A method for intelligent test change perception and decision making

Abstract

This invention discloses an intelligent test change perception and decision-making method, which includes: scanning application system code versions; generating code difference tracking relationships; calculating the impact of changes to generate mandatory testing scope; intelligently recommending and associating test cases; calculating missing asset test cases to supplement test cases; and generating coloring statistics to reveal missed test points. This concentrates testing resources on the core areas affected by changes and high-risk scenarios, improving testing efficiency and quality while reducing testing costs.

Description

Technical Field

[0001] This invention relates to the field of intelligent testing, and more particularly to a method for intelligent testing change perception and decision-making. Background Technology

[0002] Intelligent testing and change perception decision-making are crucial in an era where the "AI+" strategy is being deeply implemented and driving the comprehensive intelligent upgrade of the financial industry. The stability and security of core financial systems directly affect the smooth operation of the financial market and are closely linked to the asset safety of hundreds of millions of people. As a key link in ensuring the quality of financial software and strengthening system security, financial testing is facing multiple severe challenges and urgently needs innovative breakthroughs.

[0003] The complexity of current financial operations continues to rise, with increasingly frequent cross-domain and cross-scenario business collaborations. Coupled with the accelerating pace of financial product iteration to respond to market demands, and increasingly stringent regulatory compliance requirements, traditional financial testing models are gradually revealing their shortcomings in addressing these multi-dimensional challenges. Traditional testing relies heavily on manual operations, which is not only inefficient and unable to keep pace with the rapid iteration of business processes, but also limited in its coverage of complex business logic and accurate identification of potential risks, failing to fully meet the high standards of quality and security required by the financial industry. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide an intelligent test change perception decision-making method that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, an intelligent test change perception and decision-making method is provided, the perception and decision-making method comprising: Application system code version scanning; Generate code differences to trace lineage; Calculate the impact of the changes to generate the mandatory measurement range; Intelligent recommendation and association of test cases; Calculate missing asset test cases; Generate staining statistics to reveal missed detection points.

[0006] Optionally, the application system code version scanning specifically includes: risk management and testing efficiency improvement throughout the entire code change lifecycle.

[0007] Optionally, the generation of code differences for tracing lineage specifically includes: Focusing on tracing the differences in the code version iteration process, we construct a complete code lineage tracing chain by comparing the core differences between different versions of the code; Locate the module to which the difference code belongs, the person responsible for its development, and the submission time; trace the historical iteration trajectory of the difference code; and clarify its relationship with upstream dependent modules and downstream related modules. By tracing the lineage, we can clarify the origins and development of the code changes.

[0008] Optionally, the core differences between the different versions of the code specifically include: newly added, modified, and deleted code blocks, functions, and configuration items.

[0009] Optionally, the calculation of the impact of changes to generate the mandatory measurement range specifically includes: Based on the analysis results of code differences and lineage relationships, the potential impact of code changes on the application system is automatically estimated. By analyzing the dependency chain, call relationship and business logic of the changed code, the core business processes, functional modules, interfaces and data links that are affected can be identified. Generate a minimum scope of mandatory testing, and identify the key functionalities, interface test cases, and data scenarios that need to be tested.

[0010] Optionally, the intelligent recommendation and association of test cases specifically includes: By combining the scope of the change's impact with the code's business attributes, intelligent algorithms can accurately recommend test cases highly relevant to the change from the test case library. The recommendations cover all testing phases, and automatically establish the association between recommended test cases and changed code and affected modules, generating an association mapping table.

[0011] Optionally, the intelligent algorithm specifically includes: code similarity matching, historical test data association, and business scenario mapping.

[0012] Optionally, the test cases for supplementing missing assets include: Analyze the coverage of test assets within the mandatory testing scope, and in conjunction with the new business scenarios, new code logic, and potential risk points affected by the changes, estimate the missing test assets in the existing test case library; For missing test scenarios, generate suggestions for supplementing test cases, clarifying the core test points, input and output parameters, and expected results of the supplementary test cases; It provides basic test case templates to help testers quickly write supplementary test cases.

[0013] Optionally, the generation of staining statistics to reveal missed detection points specifically includes: Code coloring technology enables end-to-end tracing of the testing process, mapping test case execution to code change areas, and generating coloring statistical reports. The report marks the covered, uncovered, and partially covered areas of the changed code, and, combined with the analysis results of the mandatory testing scope, reveals the points of missed testing during the testing process.

[0014] This invention provides an intelligent test change perception and decision-making method, which includes: scanning application system code versions; generating code difference tracking relationships; calculating the impact of changes to generate mandatory testing scope; intelligently recommending and associating test cases; calculating missing asset supplementary test cases; and generating coloring statistics to reveal missed test points. This concentrates testing resources on the core areas affected by changes and high-risk scenarios, improving testing efficiency and quality while reducing testing costs.

[0015] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of an intelligent test change perception and decision-making method provided in an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0019] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0021] like Figure 1 As shown, the present invention provides an intelligent test change perception and decision-making method, the perception and decision-making method comprising: Application system code version scanning; Generate code differences to trace lineage; Calculate the impact of the changes to generate the mandatory measurement range; Intelligent recommendation and association of test cases; Calculate missing asset test cases; Generate staining statistics to reveal missed detection points.

[0022] Application system code version scanning: Application system code version scanning is a key step in ensuring code quality and reducing change risks. Its core functions revolve around risk management and testing efficiency improvement throughout the entire code change lifecycle.

[0023] Generate code difference tracing and lineage: Focusing on the source of differences during code version iteration, this feature constructs a complete code lineage tracing chain by comparing the core differences between different versions (including added, modified, and deleted code blocks, functions, and configuration items). It not only accurately identifies the module to which the differing code belongs, the developer responsible, and the commit time, but also traces the historical iteration trajectory of the differing code, clarifying its relationship with upstream dependent modules and downstream related modules. By clarifying the lineage, the origins and development of code changes can be quickly understood, providing core evidence for subsequent change impact analysis and problem tracing, effectively solving the problem of unclear code change chains in multi-version iterations.

[0024] Estimating the Impact of Changes and Generating the Scope of Mandatory Testing: Based on code differences and lineage analysis, this function can automatically estimate the potential impact of code changes on the application system. By analyzing the dependency chains, call relationships, and business logic of the changed code, it identifies the affected core business processes, functional modules, interfaces, and data links. Based on this, it accurately generates a minimum scope of mandatory testing, clearly defining the functional points, interface test cases, and data scenarios that need to be prioritized for testing. This avoids the inefficiencies of full-scale testing while ensuring that no core areas affected by the change are missed, improving the targeting and effectiveness of testing.

[0025] Intelligent test case recommendation and association: Combining the scope of the change's impact and the code's business attributes, this feature uses intelligent algorithms (based on code similarity matching, historical test data association, business scenario mapping, etc.) to accurately recommend test cases highly relevant to the changed content from the test case library. The recommendation scope covers all testing phases, and it automatically establishes associations between recommended test cases and the changed code and affected modules, generating an association mapping table. This allows for rapid reuse of existing effective test cases, reducing the workload of writing repetitive test cases, improving test preparation efficiency, and ensuring an accurate match between test cases and change requirements.

[0026] This feature calculates missing test cases for assets: After intelligently recommending test cases, it further analyzes the coverage of test assets within the mandatory testing scope. Combining the impact of changes on new business scenarios, new code logic, and potential risks, it calculates the missing test assets in the existing test case library. For missing test scenarios, it generates suggested supplementary test cases, clearly defining the core test points, input / output parameters, and expected results. If necessary, basic test case templates can be provided to assist testers in quickly writing supplementary test cases, ensuring the completeness and comprehensiveness of test assets.

[0027] Code coloring statistics reveal missed test cases: By using code coloring technology to trace the entire testing process, the execution of test cases is mapped to code change areas, generating detailed coloring statistics reports. These reports clearly mark covered, uncovered, and partially covered changed code areas. Combined with mandatory test scope analysis results, they accurately reveal missed test cases. Furthermore, by statistically analyzing the business scenarios, code modules, and risk levels corresponding to these missed test cases, targeted supplementary testing suggestions are provided to testers, helping them quickly complete supplementary testing of the missed content, ensuring comprehensive coverage of code change areas, and reducing the possibility of risks introduced by changes.

[0028] Based on blood relations, the recommended testing range includes: Intelligent test change perception and decision-making is demonstrated through pipeline engineering applications, constructing an intelligent agent (intelligent perception sentinel) to reveal changes and missed tests. This application is model-driven, automatically identifying the scope of impact of software changes and accurately detecting test blind spots. Through a closed loop of "perception-analysis-decision-execution," it helps teams to promptly discover and fix potential defects caused by changes.

[0029] It is a perfect combination of change management and test quality assurance, designed to solve the most common pain points in software development, transforming the passive response mode of "change-test-defect" into a proactive defense system of "change identification-impact analysis-missed test warning-test optimization".

[0030] Technical implementation architecture: Change source (code / Git) → Change parser → Impact graph construction (lineage) → Decision engine → Action execution (test triggering) → Missed test detector (coverage analysis).

[0031] 1) Changes to the perception and parsing engine: By adopting a dual technical approach of "static scanning + architecture restoration", the core objective is to accurately locate the scope of impact of code changes by building the "blood relationship" between code, and to provide a reliable basis for risk assessment and test scope definition in R&D iteration.

[0032] The static scanning module, as the foundational step in change identification, primarily enables precise comparison of code files before and after version iterations. Through in-depth analysis of the old and new code versions, this module can perform multi-dimensional differential analysis, specifically covering key code elements such as variable definitions, function calls, interface parameters, class structures, and configuration items. During the analysis, the system automatically marks newly added, deleted, and modified code snippets, and further subdivides the modification types (such as changes in variable types, additions or removals of function parameters, and adjustments to interface return values), forming a clear change list and providing fundamental data support for subsequent impact analysis.

[0033] The code architecture reconstruction module bears the core responsibility of constructing the "bloodline relationship," which is based on Abstract Syntax Tree (AST) technology to deeply mine and visualize code dependencies. This module first performs AST parsing on the complete code of the current version, extracting core elements such as modules, classes, functions, and variables. Then, based on the call and reference relationships between these elements, it constructs a global code dependency network. Through this intuitive "bloodline relationship" presentation, the call chains between core modules and related modules, and between core functions and their upstream and downstream calling functions, are clearly identified, clarifying the hierarchy and logical relationships of each code element within the overall architecture.

[0034] With the synergy of the two technical approaches, the implementation logic for locating the scope of change impact is as follows: First, the static scanning module identifies the specific changed code snippets and change types; then, relying on the dependency network built by the code architecture restoration module, starting from the changed code snippets, the system traces upstream along the "bloodline" link to the dependent code elements and downstream to the affected related code elements; finally, combining the risk level of the change type (such as core interface changes, basic function modifications, etc.), the system accurately defines the impact boundaries of the changed code on upstream and downstream functions, forming a complete change impact scope report, clearly indicating which modules, functions, interfaces, and business processes may be affected by this change.

[0035] 2) Precise association and intelligent recommendation: Using a user behavior "travel map" generated by intelligent sensing and user operation behavior monitoring as the core bridging carrier, precise correlation is achieved between the scope of changes and business scenario use cases. Data such as high-frequency operation paths, high-risk transaction nodes, and concentrated user interaction scenarios recorded in the travel map are cross-analyzed with the scope of the change's impact. "High-frequency scenario use cases involved in the change, core function use cases, and historical defect-related use cases" are prioritized as mandatory test attributes, and key attributes such as use case priority, verification type, and execution environment are simultaneously defined. This makes testing decisions and key investments more precise.

[0036] This is achieved through three steps: "data extraction - cross-matching - precise positioning". Data extraction: Extract core data such as high-frequency operation paths, high-risk transaction nodes, and concentrated user interaction scenarios from the user behavior "travel map" to form a "user behavior core data list"; at the same time, sort out the functional modules, data interfaces, and business processes corresponding to the change requirements to form a "change impact scope list".

[0037] Cross-matching: Align and cross-analyze the "Core User Behavior Data List" with the "List of Scopes Affected by Changes" to identify the intersection area between the two—that is, the high-frequency operation paths covered by the scope of changes, the high-risk transaction nodes involved, and the associated concentrated user interaction scenarios.

[0038] Precise positioning: Based on the results of cross-analysis, the core business scenarios affected by the change are located, and then the corresponding business scenario use cases are associated to achieve a precise mapping of "change impact - user behavior - business use cases".

[0039] 3) Decision-making actions and missed detection revelation: Incremental dynamic coloring tests are conducted on the changed code. By embedding lightweight coloring probes during the code compilation stage, the lines of code, functions and related call chains involved in the changes are colored and marked in real time.

[0040] Targeted tests are automatically triggered to verify the effectiveness of changes and fixes. During test execution, probes synchronously collect the execution status of stained code and generate incremental stained reports containing "stained scope, execution coverage, and unexecuted code segments". The reports visually present the uncovered parts of the test in the form of heatmaps and automatically associate them with the corresponding business scenarios and test points, providing testers with accurate supplementary test cases.

[0041] Beneficial effects: 1. Functionality Enhancement Intelligent test change perception and decision-making transforms manual judgment into intelligent estimation and recommendation, estimating the scope of changes and recommending test case assets, replacing manual driving with model-driven approaches.

[0042] 2. Improved efficiency Intelligent test change perception and decision-making enables automatic calculation of the impact of changes, replacing traditional manual calculations and experience-based decisions. This reduces the impact analysis time of system changes from several hours to 10 minutes, and the accuracy rate of change scope identification reaches 96%, avoiding test omissions caused by incomplete change assessments.

[0043] 3. Breakthrough in technological bottlenecks Intelligent test change awareness and decision-making uses use case travel graphs and code lineage relationships as bridges to achieve a two-way association between code and use cases. It constructs a link between the scope of changes and use cases in business scenarios, and its core value is reflected in two main aspects: More precise test decisions: Based on cross-analysis of real user behavior data, it avoids the "broad-based" test case coverage in traditional testing, clarifies the must-test test cases and priorities, and makes test decisions more in line with actual business needs; More rational allocation of resources: By clearly defining high-priority test cases, core verification types, and execution environments, testing resources are concentrated on the core areas affected by changes and high-risk scenarios, thereby improving testing efficiency and quality and reducing testing costs.

[0044] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent test change perception and decision-making, characterized in that, The perception-decision method includes: Application system code version scanning; Generate code differences to trace lineage; Calculate the impact of the changes to generate the mandatory measurement range; Intelligent recommendation and association of test cases; Calculate missing asset test cases; Generate staining statistics to reveal missed detection points.

2. The intelligent test change perception and decision-making method according to claim 1, characterized in that, The application system code version scanning specifically includes: risk management and testing efficiency improvement throughout the entire code change lifecycle.

3. The intelligent test change perception and decision-making method according to claim 1, characterized in that, The specific aspects of the generated code difference tracking bloodline relationship include: Focusing on tracing the differences in the code version iteration process, we construct a complete code lineage tracing chain by comparing the core differences between different versions of the code; Locate the module to which the difference code belongs, the person responsible for its development, and the submission time; trace the historical iteration trajectory of the difference code; and clarify its relationship with upstream dependent modules and downstream related modules. By tracing the lineage, we can clarify the origins and development of the code changes.

4. The intelligent test change perception and decision-making method according to claim 3, characterized in that, The core differences between the different versions of the code specifically include: newly added, modified, and deleted code blocks, functions, and configuration items.

5. The intelligent test change perception and decision-making method according to claim 1, characterized in that, The specific scope for calculating the impact of changes and generating mandatory measurement ranges includes: Based on the analysis results of code differences and lineage relationships, the potential impact of code changes on the application system is automatically estimated. By analyzing the dependency chain, call relationship and business logic of the changed code, the core business processes, functional modules, interfaces and data links that are affected can be identified. Generate a minimum scope of mandatory testing, and identify the key functionalities, interface test cases, and data scenarios that need to be tested.

6. The intelligent test change perception and decision-making method according to claim 1, characterized in that, The intelligent recommendation and association of test cases specifically includes: By combining the scope of the change's impact with the code's business attributes, intelligent algorithms can accurately recommend test cases highly relevant to the change from the test case library. The recommendations cover all testing phases, and automatically establish the association between recommended test cases and changed code and affected modules, generating an association mapping table.

7. The intelligent test change perception and decision-making method according to claim 6, characterized in that, The intelligent algorithm specifically includes: code similarity matching, historical test data association, and business scenario mapping.

8. The intelligent test change perception and decision-making method according to claim 1, characterized in that, The specific test cases for supplementing missing assets include: Analyze the coverage of test assets within the mandatory testing scope, and in conjunction with the new business scenarios, new code logic, and potential risk points affected by the changes, estimate the missing test assets in the existing test case library; For missing test scenarios, generate suggestions for supplementing test cases, clarifying the core test points, input and output parameters, and expected results of the supplementary test cases; It provides basic test case templates to help testers quickly write supplementary test cases.

9. The intelligent test change perception and decision-making method according to claim 1, characterized in that, The generated staining statistics reveal the specific missed detection points, including: Code coloring technology enables end-to-end tracing of the testing process, mapping test case execution to code change areas, and generating coloring statistical reports. The report marks the covered, uncovered, and partially covered areas of the changed code, and, combined with the analysis results of the mandatory testing scope, reveals the points of missed testing during the testing process.

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