Go library migration method based on semantic dependency graph and complexity scheduling

Abstract

The application discloses a Go language library migration method based on semantic dependency graph and complexity scheduling, and comprises the following steps: S1, source code is acquired, and a unified semantic dependency graph is generated; S2, a minimum migration unit is generated; S3, multi-dimensional complexity quantification and risk modeling calculation are carried out on each minimum migration unit, a risk score is generated, all minimum migration units are sorted according to the risk score from low to high, and a migration priority queue is constructed; S4, according to the migration priority queue, the migration unit with lower risk is preferentially executed, and a migration state is recorded; S5, a test verification process is carried out; and S6, migration scheduling is carried out. The application realizes automatic migration planning based on program analysis, graph theory structure and complexity scheduling algorithm, does not depend on model training or external knowledge enhancement mechanism, and has high engineering applicability.

Description

Technical Field

[0001] This invention relates to the field of computer software technology, and more specifically to a method for migrating Go language dependency library APIs. Background Technology

[0002] With the widespread adoption of cloud-native architectures and microservice systems, Go, due to its high performance, concurrency model, and ease of deployment, has gained widespread use in backend services, infrastructure components, and distributed systems. In real-world engineering environments, upgrading, replacing, and migrating third-party libraries has become an unavoidable and routine operation.

[0003] In Go projects, library migration typically occurs in the following scenarios: the original library is no longer maintained or is deprecated; the target library offers a better performance or more secure implementation; the Application Programming Interface (API) undergoes a disruptive change during version evolution; or the project needs to unify the technology stack or eliminate dependency conflicts.

[0004] However, the Go language ecosystem emphasizes lightweight dependencies and rapid iteration, resulting in frequent library version updates and a fast pace of API changes. In large-scale projects, library calls are typically distributed across multiple files and packages, forming complex cross-file semantic dependency networks through function calls, struct fields, interface implementations, and return value propagation. When a library is replaced, modifications to a single API often propagate along the call chain, data flow chain, and type implementation chain, creating cascading effects across modules.

[0005] Traditional library migrations primarily rely on manual labor. Developers typically work by reading documentation, searching for call points, modifying function signatures and parameter structures one by one, and then verifying the changes through compilation and testing. This approach has the following problems: (1) Uncontrollable migration scope. Developers find it difficult to accurately determine the true impact of a certain API call, and are prone to overlooking indirect dependent nodes or excessively modifying unrelated code, resulting in low migration efficiency.

[0006] (2) Complex dependency propagation. When migration involves changes in struct fields, adjustments to interface method signatures, or changes in return value semantics, the impact often propagates across files and packages, making it difficult for humans to fully analyze the data flow and call graph structure.

[0007] (3) Migration order cannot be planned. In projects with multiple migration points, the complexity and risk level of different migration units vary significantly. If highly coupled nodes are processed first, it may lead to a large number of system compilation failures.

[0008] (4) Failures are difficult to isolate. When migration introduces errors, it is often necessary to roll back the entire process or manually troubleshoot, lacking a progressive verification and local convergence mechanism.

[0009] To improve migration efficiency, existing technologies (Padioleau, Y., Lawall, J., & Muller, G. SmPL: ADomain-Specific Language for Specifying Collateral Evolutions in Linux Device Drivers, ACM SIGPLAN Notices, 2009.) have proposed a class of automated migration tools based on syntax analysis and rule replacement. For example, tools based on Abstract Syntax Tree (AST) matching and rewriting rules can automatically identify specified API calls and replace them with the signature of the target function. While these methods can automatically perform parameter rearrangement or function renaming in simple scenarios, they still have significant shortcomings: (1) It only supports equivalent replacement and has difficulty in handling non-equivalent migration. When the target library changes its initialization process, context passing method or resource lifecycle management mode, simple syntax rewriting cannot guarantee semantic consistency.

[0010] (2) Lack of cross-file dependency analysis capabilities. Most tools only perform matching and replacement at the single-file level and cannot handle cross-function call chains or cross-package propagation scenarios.

[0011] (3) No unified semantic dependency model has been established. Existing tools are usually based on local AST rewriting, lacking a unified dependency graph structure that covers call relationships, data flow relationships and type implementation relationships.

[0012] (4) Lack of a mechanism for quantifying migration risks. Existing technologies cannot measure migration complexity or plan the migration sequence according to risk levels, resulting in a lack of engineering control over the overall migration process.

[0013] (5) Lack of a progressive convergence scheduling mechanism. When a migration fails, the system cannot dynamically adjust the subsequent migration order or re-evaluate the dependency graph structure, and the migration process lacks adaptive capability.

[0014] On the other hand, Go's engineering features further exacerbate migration complexity. Go emphasizes implicit interface implementation, struct embedding and composition, and the extensive use of concurrency models (goroutines, channels). In library migration scenarios, changes to interface signatures may affect multiple implementation structs; adjustments to concurrent structures may trigger race conditions; and changes in return value error types may affect error handling chains. Therefore, relying solely on text replacement or simple rule matching is insufficient to guarantee post-migration engineering stability.

[0015] In summary, existing technologies in the field of Go library migration have the following core technical deficiencies: lack of a unified semantic dependency graph model covering cross-file and cross-package propagation; lack of a multi-dimensional and complex metric mechanism for characterizing migration risks; lack of an adaptive migration scheduling algorithm based on dynamic risk ranking; lack of a dynamic update mechanism for dependency structure after successful migration; and lack of a local isolation and compatibility guarantee mechanism when migration fails.

[0016] Therefore, it is necessary to propose a Go library migration method based on a unified semantic dependency graph and adaptive complexity scheduling. By constructing a unified dependency graph model that covers call relationships, data flow relationships, and type implementation relationships, and combining multi-dimensional complexity risk assessment and dynamic scheduling mechanisms, a step-by-step, verifiable, and convergent progressive migration process can be realized, thereby improving the engineering controllability and stability of the migration process.

[0017] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0018] To address the common engineering and technical problems encountered in the migration of existing Go libraries, such as uncontrollable migration scope, unclear dependencies, complex cross-file propagation, unpredictable migration order, difficulty in locating migration failures, and inability to reliably pass compilation and testing verification, this invention proposes a progressive migration method for Go libraries based on a unified semantic dependency graph, minimum self-dependent unit closure convergence, and a multi-dimensional complexity adaptive scheduling mechanism.

[0019] To achieve the above objectives, the technical solution of the present invention is as follows: The Go library migration method based on semantic dependency graphs and complexity scheduling includes the following steps: S1. Obtain the source code and generate a unified semantic dependency graph G = (V, E), where: V represents the set of semantic nodes, and E represents the set of dependencies; the semantic node refers to the node used to express or carry semantic information in the syntactic structure of the code, and the dependency refers to the relationship in the code project where one semantic node uses another semantic node in some operations; S2. Generate the minimum migration unit The smallest migration unit This refers to a single call node within the semantic node set A of the old library API calls. The scope of all dependencies and relationships is represented as follows: ,in, Represents semantic nodes. For serial number, This represents a reachable path along dependent edges, i.e., a propagation path formed through several dependent edges; S3. For each smallest migration unit Perform multidimensional complex quantification and risk modeling calculations to generate risk scores. All according to Sort the data from lowest to highest priority and construct a migration priority queue. S4. Based on the migration priority queue, migration units with lower risk are migrated first, and their migration status is recorded; S5. Conduct testing and verification procedures; S6. Migration Scheduling.

[0020] Preferably, step S7, recording and quantitatively evaluating migration results, can be performed.

[0021] Preferably, in step S2, strong connected component expansion is performed. If a node in the current minimum migration unit belongs to a strong connected component, and there are still nodes in the strong connected component that have not been included in the minimum migration unit, then these nodes are added to the minimum migration unit.

[0022] Preferably, in step S2, if the number of overlapping nodes of two minimum migration units reaches a threshold, they are merged.

[0023] Preferably, in step S2, the element boundary function is defined. ,like If the dependencies in the migration unit can be isolated through interface abstraction, then the migration unit is considered an independent migration unit; otherwise, the migration unit is marked as a cross-domain propagation unit.

[0024] Preferred risk score Calculated using the following formula:

[0025] in, Indicates the first Complexity, express The weight of the complexity includes both syntactic complexity and the complexity of cross-file propagation, cross-package propagation, type structure, concurrency coupling, and circular dependency engineering factors.

[0026] Preferably, step S6 includes: S6a. When If all verifications pass and the migration is deemed successful, the affected subgraph region is reconstructed. Delete the old library related nodes and the dependency edges related to the nodes, replace the API call structure, add the target library API node set after migration to the node set V, and re-establish the new dependency edges according to the code structure after migration. Only recalculate the complexity index and risk score for this subgraph region, then update the migration priority queue and return to step S4. S6b. If If migration verification fails, determine whether to enter proxy layer compatibility mode. If you enter proxy layer compatibility mode, do not perform complexity decay or update the risk score, and return to step S4. S6c. If the migration unit If multiple consecutive verifications fail and the proxy layer is not entered, the cell is marked as Failed and temporarily frozen, then the process returns to step S4. S6d. The scheduling loop terminates when any of the following conditions are met: all migration units are in the status of successful migration and have passed the verification process or the actual migration failed but was completed through a proxy layer compatible method; only frozen units remain in the queue; or the preset migration budget or iteration limit is reached.

[0027] Furthermore, this invention also provides a Go library migration system based on semantic dependency graphs and complexity scheduling. This system includes a unified semantic dependency graph module, a minimum migration unit module, a multi-dimensional complexity quantification and risk modeling module, a unit migration module, a testing and verification module, and a migration scheduling module. Preferably, it also includes a result recording and quantitative evaluation module.

[0028] Meanwhile, the present invention also provides a Go language library migration device based on semantic dependency graph and complexity scheduling, including: a memory, a processor, and a Go language library migration program based on semantic dependency graph and complexity scheduling stored in the memory and executable on the processor. The Go language library migration program based on semantic dependency graph and complexity scheduling is configured to implement a Go language library migration method based on semantic dependency graph and complexity scheduling.

[0029] The present invention also provides a storage medium storing a Go language library migration program based on semantic dependency graph and complexity scheduling. When the Go language library migration program based on semantic dependency graph and complexity scheduling is executed, it implements the Go language library migration method based on semantic dependency graph and complexity scheduling.

[0030] By adopting the above technical solution, the present invention has the following beneficial technical effects: (1) By using a unified semantic dependency graph, the migration scope and dependency propagation path can be calculated and defined, thereby improving the accuracy of migration boundaries. (2) By using the minimum self-dependent migration unit closure algorithm, engineering-level decomposition is achieved, reducing the migration granularity from the global level to the unit level. (3) Through a multidimensional complex quantification model, the mathematical modeling and sortable processing of migration risk are realized. (4) By using independent dynamic dependency graph updates and complexity decay steps, the migration risk decreases as the execution process progresses, ensuring that the migration algorithm has engineering-level convergence characteristics. Through a testing and verification mechanism, the migration process has unit-level verification capability, improving the stability of the results. (5) By using the proxy layer compatibility mechanism, a workable fallback mechanism is implemented in case of migration failure, thereby reducing the overall risk of the system.

[0031] (6) This invention implements automatic migration planning based on program analysis, graph theory structure and complexity scheduling algorithm, without relying on model training or external knowledge enhancement mechanism, and has high engineering applicability. Attached Figure Description Figure 1 This is a flowchart illustrating the method of the present invention.

[0032] Figure 2 This is a schematic diagram of the system of the present invention. Detailed Implementation

[0033] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0034] Example 1: As Figure 1 As shown, the Go library migration method based on semantic dependency graphs and complexity scheduling includes the following steps: S1. Obtain the source code and generate a unified semantic dependency graph G: G = (V, E), where V represents the set of semantic nodes and E represents the set of dependencies. A semantic node is a node in the code's syntactic structure (such as an abstract syntax tree, AST) used to express or carry semantic information. A dependency relationship refers to a situation in the code project where one semantic node (the dependent party) uses another semantic node (the party being depended upon) in certain operations. For ease of explanation of the subsequent actual workflow, some relationships are listed below (those skilled in the art can establish other relationships as needed): call relationships, data flow dependencies, type dependencies, cross-file dependencies, cross-package dependencies, global variable dependencies, and concurrency dependencies.

[0035] This embodiment provides a specific method for constructing a unified semantic dependency graph (those skilled in the art can also use other existing technical means to construct a unified semantic dependency graph): Abstract Syntax Tree (AST) parsing is performed on the source code to extract semantic nodes such as functions, methods, structures, interfaces, and variables; symbol binding is performed based on type system analysis to determine type dependencies; call relationships are constructed by traversing call expressions; data flow analysis is performed based on variable assignment and parameter passing relationships to obtain data flow dependencies; cross-file and cross-package dependencies are obtained by combining import parsing, and concurrent structures are identified to establish concurrent dependencies.

[0036] The above processes can all be completed automatically through static program analysis methods, without relying on large language models or manual annotation. They can be achieved based on existing compiler front-end and code analysis technologies, thereby forming a unified semantic dependency graph that covers call relationships, data flow relationships, and type dependencies.

[0037] The semantic node In the syntactic structure of code (such as an Abstract Syntax Tree, AST), nodes are used to express or carry semantic information. The set representation is as follows (this only lists commonly used nodes and does not represent all nodes):

[0038] in: For a set of function nodes, For a set of method nodes, A collection of structure nodes. For a set of interface nodes, For a set of variable nodes, For a set of constant nodes, It is a collection of anonymous functions and closure nodes. For a set of concurrent structure nodes, For a collection of generic type parameter nodes, For a set of package nodes, It is a collection of file nodes.

[0039] Some special nodes, such as function parameter nodes, return value nodes, structure field type nodes, local variable nodes, and global variable nodes, can be identified by existing nodes (such as variable nodes). constant nodes Modeling is performed in a specific form, and the role of each node (parameter, return value, structure field, local variable or global variable) is identified in the node attributes so that it can be used in subsequent data flow analysis and type dependency analysis; package nodes and file nodes are used to characterize cross-file and cross-package propagation relationships, providing a basis for subsequent dependency scope calculation.

[0040] The dependencies are uniformly modeled as directed relations, represented as follows: ,in For dependent semantic nodes, For the semantic node of the dependent party, For dependency relationship type, The dependency graph is derived from a predefined set of relation types T = {call relations, data flow relations, type dependency relations, cross-file propagation relations, and concurrent structure relations}. All dependencies satisfy directionality, consistency, and node closure constraints, meaning that the starting and ending points of dependency edges both belong to the semantic node set V, and the relation type and node type satisfy predefined matching rules. All dependencies can be extracted using static program analysis methods based on abstract syntax trees, type systems, and symbolic resolution. This dependency graph is used to characterize the semantic topology of the entire project and serves as the basis for subsequent minimum migration unit convergence and complexity calculation.

[0041] The following describes the specific relationship construction. This is only an example, and those skilled in the art can choose other methods to construct relationships based on existing technology.

[0042] The call relationships are obtained by traversing all function call expressions in the abstract syntax tree and constructing call dependency edges. ,in: : Represents the calling node (usually a function node or method node); : Indicates the node being called (usually a function or method node); Indicates the package path to which the called node belongs; : Indicates whether the call is a legacy library API call, where This indicates an API call from the source library to be migrated. This represents a regular function call. Call dependency edges are directional based on the relationship between the caller and the called party. All call dependency edges constitute the call dependency edge set. Simultaneously, a call graph is established based on the set of call dependency edges. The call relationships can be represented by this call graph. Each edge in the call graph corresponds to a call dependency edge, ecall. The call graph is used to depict the call hierarchy and call propagation path between functions, and to analyze the call chain diffusion range of old library API calls in the program, providing a basis for the convergence calculation of the minimum self-dependent migration unit.

[0043] The data flow dependencies are obtained as follows: Based on parameter passing and variable assignment relationships, a data flow dependency graph DFG=(Vdata,Edata) is constructed, where: Vdata V represents the set of semantic nodes involved in data propagation (generally including parameter nodes, local variable nodes, structure field nodes, and return value nodes). All of these types of nodes are obtained by parsing the function parameter list, return value definition, variable declaration statement, and structure field definition in the abstract syntax tree. Data flow dependencies are generated by identifying assignment statements, function call parameter passing, return statements, and structure field assignment operations in the abstract syntax tree. This process falls under the category of static data flow analysis and can be automatically implemented based on existing compiler front-ends or program analysis tools. Edata represents the set of data flow dependency edges. Data flow dependency edges are represented as follows: ,in: Indicates the source data node; Indicates the target data node; Indicates the type of data propagation relationship, when =1 indicates that the parameter is passed to the edge. =2 indicates a local variable assignment edge. =3 indicates that the assignment of a structure field propagates along an edge. =4 indicates a return value propagation edge. All data flow dependent edges constitute the data flow dependent edge set: .

[0044] The type dependencies are obtained through the following method: based on the analysis results of the abstract syntax tree and type system, the type definitions, type references, interface implementations, and generic constraints between semantic nodes are identified. The type dependencies are automatically identified through static type analysis, specifically: based on the abstract syntax tree parsing results combined with the language type system, symbol binding and type inference are performed to identify function parameter types, return value types, structure field types, method receiver types, interface implementation relationships, and generic constraints. This process falls under the scope of compiler front-end semantic analysis or static program analysis and can be automatically implemented based on existing type checkers or code analysis tools, without relying on model training or manual annotation. The set of type dependency edges is represented as:

[0045] in: This indicates a dependency on the initiating node; , indicating a node of the dependent type. This indicates the type dependency category. For example, when t=1, it indicates parameter type dependency, meaning the function or method node depends on its parameter type node; when t=2, it indicates return value type dependency, meaning the function or method node depends on its return value type node; when t=3, it indicates struct field type dependency, meaning the struct node depends on its field type node; when t=4, it indicates interface implementation dependency, meaning the struct or method node depends on the interface node; and when t=5, it indicates generic constraint dependency, meaning the generic type parameter node depends on its constraint type node.

[0046] The type dependency relationship is used to characterize the propagation relationship between type definition, type reference, interface implementation and generic constraint, and provides a foundation for subsequent structural coupling closure and type complexity calculation.

[0047] By identifying the reference relationships between functions, types, or variables in different files and packages, cross-file dependency edges, cross-package dependency edges, and global variable access edges are constructed, forming cross-file dependency relationships, cross-package dependency relationships, and global variable dependency relationships.

[0048] The cross-file dependency edge is represented as follows: ,in: This indicates the node that initiates the cross-file reference; This indicates a node that is referenced across files; Represents a node The file in question; Represents a node The file in question; and All cross-file dependency edges constitute a set: .

[0049] The cross-packet dependency edge is represented as follows: ,in: This indicates a cross-package reference to the initiating node; This indicates a node referenced across packages; Represents a node Package to which it belongs; Represents a node Belonging to the package; and All cross-package dependency edges constitute a set:

[0050] The global variable dependency edge is represented as follows: ,in: This indicates a node that accesses global variables (generally including function, method, or concurrent structure nodes). , represents a global variable node; This indicates the access method, such as: when m=1, it represents a read access; when m=2, it represents a write access; when m=3, it represents a read-write access. All global variable access edges form a set: .

[0051] The global variable dependencies are used to characterize the impact of shared state on program semantic propagation, and are used for the calculation of data reverse closure in subsequent steps, as well as the quantitative analysis of side effect intensity and data flow complexity. Write access and read-write access are used to identify potential state modification paths, thereby affecting the expansion of migration unit boundaries and risk assessment.

[0052] Cross-file dependency edges, cross-package dependency edges, and global variable dependency edges record the number of files and packages involved and the cross-file or cross-package propagation path, which are used to evaluate the propagation range of migration units.

[0053] The concurrent structure relationships are obtained through the following method: detecting syntax nodes in the project that involve concurrent structures (generally including goroutine start points, channel creation and communication, select branches, and lock (mutex, RWMutex) usage points). Concurrent dependency edges are constructed, represented as follows: ,in, Indicates the node that initiates the concurrent structure; This represents the target node associated with this concurrent structure; This indicates the type of concurrency relationship, such as: when q = 1, it represents a goroutine startup dependency; when q = 2, it represents a channel send dependency; when q = 3, it represents a channel receive dependency; when q = 4, it represents a select branch dependency; when q = 5, it represents a mutex lock acquisition dependency; and when q = 6, it represents a mutex lock release dependency. All concurrency dependency edges constitute the concurrency dependency edge set. The set of concurrent dependency edges is used to characterize the coupling degree between potential race-state propagation risks and synchronization structure.

[0054] S2. Generate the minimum migration unit All the smallest migration units constitute the set of smallest migration units. : .

[0055] The smallest migration unit This refers to the set of semantic nodes A (A) in the old library API call. In ), a single calling node ( The scope of all dependencies and dependent relationships is represented as follows:

[0056] in, This represents a reachable path along dependent edges, i.e., a propagation path formed by several dependent edges.

[0057] This embodiment provides a specific method for generating the minimum migration unit as follows (those skilled in the art can also choose other known methods): S2a. Obtain the set A of API call nodes from the old library. This set of API call nodes is obtained by filtering call dependency edges in the unified semantic dependency graph. Specifically, when constructing call dependency relationships, function or method nodes belonging to the source library to be migrated are identified based on import paths or module dependency information and marked as target API call nodes; all semantic nodes that meet this marking condition constitute set A. This identification process is automatically completed based on import statement parsing, package path matching, and function call expression analysis in the abstract syntax tree, falling under the category of static program analysis and requiring no manual annotation.

[0058] S2b. For each calling node Calculate its backward reachable set The reverse reachable set can be computed through the following steps: (1) Initialization: , (2) Iteration:

[0059] in: This indicates the existence of a dependency edge. Indicates the number of iterations. This represents a semantic node in the reverse reachable set.

[0060] This formula indicates that: if semantic nodes exist... It already belongs to the current reverse reachable set. And there are data flow dependent edges. This indicates that the node Data propagation to nodes Therefore, it is necessary to make the node Include it in the reverse reachable set. When the condition is met... When the set stops growing, the iteration stops. That is, calling the node Data inverse closure (inverse reachable set) ).

[0061] S2c. For each calling node Calculate its forward reachable set The forward reachable set can be computed through the following steps: (1) Initialization: , (2) Iteration:

[0062] in: This indicates the existence of a dependency edge. Indicates the number of iterations. This represents a semantic node in the forward reachable set.

[0063] This formula states that if a node exists... Belongs to the current forward reachable set And there are data flow dependent edges. This indicates that the node Dependency Nodes The execution result, therefore, will the node Include it in the positively reachable set. When the condition is met... When the set stops changing, the iteration stops. That is, calling the node Forward propagation closure (forward reachable set) ).

[0064] In actual computation, based on call relationships and data flow dependencies, the reverse closure can ensure that all parameter sources, all variable dependencies, and all data assignment paths are included in the migration unit, while the forward propagation closure can ensure that all functions that depend on the API's return results and all semantic nodes on the propagation chain are included in the migration unit.

[0065] Based on type dependency ( This can yield the minimal closed set of structures that includes all structs, interfaces, and generic constraint dependency nodes.

[0066] Preferably, to facilitate subsequent calculation of the risk of circular dependencies, strongly connected components can be expanded. Specifically, the strongly connected components are calculated in the unified semantic dependency graph. If a node in the current minimum migration unit belongs to a strongly connected component, and there are still nodes in that strongly connected component that have not been included in the minimum migration unit, then those nodes are added to the migration unit to avoid the circular dependency structure being broken.

[0067] Preferably, if two minimum migration units have a strong dependency, they can be merged to avoid duplicate migrations and conflicts. Specifically, if two minimum migration units... and satisfy: , Then the execution units are merged: , in: Represents the smallest migration unit Number of nodes; This indicates the number of overlapping nodes between two migration units; This indicates the overlap ratio threshold.

[0068] Preferably, define the element boundary function: This function represents a migration unit. The set of dependency edges between the program and external program nodes. If If dependencies can be isolated through interface abstraction, then the migration unit is considered an independently migrateable unit. Interface abstraction isolation refers to decoupling the internal implementation of the migration unit from the external calling logic through interface definition or an adaptation layer, allowing the migration unit to be replaced independently without affecting external modules. Otherwise, the migration unit is marked as a cross-domain propagation unit (the cross-domain propagation unit can be used as one of the input features for cross-file propagation complexity, cross-package coupling complexity, and data propagation complexity in the subsequent complexity quantification step S3, and as a basis for high-risk priority adjustment during migration scheduling; specifically, cross-domain propagation units can be delayed in execution or prioritized into the proxy layer compatibility path during sorting to reduce the impact on the overall migration process).

[0069] S3. Obtaining the set of migration units Then, for each smallest migration unit Perform multidimensional complex quantification and risk modeling calculations to generate risk scores. All according to Sort the migrations from low to high to build a migration priority queue, which will serve as the priority basis for subsequent migration scheduling.

[0070] The complexity model is calculated based on the structural information and propagation features of the unified semantic dependency graph G=(V,E), as shown in the following formula:

[0071] in, Indicates the first Complexity, express The weight.

[0072] Complexity includes both syntactic level complexity and the complexity of engineering factors such as cross-file propagation, cross-package propagation, type structure, concurrency coupling, and circular dependencies. Syntactic level complexity refers to the calculation of the propagation path depth (i.e., path length) and propagation range (i.e., number of participating nodes) within the smallest migration unit, based on the call relationship and data flow dependency relationship.

[0073] This embodiment lists several specific levels of complexity. Those skilled in the art can also construct other complexities as needed.

[0074] Specifically, it includes: (1) API structural complexity Calculated using the following formula:

[0075] in, As weight, The smallest unit of migration for statistics based on call relationships. The number of semantic nodes involved in It is a chained call hierarchy, that is, in the call graph, from the call node... Derivative edges are invoked along the starting line. The number of consecutive call layers formed (i.e., path length).

[0076] Different weights can be assigned to different types of nodes in a more detailed manner, and those skilled in the art can set the specific weights as needed. Furthermore, the detailed content involved in semantic nodes, such as the number of parameters, the number of generic parameters, the number of interface type parameters, and the number of variable parameters, can be statistically analyzed and assigned different weights. The number of parameters can be obtained by parsing the list of actual arguments in a function or method call expression; the number of generic parameters can be obtained by identifying generic type parameter nodes and their instantiation expressions; the number of interface type parameters can be obtained by analyzing the type system to determine whether the type corresponding to the parameter node belongs to an interface node; and the number of variable parameters can be obtained by parsing the variable parameter flag in the function signature.

[0077] (2) Data stream propagation complexity Calculated using the following formula:

[0078]

[0079] in, Based on data flow dependencies, it is the smallest statistical unit of migration. The longest propagation path length of the semantic nodes involved; This indicates the length of the propagation path, which is the number of dependent edges in the propagation path; ; ; The smallest unit of migration The number of all nodes along the propagation path. This is used to smooth the propagation scale and prevent excessive score growth due to an excessive number of nodes. This item reflects the scope of the impact of parameter adjustments on the system after migration.

[0080] (3) Cross-file propagation complexity The calculation method is as follows:

[0081] in: Represents the smallest migration unit The number of source code files involved; Represents the migration unit The number of cross-file dependency edges between nodes; Used to normalize file size; This is used to reduce the non-linear impact of numerous cross-file dependencies on the complexity score. The more files there are, the larger the scope of engineering modifications required for the migration.

[0082] (4) Cross-package coupling complexity Calculated using the following formula:

[0083]

[0084] in: Represents the smallest migration unit The middle belongs to the first The number of nodes per packet accounts for The proportion of the total number of nodes; Indicates to Summing all involved packets in the process. express The number of packages involved. This reflects the degree of impact of the migration on the module boundary; the more dispersed the packages are, the higher the migration coupling.

[0085] (5) Circular dependence risk factor Calculated using the following formula:

[0086] SCC_size represents the minimum migration unit. The number of nodes contained in a strongly connected component; This represents the summation over all strongly connected components. The squared term is used to amplify the risk impact of large-scale circular dependencies.

[0087] (6) Type structure complexity Calculated using the following formula:

[0088] in, Indicates the number of fields in the structure. Indicates the depth of nested structures. Indicates the number of interface implementations. Indicates the number of generic instantiations. Type structure complexity is used to characterize the cost of type adaptation.

[0089] (7) Concurrent structure complexity Calculated using the following formula:

[0090] in, This indicates the number of goroutine startup statements. Indicates the number of times the channel sends and receives data. Indicates the number of mutex lock operations. This indicates the number of branches in the select statement. This item reflects the potential risks of concurrent semantic migration.

[0091] S4. Based on the migration priority queue, migration units with lower risk are migrated first, and the migration status is recorded.

[0092] The present invention provides a specific migration process as follows. Those skilled in the art can also use other existing technologies (such as the Big Prophet model) to perform the migration.

[0093] Specifically, based on the nodes marked as old library API calls in the unified semantic dependency graph G=(V, E) The process involves locating the specific code position of the target library within the source code file; then, based on the function signature, parameter structure, and calling method of the target library's API, replacing the old library's calling code and generating migration code accordingly.

[0094] Replacement and code generation are achieved through a program transformation method based on Abstract Syntax Trees (ASTs). Specifically, this includes: locating the calling expression nodes corresponding to the old library API calls within the AST; rewriting the AST nodes according to predefined API mapping rules (including function name mapping, parameter rearrangement rules, type conversion rules, and return value adaptation rules); the rewriting process includes: function identifier replacement, argument list reconstruction, type node replacement, and return value receiving structure adjustment; and then regenerating the source code from the modified AST. This process falls under the category of compiler front-end code rewriting or program transformation techniques and can be automatically implemented using existing syntax parsing and code generation tools without relying on large language models. In cases where rules cannot be fully matched, template-driven code generation can be used to generate adapted code snippets, thus ensuring the controllability and repeatability of the migration process.

[0095] The migration code includes: updating import paths, replacing API call function names, adjusting parameter structures, adapting return value types, and necessary error handling logic. The generated migration code will be written to a temporary project copy for subsequent verification. Simultaneously, the migration state table (State) will be initialized. The migration status table records the current migration status of each smallest migration unit. The statuses include: Pending (the migration unit has not yet started migrating); Migrating (the migration unit is currently performing the migration process); Success (the migration unit has successfully migrated and passed the verification process); Shimmed (the actual migration failed but was completed using a proxy layer compatibility method); and Failed (the migration unit failed and cannot be repaired by the proxy layer). When performing migration scheduling, the system dynamically updates the status information of each migration unit based on the migration status table.

[0096] S5. To ensure the compilability and runnability of the migration results in a real engineering environment, proceed to the testing and verification process.

[0097] This step performs layered verification of the migration units to ensure that the migration results are not only syntactically correct but also meet the constraints of the engineering operation.

[0098] Specifically, this includes: (1) Building the verification environment. Before performing migration verification, the system builds an isolated verification environment, including: creating a temporary working copy; injecting the code changes after migration; retaining the original build configuration of the project; updating the go.mod dependency information; and clearing the cache (goclean). The verification environment is logically isolated from the main project environment to avoid the migration that fails verification from affecting the overall codebase.

[0099] (2) Minimum Compilable Unit Generation. After generating the migration code in step S4, the system further constructs the minimum compilable unit to ensure that the migration code can independently participate in the build process. The minimum compilable unit does not generate a complete business implementation, but rather supplements the minimum context dependencies necessary for the migration unit to participate in compilation. Specifically, this includes: completing import paths, supplementing necessary type references, generating minimum compilable function or method encapsulations, automatically generating the test entry file (*_test.go), supplementing the initialization statements required for compilation, and the minimum dependency stubs, such as mock objects or minimum initialization logic. The dependency stubs are only used to satisfy type checking, symbol resolution, and construction constraints, and do not require the implementation of complete business semantics. The above completion process is automatically completed based on the nodes, type information, and dependency relationships in the unified semantic dependency graph, and its effectiveness is verified through subsequent compilation verification steps. Wherein: the minimum compilable function refers to a function or method structure that can be recognized by the Go compiler and participate in the compilation process; the dependency stub refers to the minimum placeholder implementation generated to satisfy compilation dependencies, such as interface mock objects or initialization code. This step ensures that the migration unit can independently participate in the build process without the need for manual supplementation of missing dependencies.

[0100] (3) Compilation-level verification. Execute the compilation command: gobuild . / ..., and capture the following error types: type mismatch error; undefined symbol error; import path error; method signature mismatch; generic instantiation error. If a compilation error exists, log the error and return it to the scheduling module for processing.

[0101] (4) Unit test verification. Execute: gotest . / ... to verify the test files involved in the migration unit. The test verification content includes: consistency of functional return values; consistency of error handling logic; boundary condition coverage; panic capture behavior; consistency of concurrent execution results (if applicable). The test results serve as the basis for judging the consistency between the actual performance and behavior of the migration.

[0102] (5) Static analysis verification. Execute static analysis tools, including but not limited to: goget, staticcheck. govulncheck (optional). Detects: potential nil reference risks; unhandled errors; unsafe conversions; resource leak risks; concurrency security issues. Static analysis is used to identify structural vulnerabilities that cannot be exposed during the compilation phase.

[0103] (6) Concurrency safety verification. When the migration unit involves concurrent structures, execute: gotest -race Used to detect: data races, asynchronous access, and concurrent write conflicts. This step ensures that concurrent semantics are not broken after migration.

[0104] The system organizes the verification results into a structured record, including: compilation status, test status, number of static analysis alarms, concurrent detection results, error type and location information, and failure file path and line number.

[0105] The verification log is used to: determine whether the migration was successful; update the execution status of the scheduling module; and provide statistical data for the migration report generation module.

[0106] This testing and verification process has the following engineering characteristics: automation, requiring no manual intervention; layered verification: compilation → testing → static analysis → concurrent detection; scalability, with configurable verification strategies; isolation, with the verification environment separated from the main project; and logging, with complete logs for each migration.

[0107] Through the above steps, the migration process is upgraded from "code replacement" to "engineering verification-driven migration execution process", ensuring that each migration unit undergoes a complete verification process before entering the main project.

[0108] S6. Migration Scheduling.

[0109] Migration success criteria. A migration unit is considered to have been successfully migrated only if all of the following conditions are met: compilation passes, all unit tests pass, static analysis shows no serious warnings, and if concurrent structures are involved, race condition detection passes.

[0110] S6a. When Once all tests and verifications pass and the migration is deemed successful, the affected subgraph region is reconstructed. Set up migration unit The corresponding node set is V( Then, the affected subgraph region is defined as: , in: Represents a set of nodes There exists a set of adjacent nodes connected by dependent edges; This represents the set of dependency edges between these nodes.

[0111] The system deletes nodes related to the old library. It removes the set of API call nodes belonging to the old library from the node set V, along with the dependency edges associated with those nodes. The API call structure is then replaced. The target library API node set after migration is added to the node set V, and new dependency edges are re-established based on the migrated code structure. Complexity metrics and risk scores are recalculated only for this subgraph region to ensure that complexity calculations are based on the latest project status. The migration priority queue is then updated, and the process returns to step S4.

[0112] To prevent scheduling oscillations caused by frequent updates, the system can be configured with the following constraints: minimum risk change threshold, maximum number of queue reorderings in a single operation, and a local update caching mechanism. This mechanism ensures the stability and predictability of migration scheduling.

[0113] S6b. If If the test verification fails and the system enters proxy layer compatibility mode, then: mark the State( If the proxy layer is shimmed, it does not perform complexity decay or update the risk of neighboring units, and returns to step S4 to continue migrating other units. The reason is that the proxy layer only adapts and encapsulates the old library calls, but does not eliminate the old library dependencies. Therefore, the overall dependency structure and complexity model will not change fundamentally.

[0114] The proxy layer compatibility mode is as follows: When a certain migration unit If a project fails compilation verification, unit testing, or concurrency safety checks under a real migration path, the system analyzes the failure reasons based on verification logs and a complexity model. If the system detects the following structural adaptation issues: incompatible API parameter structures, differences in resource lifecycle management methods, differences in concurrency semantics implementation, or differences in external resource dependency behavior, the system determines that the problem is a structural difference that cannot be resolved by simple code replacement in the short term. In this case, the system does not terminate the overall migration process but enters the interface proxy adaptation mechanism (Shim / AdapterLayer). The purpose of this mechanism is to achieve the functional conversion from the old library to the target library through an intermediate adaptation layer without changing the caller's code structure, thereby ensuring that the system is still compileable and runnable.

[0115] Proxy layer generation trigger conditions. Proxy layer generation can be triggered when any of the following conditions are met: API signature differences lead to large-scale call point reconstruction; the target library initialization method has structural differences from the old library; the migration unit involves complex concurrency semantics and cannot be safely rewritten in the short term; the migration involves differences in the lifecycle of external resources; the unit complexity exceeds the preset migration budget. The system comprehensively determines whether to enable the proxy layer based on verification logs and the complexity model.

[0116] The proxy layer is an adaptation module located between the old library's API call and the target library's implementation. Its role is to maintain the original API interface unchanged while internally calling the target library's implementation and performing necessary data conversions and behavior adaptations. It adopts the following structure: keeping the old library's function signature unchanged, internally calling the target library's implementation, performing parameter conversion, performing return value structure mapping, unifying error types, and handling resource lifecycle differences. Structure diagram: Old call → Proxy layer interface → Target library call → Result mapping → Return to caller. The proxy layer exists as an independent package and is integrated by modifying the import path.

[0117] The proxy layer automatically generates the following adaptation logic: parameter structure mapping; default value filling; type conversion; error object wrapping; and context object bridging. For complex structure mappings, the system can use a field-by-field mapping method and retain comments explaining the mapping relationships.

[0118] If the target library differs from the old library in its initialization or resource release mechanism, the proxy layer is responsible for: encapsulating the initialization logic; injecting the necessary context; unifying the resource closing method; and ensuring that the caller is unaware of lifecycle changes. For example, if the old library does not require explicit closing, but the target library requires a Close() operation, the proxy layer is responsible for registering unified closing logic.

[0119] If the migration unit involves differences in concurrent structure, the proxy layer can: add lock protection; wrap channel operations; ensure that goroutine behavior is consistent with the old semantics; and control the context passing method. This step is used to prevent race-state behavior differences after migration.

[0120] After the proxy layer is generated, the system re-executes the verification process, including: compilation verification; unit testing; static analysis; and concurrency detection. Only when the proxy layer solution passes verification is it marked as: State( = Shimmed.

[0121] Migration units marked as Shimmed: remain in the migration unit set; do not trigger complexity decay; can be re-evaluated in subsequent scheduling phases. As the dependency structure is further simplified or the neighborhood risk decreases, the system can attempt real migration again, gradually removing the proxy layer.

[0122] The system generates the following for each proxy layer: mapping documentation; parameter difference records; behavior difference descriptions; and adaptation logs. These are used for subsequent manual auditing or refactoring optimization. The mapping documentation, parameter difference records, behavior difference descriptions, and adaptation logs are synchronously recorded and automatically generated by the system during the proxy layer generation process. The generated content includes at least: the parameter correspondence between the old and new interfaces, default value filling rules, type conversion relationships, return value adaptation relationships, resource lifecycle processing logic, and concurrency semantic adaptation strategies. Preferably, the automatically generated results support manual auditing and correction for subsequent refactoring optimization and migration tracking.

[0123] This proxy layer compatibility mechanism has the following engineering characteristics: it does not block the overall migration process; it ensures the continuous buildability of the system; it supports gradual replacement; it is decoupled from the risk scheduling mechanism; and it can exist as a transition layer for a long time or be eventually removed.

[0124] S6c. If the migration unit If multiple consecutive tests fail and the unit does not enter the proxy layer, then: mark it as Failed and temporarily freeze the unit, return to step S4, and continue to schedule other migration units to ensure that a single point of failure will not block the overall migration progress.

[0125] S6d. The scheduling loop terminates when any of the following conditions are met: all migration units are in the Success or Shimmed state; only high-risk frozen units remain in the queue; or the preset migration budget or iteration limit is reached.

[0126] Preferably, after each successful migration, the system records the following statistical indicators: current average risk value, current maximum risk value, and number of remaining migration units. Based on these statistical values, the system generates a risk convergence curve, which allows observation of whether the overall migration risk gradually decreases as the migration progresses.

[0127] This adaptive migration scheduling algorithm possesses the following engineering characteristics: local recalculation, recalculating complexity only for the affected subgraph; backtrackability, with state records for each migration; pluggability, allowing dynamic adjustment of complexity model weights; and asymptotic convergence, where a successful migration reduces neighborhood risk. Through these steps, an engineering-oriented adaptive migration scheduling system is formed, transforming the migration process from static sorting to a dynamic feedback-driven asymptotic optimization process.

[0128] S7. Recording and Quantitative Evaluation of Migration Results.

[0129] After the migration scheduling process is completed or a phased migration node is reached, the system performs convergence analysis and quality assessment on the entire migration process. This step is used to measure the migration execution effectiveness, risk evolution trend, and project stability, providing a basis for subsequent optimization decisions.

[0130] This includes: migration status statistics, risk evolution trend analysis, traceable migration report generation, and engineering optimization suggestion output.

[0131] Through the above steps, the migration process not only has the ability to execute, but also the ability to evaluate and continuously optimize, upgrading library migration from a one-time code replacement behavior to a measurable and convergent engineering transformation process.

[0132] Example 2: Furthermore, this embodiment of the invention also proposes a Go language library migration system based on semantic dependency graphs and complexity scheduling, such as... Figure 2 As shown, the system includes a unified semantic dependency graph module G = (V, E) and a minimum migration unit module. The module includes a multidimensional complex quantification and risk modeling module, a unit migration module, a testing and verification module, a migration scheduling module, and a result recording and quantitative evaluation module.

[0133] The unit migration module performs migration on lower-risk units according to the migration priority queue and records the migration status.

[0134] Furthermore, this embodiment is merely a basic description of the Go language library migration system based on semantic dependency graph and complexity scheduling of the present invention. Technical details not described in detail in this embodiment can be found in the methods provided in any embodiment of the present invention, and will not be repeated here.

[0135] Example 3: Those skilled in the art will clearly understand that the systems and methods of the above embodiments can be implemented using software plus necessary general-purpose hardware platforms. Of course, they can also be implemented using hardware, but in many cases, the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, node packaging device, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0136] Therefore, the present invention also provides a Go library migration device based on semantic dependency graph and complexity scheduling, including: a memory, a processor, and a Go library migration program based on semantic dependency graph and complexity scheduling stored in the memory and executable on the processor. The Go library migration program based on semantic dependency graph and complexity scheduling is configured to implement a Go library migration method based on semantic dependency graph and complexity scheduling.

[0137] In addition, the present invention also provides a storage medium on which a Go language library migration program based on semantic dependency graph and complexity scheduling is stored.

[0138] In reality, when deploying equipment or programs, a program may execute all steps, or it may execute only one step, with multiple programs working together to achieve the full process. Therefore, the Go library migration program based on semantic dependency graph and complexity scheduling, when executed, implements all or some of the processes in the Go library migration method based on semantic dependency graph and complexity scheduling.

[0139] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification, or direct or indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A Go library migration method based on semantic dependency graphs and complexity scheduling, characterized in that, Includes the following steps: S1. Obtain the source code and generate a unified semantic dependency graph G = (V, E), where: V represents the set of semantic nodes, and E represents the set of dependencies; the semantic node refers to the node used to express or carry semantic information in the syntactic structure of the code, and the dependency refers to the relationship in the code project where one semantic node uses another semantic node in some operations; S2. Generate the minimum migration unit The smallest migration unit This refers to a single call node within the semantic node set of the legacy library API calls. The scope of all dependencies and relationships is represented as follows: ,in, Represents semantic nodes. For serial number, This represents a reachable path along dependent edges, i.e., a propagation path formed through several dependent edges; S3. For each smallest migration unit Perform multidimensional complex quantification and risk modeling calculations to generate risk scores. All the smallest migration units Risk Score Sort the data from lowest to highest priority and construct a migration priority queue. S4. Based on the migration priority queue, migration units with lower risk are migrated first, and their migration status is recorded; S5. Conduct testing and verification procedures; S6. Migration Scheduling.

2. The Go library migration method based on semantic dependency graph and complexity scheduling as described in claim 1, characterized in that, Proceed to step S7. Record and quantify the migration results.

3. The Go library migration method based on semantic dependency graph and complexity scheduling as described in claim 1, characterized in that, In step S2, strong connected component expansion is performed. If a node in the current minimum migration unit belongs to a strong connected component, and there are nodes in the strong connected component that have not been included in the minimum migration unit, then these nodes are added to the minimum migration unit.

4. The Go library migration method based on semantic dependency graph and complexity scheduling as described in claim 1, characterized in that, In step S2, if the number of overlapping nodes between two minimum migration units reaches a threshold, they are merged.

5. The Go library migration method based on semantic dependency graph and complexity scheduling as described in claim 1, characterized in that, In step S2, the element boundary function is defined. ,in, Represents a semantic node, if If the dependencies in the migration unit can be isolated through interface abstraction, then the migration unit is considered an independent migration unit; otherwise, the migration unit is marked as a cross-domain propagation unit.

6. The Go library migration method based on semantic dependency graph and complexity scheduling as described in claim 1, characterized in that, Risk Score Calculated using the following formula: in, Indicates the first Complexity, express The weight of the complexity includes both syntactic complexity and the complexity of cross-file propagation, cross-package propagation, type structure, concurrency coupling, and circular dependency engineering factors.

7. The Go library migration method based on semantic dependency graph and complexity scheduling as described in claim 1, characterized in that, Step S6 includes: S6a. When Once all tests and verifications pass and the migration is deemed successful, the affected subgraph regions are reconstructed. Delete the old library related nodes and the dependency edges related to the nodes, replace the API call structure, add the target library API node set after migration to the node set V, and re-establish the new dependency edges according to the code structure after migration. Only recalculate the complexity index and risk score for this subgraph region, then update the migration priority queue and return to step S4. S6b. If If the test verification fails, determine whether to enter the proxy layer compatibility mode. If the proxy layer compatibility mode is entered, do not recalculate the complexity index and risk score, and return to step S4. S6c. If the migration unit If multiple consecutive verification tests fail and the proxy layer is not entered, the unit is marked as Failed and temporarily frozen, then the process returns to step S4. S6d. The scheduling loop terminates when any of the following conditions are met: all migration units are in a successful migration status or have actually failed but completed the migration through a proxy layer compatible method; only frozen units remain in the queue; or the preset migration budget or iteration limit is reached.

8. A Go library migration system based on semantic dependency graphs and complexity scheduling, characterized in that, It includes: a unified semantic dependency graph module, a minimum migration unit module, a multidimensional complex quantification and risk modeling module, a unit migration module, a testing and verification module, and a migration scheduling module.

9. A Go library migration device based on semantic dependency graphs and complexity scheduling, characterized in that, include: The system includes a memory, a processor, and a Go library migration program based on semantic dependency graphs and complexity scheduling, which is stored in the memory and can run on the processor. The Go library migration program based on semantic dependency graphs and complexity scheduling is configured to implement the Go library migration method based on semantic dependency graphs and complexity scheduling as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a Go language library migration program based on semantic dependency graphs and complexity scheduling. When executed, the Go language library migration program based on semantic dependency graphs and complexity scheduling implements the Go language library migration method based on semantic dependency graphs and complexity scheduling as described in any one of claims 1 to 7.

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