Intelligent auxiliary system for software development

Abstract

The invention relates to the technical field of computer software, and discloses a software development intelligent auxiliary system which comprises a terminal subsystem, a server side, a storage system and an operation environment. The terminal subsystem analyzes the code change and extracts a structural feature vector and a key variable symbol; the server receives the feature vectors and executes hierarchical retrieval, and performs performance prediction by using homomorphic historical function data or dynamic acquisition data; when the data is missing, the server triggers the acquisition probe in the operating environment to perform on-demand sampling through the signaling controller; the calculation core combines the algorithm complexity and the operation cardinal number, calculates the predicted time consumption and maps the predicted time consumption to the full-link topology to evaluate the global risk. According to the method, code logic is mapped into vector space features, code performance deduction in a deployment-free environment is realized, and in combination with a dynamic closed-loop acquisition mechanism, resource loss of a production environment is reduced while data integrity is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of computer software technology, specifically to an intelligent auxiliary system for software development. Background Technology

[0002] With the widespread adoption of microservice architecture and cloud-native technologies, the complexity of modern enterprise application systems is increasing, and the call chains between services are becoming increasingly intricate. During software development, the impact of code changes on overall system performance is often difficult to accurately assess through static code analysis or local unit testing. Developers typically need to commit code to a version control system, build and deploy it to a test or pre-release environment, and then perform stress tests or integration tests to obtain actual runtime performance data. This delayed feedback mechanism prolongs the development iteration cycle, causing performance defects to often only be discovered in the later stages of the development process or even after deployment, significantly increasing the cost of fixing them.

[0003] Existing performance evaluation methods primarily rely on historical data collected by application performance monitoring (APM) tools. However, conventional APM systems typically aggregate data based on defined service interfaces or function signatures. When developers write entirely new business logic or significantly refactor existing functions, the lack of directly corresponding historical execution records prevents the system from providing effective performance references. While some static analysis tools can calculate cyclomatic complexity or lines of code, these static metrics cannot reflect the actual time consumption caused by database size, network latency, or resource contention in a real production environment, resulting in a disconnect between the logical structure at the code level and the performance at the runtime level.

[0004] Furthermore, to achieve accurate performance prediction, it is often necessary to obtain specific variable distributions or parameter characteristics in the production environment. However, continuously enabling full and in-depth parameter-level data collection in the production environment consumes a significant amount of computing resources and network bandwidth, severely impacting the throughput of business systems. Existing monitoring systems typically employ fixed sampling rates or coarse-grained statistical strategies, making it difficult to strike a balance between low-overhead monitoring and high-precision data acquisition. This often leads to the predicament of missing key dimension data when fine-grained analysis of specific code changes is required. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent software development assistance system that solves the problems of existing technologies being unable to accurately predict the performance of newly written code lacking direct historical records in non-deployment environments, and the difficulty in balancing high-precision runtime data acquisition with production environment resource consumption.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides an intelligent auxiliary system for software development, which includes a terminal subsystem, a server, a storage system, and an operating environment.

[0007] The terminal subsystem is configured to listen for code change events in the computing device, and use an internally deployed parsing engine to read the source code of the changed function through an incremental parsing algorithm and construct an abstract syntax tree;

[0008] The terminal subsystem identifies control flow statements and data flow operations by performing a depth-first traversal of the abstract syntax tree, and extracts topological features that characterize the complexity of the code logic.

[0009] The topological features include control flow nesting depth, number of conditional branches, cumulative count of input and output operations, cumulative count of loop structures, and normalized local sensitivity hash value of operation sequence.

[0010] The terminal subsystem multiplies each of the extracted topological features by a preset dimension weight coefficient to generate a multidimensional numerical structural feature vector, and parses out the symbols of key variables that affect the scale of input and output.

[0011] The storage system is configured to store distributed link tracing data for applications, which includes historical function time statistics and variable cardinality distribution.

[0012] The server is configured to receive structural feature vectors and method signatures from the terminal subsystem, and execute a hierarchical retrieval strategy using its internal indexing engine.

[0013] First, the method signature is used to search for historical tracking data in the precise index established in the storage system;

[0014] If the exact search fails or the sample size does not meet the preset threshold, the structural feature vector is used to perform an approximate nearest neighbor search in the vector index library, calculate the Euclidean distance between the change function and the historical function in the multidimensional vector space, and select the preset number of historical functions with the smallest Euclidean distance as the set of similar historical functions.

[0015] When the minimum Euclidean distance is less than the dynamic similarity threshold, the inverse distance weighted interpolation method is used, with the reciprocal of the Euclidean distance as the weight, to perform a weighted summation of the measured performance indicators of the similarity history function to obtain the benchmark performance data.

[0016] If the minimum Euclidean distance does not meet the dynamic similarity threshold, then the component-level global baseline data is called for backfilling.

[0017] The server is also configured to generate dynamic acquisition signaling in response to data missing status.

[0018] When the server determines that there is tracking data but the cardinality record corresponding to the key variable symbol is missing, or the difference between the collection timestamp of the existing sample and the current time exceeds the timeliness threshold, the signaling controller generates the dynamic collection signaling that includes the target filter, sampling strategy, data collection level and effective timeliness.

[0019] The operating environment is equipped with a collection probe, configured to receive the dynamic collection signaling and use bytecode re-conversion technology or dynamic mounting technology to sample the parameters of the class method specified by the target filter;

[0020] The acquisition probe has built-in adaptive backpressure logic. It periodically reads the CPU utilization and memory utilization of the host machine and calculates the load correction coefficient. Based on this, it dynamically adjusts the actual execution probability of the dynamic acquisition signaling and sends the acquired runtime data to the storage system.

[0021] The computing core within the server is configured to calculate the latency increment of a single call based on retrieved historical data or collected data sent by the operating environment, combined with the algorithm complexity coefficient obtained from the abstract syntax tree analysis, and then input into a nonlinear prediction model. It also calculates a resource contention penalty based on a queuing theory model, and obtains the predicted time consumption by superimposing the latency increment and the resource contention penalty.

[0022] The computing core is also configured to perform full-link impact analysis, mapping the predicted time consumption to a directed acyclic graph constructed by fusing static call relationships and dynamic link tracing data, recursively calculating the expected total response time of each node and identifying critical paths, and generating a risk score by comprehensively considering the relative performance degradation rate, the scope of topology cascading impact, and data confidence.

[0023] A second aspect of this invention provides an intelligent assistance method for software development, comprising the following steps:

[0024] The terminal subsystem detects code change events, uses an incremental parsing algorithm to construct an abstract syntax tree, identifies the changed logic blocks and their structural features, encodes the structural features into structural feature vectors, and extracts key variable symbols.

[0025] The server receives a request containing the method signature and structural feature vector and performs hierarchical data retrieval: it prioritizes using the method signature to find the exact corresponding historical tracking data;

[0026] If the search fails, the structural feature vector is used to perform a homomorphic search in the vector index library to match historical functions with similar structures, and its cardinality distribution and time consumption features are extracted as backfill data.

[0027] If the homomorphic search results do not meet the preset similarity threshold, then the component-level global baseline data will be called for backfilling.

[0028] During the data retrieval process, if the server determines that the cardinality record of the key variable is missing, it generates a dynamic collection signaling that includes the class name, method name and parameter index.

[0029] After receiving the dynamic acquisition signal, the acquisition probe in the runtime environment opens a sampling window for key variables and sends the acquired runtime values ​​to the storage system, which then updates the variable cardinality projection based on these values.

[0030] The server combines the algorithm complexity coefficient obtained from the analysis of the abstract syntax tree and the obtained variable projection cardinality, uses a nonlinear prediction model to calculate the latency increment of a single call, and calculates the resource contention penalty term based on queuing theory, thereby obtaining the predicted time consumption.

[0031] In this process, confidence weights are applied to the calculation results according to the hierarchy of the data source;

[0032] The server maps the predicted latency to the reverse call link topology of the service, analyzes the impact of latency increment on the total response time of the upstream entry node, calculates a risk score including performance degradation rate, scope of impact and uncertainty penalty, and sends the simulation results and risk alarms back to the terminal subsystem.

[0033] This invention provides an intelligent auxiliary system for software development. It has the following beneficial effects:

[0034] 1. This invention transforms unstructured logic such as the nesting depth of code control flow, the number of branches, and the operation sequence into multi-dimensional structural feature vectors, and performs an approximate nearest neighbor search in the vector index library. It can use runtime statistics of historical existing code to perform performance extrapolation on newly written code, overcoming the limitation of traditional performance testing that must rely on actual deployment and stress testing environments. This allows developers to obtain statistically significant time predictions during the code editing stage, enabling early detection of performance risks.

[0035] 2. This invention establishes a closed-loop signaling feedback mechanism between the server and the operating environment. Dynamic acquisition signaling is triggered only when the cardinality record of key variables is missing or the timeliness of the samples is insufficient. Combined with the adaptive backpressure logic built into the acquisition probe, the sampling probability is dynamically adjusted according to the CPU and memory load of the host machine. This avoids the continuous resource consumption caused by traditional full monitoring to the production environment and realizes the accurate and timely completion of key runtime data for performance simulation on demand under the premise of minimizing business intrusion.

[0036] 3. This invention constructs a full-link topology that integrates static call relationships and dynamic link tracing data, maps the predicted time of a single node to the global service network for recursive deduction, and comprehensively evaluates the relative performance degradation rate, the scope of topology cascading impact, and the confidence level of data sources. It can accurately identify hidden global bottlenecks caused by local code changes and effectively prevent the response time of upstream entry nodes from exceeding the service level agreement threshold due to the accumulation of latency increments of a single underlying function. Attached Figure Description

[0037] Figure 1 This is a system architecture diagram of the present invention;

[0038] Figure 2 This is a flowchart of the method of the present invention.

[0039] Among them, 110 is the terminal subsystem; 120 is the server; 121 is the index engine; 122 is the computing core; 123 is the signaling controller; 130 is the storage system; 140 is the operating environment; and 141 is the acquisition probe. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Example:

[0042] Please see the appendix Figure 1 This invention provides a software development intelligent auxiliary system, which includes a terminal subsystem 110, a server 120, a storage system 130, and a runtime environment 140.

[0043] Terminal subsystem 110 is deployed in a computing device and is equipped with a code editor and a syntax parsing engine. Terminal subsystem 110 is configured to listen for code change events, construct an abstract syntax tree of the changed function using an incremental parsing algorithm, and extract the structural feature vectors and key variable symbols of the changed parts. Terminal subsystem 110 connects to server 120 to send prediction requests and receive inference results.

[0044] The server 120 is a computing processing unit, which internally includes an index engine 121, a computing core 122, and a signaling controller 123. The index engine 121 is configured to maintain a vector index library of code structure and perform approximate nearest neighbor searches on structural feature vectors. The computing core 122 is configured to perform time-consuming prediction algorithms, weighted summation calculations, and resource contention penalty calculations based on mathematical models. The signaling controller 123 is configured to generate dynamic configuration descriptors and manage the signaling negotiation process with the runtime environment 140.

[0045] Storage system 130 is connected to server 120 and configured to store link tracing data collected from the production environment. Storage system 130 supports histogram statistics and quantile calculations for tag attributes, providing distribution data of variable cardinality.

[0046] A data acquisition probe 141 is deployed within the runtime environment 140. The data acquisition probe 141 is configured to intercept and acquire runtime data from the application and send the data to the storage system 130. The data acquisition probe 141 is also configured to receive configuration instructions from the signaling controller 123 and perform temporary sampling on the parameters of specified class methods using bytecode re-translation technology or dynamic mounting technology.

[0047] See attached document Figure 2 This invention provides an intelligent assistance method for software development, comprising the following steps:

[0048] S1. The terminal subsystem 110 detects code change events, the parsing engine reads the changed code fragments and constructs an abstract syntax tree, the terminal subsystem 110 identifies the changed logic blocks and their structural features in the abstract syntax tree, including the control flow nesting depth, the number of conditional branches and the operation sequence, encodes the structural features into structural feature vectors, and parses out the set of key variable symbols that affect the scale of input and output.

[0049] S2. The server 120 receives a request containing a method signature and a structural feature vector. First, it uses the method signature to search for the exact corresponding historical tracking data in the storage system 130. If the exact search is not found, the index engine 121 uses the structural feature vector to perform a homomorphic search in the vector index library to match the historical function with the highest structural similarity, and extracts the cardinality distribution and time consumption characteristics of the historical function as backfill data. If the homomorphic search does not meet the preset threshold, it calls the component-level global baseline data for backfilling.

[0050] S3. During the data retrieval process in step S2, if the server 120 determines that there is tracking data but the cardinality record of key variables is missing, the signaling controller 123 generates a dynamic acquisition signaling containing class name, method name and parameter index. After the acquisition probe 141 receives the signaling, it opens a sampling window for key variables. After the sampled runtime values ​​are fed back to the storage system 130, the server 120 updates the variable cardinality projection.

[0051] S4. The computational core 122 combines the algorithm complexity coefficient obtained from the abstract syntax tree analysis and the variable projection cardinality obtained from step S2 or step S3, substitutes the data into the preset nonlinear prediction model, calculates the latency increment of a single call, and calculates the resource contention penalty term based on queuing theory to obtain the prediction time. During the calculation process, different confidence weights are applied to the results according to the level of the data source.

[0052] S5, server 120 maps the predicted latency to the reverse call link topology of the service, calculation core 122 analyzes the impact of latency increment on the total response time of the upstream entry node, determines whether the service level agreement preset threshold is exceeded, and sends the simulation results and resource bottleneck warning information back to terminal subsystem 110 for rendering and display.

[0053] The core principle of this process lies in converting unstructured code text logic into numerical coordinates in a multidimensional Euclidean space, thereby transforming complex code semantic similarity judgments into geometric distance calculations in a vector space. In this embodiment, the terminal subsystem 110 resides in the integrated development environment as a plugin, capturing code editing events using its internal file system listening interface. This process specifically involves the following steps:

[0054] Step S11: Code change capture and incremental parsing. The terminal subsystem 110 is configured with a preset time window threshold. (For example, 500 milliseconds to 2000 milliseconds), analysis begins when keyboard input stops for more than this threshold or a file save action is triggered. The parsing engine reads the current source code file content and constructs an abstract syntax tree through lexical and syntactic analysis.

[0055] To reduce computational overhead, the parsing engine employs an incremental solution algorithm, analyzing only the modified code blocks and their affected parent and child nodes to update the syntax tree structure. This is done by comparing snapshots of the syntax tree before and after the change. and The tree edit distance algorithm is used to calculate and locate the set of changed nodes containing update, modification, or deletion operations. The general framework principles of object syntax rules and the basic algorithms for incremental parsing are well-known to those skilled in the art and will not be elaborated upon here.

[0056] Step S12: Logical structure topology traversal. This involves traversing the set of changed nodes. For each function or method body involved, the terminal subsystem 110 performs a depth-first traversal to extract topological features that characterize the complexity of the code logic. During the traversal, the parsing engine maintains a state machine that can identify the type of AST node, and identifies and counts control flow statements and data flow operations. This counting process is independent of the code's indentation format or comment content, and only focuses on syntactic units that have a substantial impact on the program execution path.

[0057] Step S13: Structural Feature Vector Encoding. The terminal subsystem 110 maps the topological features extracted in step S12 into a multi-dimensional numerical vector. Since the feature values ​​of different dimensions differ significantly in magnitude, a normalization coefficient and weight adjustment are introduced to ensure a balanced contribution of each dimension to the similarity calculation. The final generated structural feature vector... The definition is as follows:

[0058] ;

[0059] In this formula, , is a preset dimension weight coefficient, with a value range of (0,1], used to adjust the retrieval sensitivity according to the application scenario.

[0060] The definitions and calculation methods of each vector component are as follows:

[0061] This represents the maximum nesting level of the control flow, which is obtained by calculating the longest path length from the control node to the leaf node of the basic block in the AST, and is used to characterize the upper limit of the algorithm's time complexity.

[0062] It represents the total number of branches related to cyclomatic complexity, and counts the cumulative number of all condition judgment nodes in the function body, used to characterize the diversity of logical paths;

[0063] This represents the cumulative count of input and output operations. The parsing engine identifies I / O nodes by matching predefined I / O library function signatures, which is used to characterize the strength of external dependencies.

[0064] This represents the cumulative count of the loop structure, counting the number of all iteration nodes.

[0065] This represents the local sensitive hash value of the normalized operation sequence. Its generation logic is as follows: the parsing engine extracts the opcode sequence from the function body, removes variable name strings and constant values, and retains only the operation instructions;

[0066] Apply the SimHash algorithm to the extracted sequence to generate A binary fingerprint is generated; this binary fingerprint is converted to a decimal value and scaled to the [0,1] range. This process allows code segments that have undergone variable renaming or no substantial logical change to calculate a higher-order binary fingerprint. Maintaining numerical approximation with minimal ranges supports subsequent homomorphic retrieval.

[0067] Step S14, key variable symbol extraction. While generating the structural feature vector, the terminal subsystem 110 performs semantic analysis on the variables within the change logic block, filtering out a set of key variables that affect the program's execution scale. .

[0068] This screening process is based on static data flow analysis techniques, and the screening rules include:

[0069] Rule 1: Identify all variables that are used as loop boundary conditions or iteration objects;

[0070] Rule 2: Identify all variables that are used as I / O operation parameters and affect the size of the transmitted payload.

[0071] For the selected variables, the parsing engine extracts their symbolic names and data types from the code, and establishes a variable symbol and its associated function signature. The associative mapping. The set output by this step. This will serve as an anchor point for subsequent dynamic data backfilling. Through the above steps, the terminal subsystem 110 converts unstructured code text into a structured digital representation that can be processed by a computer, completing the mapping from qualitative code logic to quantitative numerical features.

[0072] The core concept of this embodiment is based on the manifold assumption of code performance space. That is, in the function space of an application system, code segments with similar topological structures and I / O patterns exhibit locally clustered runtime performance in a high-dimensional space. Based on this, the server 120 can transform the performance prediction problem of unknown code into a spatial interpolation problem of known historical data through a hierarchical retrieval strategy. This process specifically involves the following steps:

[0073] Step S21: Establish a multimodal index structure. Storage system 130 first cleans and aggregates the distributed link tracing data collected from the production environment.

[0074] Storage system 130 constructs two sets of parallel indexes: the first is a precise index based on a hash table, using the hash value generated by the function name as the key, and associated with storing the historical time-consuming histogram, throughput, and resource consumption statistics of the function; the second is a homomorphic index based on a high-dimensional vector space, using the structural feature vector of the historical function as the key. Use the key to build a vector index library that supports nearest neighbor search.

[0075] For the underlying implementation of the vector index library, this example uses a hierarchical small-world graph algorithm and configures the maximum connection number parameter. and search for wide beam parameters This enables millisecond-level approximate nearest neighbor search in massive vector data.

[0076] Step S22: Exact Matching Layer Retrieval. After receiving the prediction request from the terminal subsystem 110, the server 120 parses out the symbol name of the function to be predicted. Index engine 121 first utilizes The search is performed in the exact index. If the index is hit, the sample size of the historical data is directly verified to determine whether it meets the statistical significance requirement. If it does, it is extracted as the baseline, and the confidence level of the data source is marked. The value is 1.0. If there is no match or insufficient samples, the homomorphic structure mapping process is triggered.

[0077] Step S23: Homomorphic vector space search. When an exact match fails, the index engine 121 reads the structural feature vector carried in the request. Index engine 121 executes in the homomorphic index library -Nearest neighbor search, finding and matching neighbors. Geometrically closest A set of historical function vectors In this process, the Euclidean distance is used as the metric for determining the structural homomorphism of two functions. Its calculation formula is defined as follows:

[0078] ;

[0079] in, This represents the total number of dimensions of the structural feature vectors. and Represent the vector to be predicted and the first vector respectively. The historical vector at the th ... The numerical value of the dimension. The minimum distance obtained through calculation. Compare with the preset similarity threshold Compare. The threshold. It is not a fixed value, but is dynamically determined by the indexing engine periodically calculating the distribution of pairwise distances between all vectors in the database and taking the 10th or 20th percentile of that distribution.

[0080] like If no sufficiently similar historical function exists in the library, the homomorphic search is forcibly terminated, and the process proceeds to step S25 for baseline backfilling.

[0081] Step S24: Homomorphic data weighted projection. For data that meets the threshold condition... The server extracts the performance metrics associated with each of the nearest neighbor historical functions (including average response time). P99 delay and memory allocation rate To accurately map the statistical characteristics of historical data onto the new code, computational core 122 uses inverse distance weighted interpolation to calculate the backfill value. The calculation process is shown in the following formula:

[0082] ;

[0083] in, Indicates the first Measured performance index values ​​of several similar historical functions. To prevent the smoothing coefficient from being zero in the denominator, This represents the power-law exponent of distance decay. Simultaneously, the system labels the confidence level of the data source as... The calculation formula is as follows:

[0084] ;

[0085] in, This serves as the upper limit of the baseline confidence level for homomorphic inference. denoted as the distance penalty coefficient. This formula indicates that the greater the difference in the structure of the matched historical functions, the lower the reliability of the prediction results.

[0086] Step S25: Global baseline backfilling. If no similar function meeting the threshold is found in step S23, the server 120 calls the component-level baseline data. The system first parses the fully qualified class name of the code to be predicted, extracts its prefix package name or parses its build dependency description file to identify the technical component type to which the code belongs. Subsequently, it extracts the average time consumption statistics of this component type in the entire system as backfill data and sets the confidence level. It is marked as a preset low value, which is used as the most conservative performance estimate.

[0087] Through the steps described above, server 120 achieves cross-space mapping from static code structure to dynamic runtime data. This mechanism ensures that even newly written code, as long as its algorithm structure is homomorphic to existing historical code, can generate valuable performance simulation data, thus solving the problem that traditional performance testing must rely on actual deployment and execution.

[0088] This embodiment establishes a closed-loop control system based on a negative feedback mechanism, rather than a one-way configuration distribution mode. Its core principle lies in utilizing the server as a global state observer and dynamically adjusting the acquisition entropy of the runtime environment as the executor. The system aims to find a Nash equilibrium between observation completeness and runtime resource overhead, that is, to acquire telemetry samples sufficient to support statistical confidence while minimizing intrusion into business processes. This process specifically involves the following steps:

[0089] Step S31: Sampling Requirement Detection and Missing Data Determination. The signaling controller 123 in the server 120 continuously monitors data query requests from the terminal subsystem 110. When the prediction model detects the data confidence level of a key function node... Below the preset threshold Or the collection timestamp of existing samples With current time The difference exceeds the preset timeliness threshold. At that time, the signaling controller 123 generates a sampling request event. This event contains the signature identifier of the target code. and the minimum required sample size .

[0090] Step S32: Dynamic configuration signaling generation. The signaling controller 123 converts the sampling requirements into standardized control signaling messages. These messages use a serialization format based on Protocol Buffers or compact JSON, defining the specific behaviors that the acquisition probe should perform. The signaling message contains the following core fields:

[0091] Target filters: Specify that the filter applies only to traffic with specific type qualified names, method signatures, or HTTP URI paths, and support prefix matching based on Trie trees or regular expression matching;

[0092] Sampling strategy: Define the sampling mode, including fixed window counting sampling or token bucket rate limiting sampling;

[0093] Data level: Specifies the collection depth, specifically including: collecting only function execution time and return status code, collecting method input parameters, output parameters and stack depth, and collecting the complete call chain TraceContext and local variable snapshots;

[0094] Effective period: The effective lifespan of this configuration, expressed as a relative time offset, to avoid problems caused by distributed clock asynchrony.

[0095] Step S33: Signaling issuance and runtime reconstruction. The generated signaling message is issued to the acquisition probe 141 deployed in the runtime environment 140 through a full-duplex communication channel. After receiving the signaling, the acquisition probe 141 performs dynamic logic injection without restarting the host process.

[0096] For languages ​​that support bytecode enhancement, such as Java, the acquisition probe 141 uses the retransformClasses method provided by the Instrumentation API, in conjunction with the ASM or Javassist library, to dynamically modify the bytecode instructions of the target class and insert tracking logic.

[0097] For statically compiled language environments such as Go or C++, probes modify atomic Boolean flags pre-installed in the binary file or function pointer jump tables in memory, thereby activating telemetry functionality for specific code paths at runtime. The specific implementation techniques for dynamic bytecode enhancement and probe mounting are well-known to those skilled in the art and will not be elaborated upon here.

[0098] Step S34, Differentiated Data Acquisition and Adaptive Backpressure. During the signaling effective window, acquisition probe 141 performs data capture at a specified level on service requests that hit the filter. The acquired telemetry data is marked to complete data fields and pushed to the data processing pipeline. To strictly control the performance impact of acquisition on the service system, acquisition probe 141 has a built-in adaptive backpressure algorithm based on resource load.

[0099] To mitigate the performance impact of data collection on the business system, the data collection probe 141 incorporates an adaptive backpressure algorithm based on resource load. The probe periodically reads the host machine's CPU utilization. and memory usage The actual sampling probability is dynamically adjusted according to the following formula. :

[0100] ,in ;

[0101] in, The requested original sampling rate in the signal. The safe load threshold (e.g., 60%). This is the critical circuit breaker threshold (e.g., 85%). This is a load correction factor. When the system load approaches a critical value, The sampling process linearly decays to zero, thus automatically tripping the circuit breaker and prioritizing service availability. Discarded sampling tasks will be reported back to the server via status codes.

[0102] Step S35: Closed-loop feedback and model refresh. After receiving the returned complete data, the server 120 injects it into the storage system 130. The signaling controller 123 maintains the counter of effectively acquired samples in real time. .when When the signaling TTL expires, the signaling controller 123 sends a cancellation command to the acquisition probe 141, causing the probe to remove the bytecode stub or reset the Boolean flag, restoring it to a zero-overhead silent state. Simultaneously, the computing core 122 uses the newly acquired measured data to remodel the performance characteristics of the objective function, replacing the previous extrapolated data, and adjusts the confidence level of the node. Updated to version 1.0, completing a full control loop.

[0103] This embodiment is based on the error propagation model in complex network theory, treating the software system as a directed graph composed of nodes (functions or services) and edges (call relationships). The core principle of the system lies in quantifying how the performance uncertainties of local nodes (i.e., the aforementioned dynamic sampling or homomorphic inference results) are linearly superimposed or probabilistically propagated in the entire link topology, thereby calculating the global system entropy increase caused by local code changes. This process specifically involves the following steps:

[0104] Step S41: Hybrid Call Graph Construction. The computing core 122 in the server 120 first parses out the method nodes containing code changes and their static call relationships based on the abstract syntax tree data uploaded from the terminal subsystem.

[0105] Subsequently, the system extracts the corresponding dynamic link tracing data from the storage system 130. Considering that there may be naming differences between the method signature of static code and the span name of runtime tracing, the computing core 122 loads the pre-set mapping rule table and anchors the static AST node to the dynamic topology by matching HTTPURI path mapping, RPC interface definition language annotations or fuzzy string similarity algorithms.

[0106] Then, computational core 122 instantiates a directed acyclic graph. During the construction process, if a loop is detected in the call chain, the system uses a depth-first search to detect back edges and logically disconnects or merges them into a single supernode to enforce the DAG property. Each edge... It is assigned two key attributes: probability of invocation (Based on historical traffic statistics, representing the parent node) Call child nodes (frequency) and network transmission latency .

[0107] Step S42: Full-link recursive deduction of performance indicators. Based on the constructed graph... The system then performs a bottom-up performance expectation calculation. For nodes involving code changes, the system uses the predicted latency obtained through homomorphic mapping or dynamic sampling in the preceding steps. For nodes that have not been changed, the historical baseline latency is directly reused. ,node Response time mathematical expectation Calculated based on the following recursive formula:

[0108] ;

[0109] in, Represents a node The computation time itself (i.e., the pure CPU / IO time after deducting the time spent waiting for the downstream RPC to return); For nodes The set of all directly downstream dependent nodes; The average network round-trip latency is known. This recursive calculation process, based on the weighted average principle, can simulate how the performance degradation of a single node propagates up the call chain, thereby quantifying the cumulative impact of the change on the overall response time of the ingress gateway.

[0110] Step S43: Critical Path and Bottleneck Identification. After completing the full-link time simulation, the critical path analysis of the 122 core rows is performed. The system traverses the graph. Identify the cause The longest call path sequence, if a certain change node Located on the critical path, and it introduces an increase in latency. The contribution of the root node's total time consumption to the total time consumption exceeds the preset bottleneck threshold. (For example, if set to 10% of the total time increment), the system will mark the section as a strong blocking bottleneck.

[0111] Step S44: Multidimensional Risk Score Calculation. To comprehensively assess the risks of code change deployment, server-side 120 introduces a multidimensional risk quantification model. This model considers not only the degree of performance degradation but also data confidence and the scope of impact. Risk Score The calculation formula is defined as follows:

[0112] ;

[0113] The physical meanings of the variables and parameters in this formula are as follows:

[0114] The first term characterizes the relative performance degradation rate, where The total time taken after the simulation. The baseline time before the change, To be the minimum value (to prevent the denominator from being zero), For performance weighting coefficients;

[0115] The second term characterizes the extent of the influence of topological cascades. This indicates the in-degree of the change node in the call graph. A higher in-degree means that the basic component is depended on by more upstream businesses. The weighting coefficient represents the scope of influence.

[0116] The third penalty represents uncertainty. The confidence level (range of values) of the data source calculated in the preceding steps , This represents the uncertainty penalty coefficient.

[0117] The above weighting coefficients , , It is not a fixed constant, but is derived by reverse training based on historical P1 / P2 level fault data of the system through a pre-set linear regression model.

[0118] For example, in latency-sensitive trading systems, It will be trained to a higher value (e.g., 50); however, in basic middleware where stability is extremely important... It will be trained to a higher value (e.g., 40).

[0119] Step S45, graded risk decision output, calculation core 122 will calculate the obtained The results are compared with preset grading thresholds to generate a final evaluation report. The grading criteria set in this embodiment are as follows:

[0120] like (For example, 80 points) is judged as a high-risk change. The system generates a blocking command to prevent the code from being merged into the main branch and pushes a detailed alarm containing the specific bottleneck link to the developer.

[0121] like (For example, a score of 50 to 80) is judged as medium risk, and the system generates a suggested warning, requiring supplementary manual code review or additional unit test coverage;

[0122] like If the risk is deemed low, the system allows the code to proceed to the next stage of the pipeline. The assessment report is returned to the terminal subsystem 110 via the API interface, and the risk composition is displayed in the IDE interface as a visual chart.

[0123] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A software development intelligent auxiliary system, characterized in that, include: The terminal subsystem is configured to listen for code change events, use an incremental parsing algorithm to construct an abstract syntax tree for the changed function, extract the structural feature vector and key variable symbols of the changed part, and send them to the server. The storage system is configured to store the link tracing data of the application. The server is configured to receive the structural feature vector and retrieve historical data from the storage system or an internally maintained vector index library. When historical data is retrieved but the cardinality record corresponding to the key variable symbol is missing, a dynamic acquisition signal is generated and sent to the operating environment. And based on the retrieved historical data or the collected data returned by the operating environment, calculate the predicted time consumption of the change function; The runtime environment is equipped with a data acquisition probe configured to receive the dynamic data acquisition signaling, sample the parameters of a specified class method using bytecode re-conversion technology or dynamic mounting technology, and send the acquired runtime data to the storage system.

2. The intelligent auxiliary system for software development according to claim 1, characterized in that, When extracting the structural feature vector, the terminal subsystem is configured as follows: A depth-first traversal is performed on the abstract syntax tree to count control flow statements and data flow operations; Extract topological features that characterize the logical complexity of the code. These topological features include at least the control flow nesting depth, the number of conditional branches, the cumulative count of input and output operations, the cumulative count of loop structures, and the normalized local sensitivity hash value of the operation sequence. Each extracted topological feature is multiplied by a preset dimension weight coefficient and combined to generate a multidimensional numerical structural feature vector.

3. The intelligent auxiliary system for software development according to claim 1, characterized in that, The server-side application has an indexing engine deployed within it, and the indexing engine is configured to execute a hierarchical retrieval strategy: First, the historical tracking data is searched in the precise index established in the storage system using the method signature of the change function; If an exact match is not found or the sample size does not meet the preset threshold, an approximate nearest neighbor search is performed in the vector index library using the structural feature vector to match a set of historical functions with similar structures.

4. The intelligent auxiliary system for software development according to claim 3, characterized in that, The indexing engine is configured to perform approximate nearest neighbor search as follows: Calculate the Euclidean distance in multidimensional space between the structural feature vector of the changed function and the structural feature vector of the historical function; Select the K history functions with the smallest Euclidean distance as the set of similar history functions; Determine whether the minimum Euclidean distance is less than the dynamic similarity threshold. If the similarity exceeds the dynamic similarity threshold, the search is terminated and component-level global baseline data is called for backfilling. The dynamic similarity threshold is determined based on the quantiles of the distance distribution between vectors in the vector index library.

5. The intelligent auxiliary system for software development according to claim 4, characterized in that, The server is configured to calculate the backfill data using inverse distance weighted interpolation. Extract the measured performance index value of each historical function from the set of similar historical functions; The measured performance index values ​​are weighted and summed using the reciprocal of the Euclidean distance between the structural feature vector of the changed function and the structural feature vectors of each historical function as the weight, to obtain the baseline performance data of the changed function, and the confidence level of the data source is calculated based on the Euclidean distance.

6. The intelligent auxiliary system for software development according to claim 1, characterized in that, The server is equipped with a signaling controller, which is configured as follows: When the cardinality record of a key variable is missing, or the difference between the collection timestamp of an existing sample and the current time exceeds the timeliness threshold, the dynamic collection signaling is generated. The dynamic acquisition signaling includes target filters, sampling strategies, data acquisition levels, and effective time limits; The target filter is used to specify the effective class name or method signature.

7. The intelligent auxiliary system for software development according to claim 6, characterized in that, The acquisition probe has built-in adaptive backpressure logic and is configured as follows: Periodically read the host machine's CPU usage and memory usage; The read resource utilization rate is compared with the preset safe load threshold and critical circuit breaker threshold; The load correction coefficient is calculated based on the comparison results, and the actual execution probability of the dynamic acquisition signaling is dynamically adjusted using the load correction coefficient. When resource utilization exceeds the critical circuit breaker threshold, the actual execution probability will be set to zero.

8. The intelligent auxiliary system for software development according to claim 1, characterized in that, The server-side application has a computing core deployed within it. This computing core is configured to: calculate the predicted timeout for the change function. Combining the algorithm complexity coefficient obtained from the analysis of the abstract syntax tree, and the variable cardinality obtained from the historical data or collected data, the latency increment of a single call is calculated by substituting it into a preset nonlinear prediction model. Calculate resource contention penalty terms based on queuing theory models; The latency increment is superimposed with the resource contention penalty term to obtain the final predicted time, and confidence weights are applied to the calculation results according to the data source level.

9. The intelligent auxiliary system for software development according to claim 8, characterized in that, The computing core is also configured to perform end-to-end impact analysis: The static call relationship is parsed based on the abstract syntax tree, and a hybrid call directed acyclic graph is constructed by combining the link tracing data; The prediction time of the change function is mapped to the corresponding node of the directed acyclic graph; A recursive algorithm is used to calculate the expected total response time of each node from bottom to top, and the critical path that causes the maximum total response time of each node is identified.

10. A software development intelligent auxiliary system according to claim 9, characterized in that, The server is also configured to calculate change risk scores and generate tiered alerts: A risk score is obtained by comprehensively calculating the relative performance degradation rate, the impact range of topology cascading, and the uncertainty penalty term. The relative performance degradation rate is calculated based on the total response time after the simulation and the baseline time before the change. The scope of the topology cascade effect is calculated based on the granularity of the changed node in the call graph; The uncertainty penalty term is calculated based on the confidence level of the data source; The risk score is compared with a preset grading threshold, and a blocking command or advisory warning is sent to the terminal subsystem.

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