computable quantitative relationship through the construction of an initial association matrix, providing a data basis for probability adjustment. This modeling method retains the topological characteristics of the original interaction relationship. Based on the network hierarchy structure, the probability distribution is adjusted differentially to achieve the separation of global path selection and local transfer control; the high-level modules dominate the overall direction, and the low-level modules refine the local paths to form a hierarchical probability distribution system. The static association relationship is combined with dynamic random sampling through the probability matrix, which increases the diversity of path exploration while ensuring the coverage of the core logic. The introduction of the Monte Carlo method enables the system to effectively balance the needs of key testing and edge case detection. The complete transformation process from the original interaction relationship to the final sampleable probability is realized, providing a scientific and quantifiable probability basis for test path generation.

[0114] Example 9:

[0115] On the basis of Example 8, the calling probability quantization submodule provided in this embodiment of the present invention includes:

[0116] The relationship conversion unit is used to convert the implicit module relationships in the multi-level logic chain network into computable structured data. Each connection line carries the dual attributes of dependency strength and call probability.

[0117] The convolution processing unit is used to perform bidirectional scanning along the topological path of the multi-level logical chain network. When tracing upward, it marks the aggregation relationship between high-level functional modules and low-level components. When tracing downward, it records the support mode of basic functional modules for complex functions. After three-dimensional convolution processing, it forms an initial correlation matrix.

[0118] The parameter comparison unit is used to process the symbiotic parameters of the initial correlation matrix using adaptive weighting. For interaction edges with strong dependence but low frequency, structural weight advantages are given; while for connection lines with high frequency and weak dependence, their dynamic characteristics are retained. Both the dependence strength and call frequency are set with strong and weak, high and low standards.

[0119] In the above embodiment, the call probability quantization submodule of this embodiment realizes the precise transformation from abstract system architecture to computable model through the coordinated operation of three core units; the relationship conversion unit establishes the mathematical expression basis of the interaction relationship between functional modules, and makes the implicit connection in the network topology explicit as a data link with quantitative attributes; the convolution processing unit carries out feature extraction of spatial dimension on this basis, and captures the upstream and downstream influence relationship in the hierarchical architecture through bidirectional scanning. The initial correlation matrix it outputs has a composite expression of structural characteristics and dynamic characteristics. The parameter comparison unit finally completes the dynamic balance of key parameters. Its adaptive weighting mechanism is compatible with runtime characteristics while maintaining structural integrity, so that the finally generated correlation matrix reflects both the static design constraints of the system and the dynamic patterns in actual operation; the processing chain formed by the three units essentially builds a bridge connecting the system design principles and runtime behavior, providing an input benchmark with both structural rationality and behavioral authenticity for probability distribution adjustment and dynamic path generation.

[0120] Example 10:

[0121] As shown in FIG10 , based on Example 8, the probability distribution adjustment submodule provided in this embodiment of the present invention includes:

[0122] The feature separation unit is used to separate the two feature dimensions of dependency strength and call probability from the initial association matrix and perform normalization processing on them respectively. The call probability data of the high-level functional modules is adjusted by the hierarchical amplification coefficient to shift its numerical distribution towards the high-weight interval. The dependency strength of the low-level functional modules is subjected to local focusing processing to retain its relative weight within a specific functional domain.

[0123] The factor application unit is used to apply influence factors layer by layer starting from the top node. The call probability of each high-level functional module will permeate downward along the logic chain network, and its influence will decay exponentially with the depth of the hierarchy. At the same time, the dependency strength of the bottom-level functional module will form a local weight pool within the scope of its directly associated upper-level functional module.

[0124] The probability adjustment unit, used for global path selection, mainly absorbs the adjusted call probability of high-level functional modules and forms the decision-making backbone after smoothing. The local transfer path depends on the weight distribution results of the strength parameters of the underlying functional modules, and performs regional rebalancing while maintaining the original relative relationship. The output probability distribution shows obvious hierarchical gradient characteristics. The high-level parameters have a wide range of influence but a small adjustment range, while the low-level parameters have a narrow range of influence but a high adjustment accuracy.

[0125] In the above-mentioned embodiment, the probability distribution adjustment submodule of this embodiment achieves differentiated adjustment of parameters at different levels in a multi-layered system architecture through the collaborative processing of three units. The feature separation unit first completes the dimensional decoupling and standardization of the original parameters, establishing benchmark data for hierarchical processing. The factor application unit implements vertical parameter transmission control based on the network topology, allowing high-level influences to have top-down propagation characteristics while retaining the local correlation characteristics of the bottom layer. The probability adjustment unit ultimately forms a dual-channel adjustment mechanism: the global path inherits the wide adjustment characteristics of the high-level parameters, while the local path maintains the fine control capability of the bottom-level parameters. The output results of the entire processing flow show a strict hierarchical correspondence. The probability distribution of the high-level module dominates the system-level behavior pattern, and the strength parameters of the bottom-level module constrain the component-level interaction rules. The hierarchical adjustment mechanism ensures the stability of the overall architecture while retaining appropriate autonomy for functional units of different granularity. The resulting probability distribution satisfies both the macro constraints of system design and the micro requirements of actual operation.

[0126] Example 11:

[0127] like Figure 4 As shown, based on Example 1, the report generation subsystem provided by this embodiment of the present invention includes:

[0128] The path expansion module is used to expand the preliminary test paths generated by the path selection subsystem. Positive pairs consist of path segments covering key logical nodes. The selection criteria combine the key node distribution characteristics identified by the network construction subsystem and the path weights calculated by the path selection subsystem. Negative pairs come from two situations: logical chain connection areas that do not reach the dynamic threshold, or low-weight path segments in Monte Carlo simulations.

[0129] In the dual-channel feedback module, the path features of the positive example pairs are extracted as multidimensional vectors, including dimensions such as key node coverage and adjacent node differences. The missing features of the negative example pairs serve as comparison samples. The optimization process iteratively adjusts the transition probability distribution of the test path, so that the positive example feature vector gradually approaches the topological centrality distribution of the logic chain network, while the negative example feature vector is pushed away from the effective coverage area.

[0130] The path optimization module is used to consider the node criticality scores determined by the network construction subsystem, the path weight coefficients assigned by the path selection subsystem, and the coverage gap data generated by the comparison of positive and negative examples. The optimized test paths will dynamically enhance the probability of accessing high-criticality nodes while maintaining the original weight distribution. The resulting path set retains the random characteristics of the Monte Carlo simulation.

[0131] In the above embodiment, the report generation subsystem of this embodiment realizes the intelligent optimization and evaluation of the test path through the synergy of three modules. The path expansion module first completes the classification processing of the preliminary path, and establishes a positive and negative sample library based on the network characteristics and path weights provided by the preceding subsystem. The dual-channel feedback module constructs the optimization target based on the characteristic differences of the positive and negative examples, and guides the path evolution direction through the distribution adjustment in the vector space. The path optimization module finally integrates the criticality score, weight coefficient and coverage gap data to improve the detection efficiency of key logic while maintaining the diversity of the path. The whole mechanism enables the final generated test path to have dual characteristics: it inherits the statistical randomness of the Monte Carlo simulation, and strengthens the coverage capability of the core logic chain through the optimization process. The output test report not only reflects the normal operation trajectory of the system, but also effectively exposes potential anomalies in the deep logic chain, providing a multi-dimensional detection basis for software quality assessment including structural characteristics and probability distribution.

[0132] Figure 5 A block diagram is shown of an exemplary electronic device suitable for implementing embodiments of the present invention.

[0133] The electronic device may include a central processing unit / microprocessor / main control chip, etc.; a storage medium, coupled to the central processing unit / microprocessor / main control chip, etc., and storing computer-executable instructions therein, for performing the steps of each method of an embodiment of the present invention when executed by the processor.

[0134] The central processing unit / microprocessor / main control chip etc. may include but is not limited to, for example, one or more processors or microprocessors etc.

[0135] The storage medium may include, but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (such as hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0136] In addition, the electronic device may also include (but not limited to) a data bus, an input / output bus / external bus / device bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.).

[0137] The central processing unit / microprocessor / main control chip etc. can communicate with external devices via an I / O bus via a wired or wireless network (not shown).

[0138] The storage medium may also store at least one computer-executable instruction for executing the various functions and / or method steps in the embodiments described in this technology when executed by a central processing unit / microprocessor / main control chip, etc.

[0139] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0140] Figure 6 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0141] like Figure 6 As shown, a non-transitory computer-readable storage medium stores instructions, such as computer-readable instructions. When the computer-readable instructions are executed by a processor, the various methods described above can be executed. Non-transitory computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium can be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0142] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0143] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0144] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0145] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the various embodiments of the method of the present invention through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (English full name: Read-Only Memory, English abbreviation: ROM), random access memory (English full name: Random Access Memory, English abbreviation: RAM), magnetic disk or optical disk, and other media that can store program code.

[0146] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A testing device for Internet software development, characterized in that: Include: The network construction subsystem is used to extract the calling relationships between functional modules by monitoring the calling sequence of functional modules during software runtime, and calculate the calling probability distribution of each module; it then uses the entropy weight method to calculate the dependency strength between modules and construct the initial logical chain based on the dependency strength; By calculating the difference between adjacent logic chains and combining dynamic thresholds, key logic nodes are identified to form a multi-level logic chain network; The path selection subsystem is used to define randomly sampled transition probabilities based on a multi-level logic chain network and generate multiple test paths through Monte Carlo simulation. The weight of each path is determined by the number of logic nodes covered by the path, and the paths with the highest weights are selected as preliminary test paths. The report generation subsystem is used to construct positive and negative example pairs in a multi-level logic chain network; optimize the test path through the loss function so that the path covers the key nodes in the logic chain network; and perform weighted aggregation on the test paths in the multi-level logic chain network to generate the final test report.

2. The Internet software development testing device according to claim 1, wherein: Network construction subsystem, including: The operation monitoring and relationship extraction module is used to monitor the function module calling behavior in real time during software operation, capture the calling sequence and frequency between function modules, and form a dynamic calling sequence; Based on the dynamic call sequence, the actual interaction pattern between functional modules is analyzed, the call probability distribution of each functional module is quantified, and the actual operation rules within the monitoring software are revealed; The dependency strength modeling module is used to calculate the dependency strength between functional modules using the entropy weight method using probability distribution, transforming abstract call relationships into quantifiable logical chains. Functional modules whose dependency strength meets the preset standard form a closely related logical chain, while those that do not meet the preset standard serve as auxiliary branches to build the initial logical chain framework. The key node identification module is used to analyze the differences between adjacent logic chains and, combined with dynamic thresholds, screen out the core branch points of the monitoring software functions corresponding to the nodes with sudden changes in the differences in the initial logic chain framework, mark them as key logic nodes, and ultimately form a multi-level logic network consisting of a trunk chain and branch chains.

3. The Internet software development testing device according to claim 2, wherein: Run monitoring and relationship extraction module, including: The behavior capture and pattern extraction submodule is used to continuously observe and record the activation order and interaction frequency between functional modules. Based on the activation order, the call sequence is obtained, and the call frequency is obtained from the interaction frequency, forming a complete time series record of the calls. Based on the time series record, the collaboration mode between functional modules is analyzed, the distribution of each functional module being triggered is calculated, and the basic characteristics of the software monitoring component operation are discovered; The association relationship quantification and structure construction submodule is used to evaluate the association degree between functional modules using weight calculation after understanding the probability distribution of each functional module being triggered, and convert the collaborative relationship of functional modules into specific numerical indicators. Functional module combinations with an association degree exceeding the set standard are identified as strong associations, forming the core path of software operation; combinations with an association degree not exceeding the set standard are used as supplementary paths, preliminarily establishing a relationship network that reflects the initial logical chain framework of the monitoring software operation. The core element positioning and network improvement sub-module is used to identify key turning points by comparing the differences between adjacent core paths, mark the locations of key turning points, and form a hierarchical operation network that reveals the actual operation rules within the monitoring software.

4. The Internet software development testing device according to claim 2, wherein: Dependency Strength Modeling Module, including: The probability distribution data preparation submodule is used to collect the probability distribution data of function module calls and extract the transition probability matrix between each function module. The transition probability matrix records the conditional probability of calling from any function module A to function module B, forming the basic data set for dependency strength calculation. The information uncertainty measurement submodule is used to calculate the information entropy value of the call distribution for each functional module. The information entropy value reflects the degree of uncertainty of the flow to other modules when the functional module is called; according to the entropy value calculation results of each functional module, it is converted into a weight coefficient through normalization processing; The dependency strength comprehensive calculation submodule is used to combine the transition probability matrix and the weight coefficient to calculate the comprehensive dependency strength index between functional modules.

5. The Internet software development testing device according to claim 1, wherein: Key node identification module, including: The input data preparation submodule is used to extract the call frequency distribution and dependency strength distribution of adjacent logic chains from the dynamic call sequence. The following features of the two logic chains are compared: the difference in call probability distribution, the gradient change in dependency strength, and the difference in topological structure. The dynamic threshold adjustment submodule is used to dynamically adjust the difference determination threshold according to the overall dependency strength distribution of the current logic chain set; modules whose difference exceeds the dynamic threshold are determined to be mutation nodes; The key logic node generation submodule is used to associate the mutation node with the module role in the initial logic chain framework. If the mutation node belongs to multiple high-difference logic chains at the same time, it is determined to be a branch convergence point; if the dependence strength of the mutation node in a single logic chain drops sharply, it is determined to be an inter-chain switching point. The node constitutes the core branch point in the multi-level logic network, connecting the main chain and the auxiliary branch chain.

6. The Internet software development testing device according to claim 5, wherein: Difference in the calling probability distribution of the input data preparation submodule: Compare the calling probability offset of the same functional module in the two chains; Dependency intensity gradient change: Analyze the dependency intensity difference between the same module pairs in two chains. If the difference exceeds the dynamic threshold, it is marked as a potential difference point. Topological structure difference: Check whether the overlapping modules of the two chains are in similar positions in their respective chains. If the position offset is significant, increase the difference weight.

7. The Internet software development testing device according to claim 5, wherein: Key logic node generation submodule, including: The multi-chain cross-validation unit is used to take the set of mutation nodes obtained from the dynamic threshold adjustment submodule as input and trace back the adjacent logical chain association information recorded by the input data preparation submodule. For each mutation node, it retrieves all the logical chain identifiers to which it belongs and extracts the difference matrix between the logical chains, which contains the call probability offset, dependency strength difference, and topological position offset. The single-chain strength decay analysis unit is used to switch to single-chain analysis mode for mutation nodes that have not passed multi-chain verification. The initial logical chain framework provided by the dependency strength modeling module is activated to locate the sequence position of the mutation node in the corresponding logical chain. When the following conditions are met at the same time, it is determined to be an inter-chain switching point: The conflict resolution and final decision unit is used to use the topological structure difference weight as the decision factor when the mutation node meets some of the characteristics of the convergence point and the switching point. If the mutation node is in a structurally symmetrical position in the majority chain, the convergence point decision is strengthened. If the position distribution is discrete and accompanied by drastic fluctuations in the call probability, the switching point determination is strengthened.

8. The Internet software development testing device according to claim 7, wherein: When a mutation node appears in three or more logical chains, and the difference indicators between the logical chains all exceed the adjusted dynamic threshold, the convergence point judgment condition is triggered. At this time, the dependency strength change pattern of each chain at the mutation node is checked: if the node's call probability in each chain remains stable, and the dependency strength distribution in the output direction shows multi-path balance, then it is confirmed that the mutation node assumes the branch convergence function.

9. The Internet software development testing device according to claim 1, wherein: Path selection subsystem, including: The logic association module is used to determine the transition probability based on the dependency strength and call probability in the multi-level logic chain network. Monte Carlo simulation generates paths based on the multi-level logic chain network structure. The weight calculation relies on the key node labels in the multi-level logic chain network to form a closed loop for test path optimization. The transition probability definition module is used to define the random sampling probability of transition between functional modules in a multi-level logic chain network based on the function module call probability distribution and dependency strength; The path simulation module is used to simulate the possible paths of the monitoring software through probabilistic sampling and generate several test paths. The weight of each test path is dynamically determined by the number of key nodes it covers, and the preliminary test path with the greatest impact on the core logic of the monitoring software is screened out.

10. The Internet software development testing device according to claim 1, wherein: Report generation subsystem, including: The path expansion module is used to expand the preliminary test paths generated by the path selection subsystem. Positive pairs consist of path segments covering key logical nodes. The selection criteria combine the key node distribution characteristics identified by the network construction subsystem and the path weights calculated by the path selection subsystem. Negative pairs come from two situations: logical chain connection areas that do not reach the dynamic threshold, or low-weight path segments in Monte Carlo simulations. In the dual-channel feedback module, the path features of the positive example pairs are extracted as multidimensional vectors, including key node coverage and adjacent node differences. The missing features of the negative example pairs form comparison samples. The optimization process iteratively adjusts the transition probability distribution of the test path, so that the positive example feature vector gradually approaches the topological centrality distribution of the logic chain network, while the negative example feature vector is pushed away from the effective coverage area. The path optimization module is used to consider the node criticality scores determined by the network construction subsystem, the path weight coefficients assigned by the path selection subsystem, and the coverage gap data generated by the comparison of positive and negative examples. The optimized test paths will dynamically enhance the probability of accessing high-criticality nodes while maintaining the original weight distribution. The resulting path set retains the random characteristics of the Monte Carlo simulation.

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