Test method and test management system for intelligent PON gateway test

Abstract

The invention relates to the field of network equipment test, discloses a test method and a test management system for intelligent PON gateway test, and solves the problems of insufficient test coverage, difficulty in equipment compatibility verification, low test efficiency, inaccurate test result analysis and the like in the existing intelligent PON gateway test process. High-quality and high-efficiency testing of the intelligent PON gateway is realized by establishing a testing historical database, dynamically generating a testing strategy, intelligently scheduling testing resources, accurately calculating a testing balance index and pointedly solving the problem of testing imbalance.

Description

Technical Field

[0001] The present invention relates to the technical field of communication network testing, in particular to a testing method and a testing management system for intelligent PON gateway testing. Background Art

[0002] With the rapid development of communication technology, intelligent PON gateways, as key devices in fiber-optic access networks, undertake important functions such as data exchange, protocol conversion, and service carrying between user terminals and the operator's network. Their performance stability, functional integrity, and compatibility with various network devices directly affect the service quality and user experience of the entire fiber-optic access network.

[0003] Under traditional manual testing, testing intelligent PON gateways faces numerous challenges. First, the testing process is highly dependent on the tester's experience and skills, and there is a lack of unified standards and specifications for test case design and execution. This makes it difficult to ensure test coverage, easily leading to blind spots and an inability to fully identify potential device issues. Second, with OLTs (Optical Line Terminals) and ONUs (Optical Network Units) from multiple vendors and models, traditional testing methods struggle to effectively verify inter-device compatibility. Protocol interaction anomalies and service interoperability issues between different devices frequently occur, increasing the complexity of network deployment and operations. Furthermore, as intelligent PON gateways continue to expand their functionality, such as supporting more service types (such as HD video, cloud gaming, and the Internet of Things) and implementing more complex QoS (Quality of Service) assurance mechanisms, traditional testing methods are struggling to handle large amounts of test data, complex test scenarios, and real-time test feedback. This results in low test efficiency and makes it difficult to meet the rapid iteration requirements of R&D.

[0004] While some automated testing tools exist on the market, most are limited in functionality and lack effective management of the entire test lifecycle. For example, some tools only automate simple functional tests and are unable to dynamically adjust test strategies based on device characteristics and historical test data. Some tools have limited resource scheduling capabilities, making it difficult to fully utilize distributed test resources, resulting in wasted or insufficient testing resources. Still others are weak in test result analysis, unable to accurately identify test imbalance types and provide targeted solutions. Consequently, they fail to meet the high-precision, high-efficiency, and comprehensiveness requirements of intelligent PON gateway testing. Summary of the Invention

[0005] The present invention aims to overcome the deficiencies of the prior art and provide a test method for intelligent PON gateway testing, comprising the following steps:

[0006] Step 1: Establish a test history database and generate test characteristic parameters based on the test cycle and historical test data; the test characteristic parameters include test scenario coverage, device compatibility indicators, and historical defect distribution models; and generate an initial test strategy based on the test characteristic parameters and preset test objectives;

[0007] Step 2: Allocate test resources according to the initial test strategy through a distributed test resource scheduling module to execute the automated test case set; the test resources include hardware test tools, a virtualized test environment, and a simulated business traffic model;

[0008] Step 3: Collect test execution result data and calculate the test balance index; the test balance index is a weighted comprehensive value of test coverage, device performance stability, and protocol consistency indicators; if the test balance index is within a preset threshold range, the test is determined to have passed and the process proceeds to step 7; otherwise, the process proceeds to step 4;

[0009] Step 4: Determine the type of test imbalance based on the deviation between the test balance index and the threshold; if the imbalance is caused by insufficient test resources, proceed to step 5; if the imbalance is caused by a test scenario defect, proceed to step 6;

[0010] Step 5: Generate a dynamic resource scheduling strategy based on the resource gap analysis model, call backup test equipment or cloud-based test resource pool to supplement test capacity, and return to step 3;

[0011] Step 6: Use the root cause analysis engine to locate test case vulnerabilities or protocol implementation deviations, generate test case optimization instructions or device firmware repair solutions, update the test strategy, and return to step 3.

[0012] Step 7: Output the test report and archive it to the test history database to complete the intelligent PON gateway test.

[0013] Furthermore, the establishment of the test history database includes:

[0014] Define the test cycle as a configurable time window containing multiple rounds of iterative test data; extract key features from historical test data, including: test scenario coverage at time granularity: statistical coverage of functional testing, performance testing, and security testing by test phase; device compatibility indicators: record the protocol interaction success rate and abnormal response code distribution of OLT / ONU devices from different manufacturers; defect distribution model: construct a correlation matrix of defect type, device model, and test phase based on historical defect data; and perform cluster analysis on the key features through machine learning algorithms to generate dynamically adjusted test priority weight coefficients.

[0015] Furthermore, the generating of the initial test strategy includes:

[0016] Allocate basic test resources based on the test scenario coverage at a time granularity, prioritizing the test density of high-risk scenarios. Map device compatibility indicators into a multi-vendor device collaborative test matrix to automatically generate cross-validation test cases. Configure enhanced testing strategies for historically high-frequency defect areas based on defect distribution models, including increasing stress test duration and anomaly injection frequency.

[0017] Furthermore, the allocating of test resources according to the initial test strategy by the distributed test resource scheduling module includes:

[0018] Test resources are divided into three categories: physical test equipment, virtualized test environment and hybrid simulation platform; physical test equipment, virtualized test environment and hybrid simulation platform are allocated according to the test priority weight coefficient in the initial test strategy.

[0019] Furthermore, the test balance index is:

[0020]

[0021] Where: The coverage rate of the test scenario; is the equipment performance stability coefficient; is the protocol consistency indicator; w1, w2, w3 are configurable weight parameters, satisfying w1+w2+w3=1.

[0022] Furthermore, the method for determining the test imbalance type includes:

[0023] When the balance index falls below the lower threshold, the resource gap dimension is analyzed using a decision tree model. If the utilization rate of physical test equipment exceeds 90% and the virtual resource pool fails to expand, it is determined to be a resource shortage imbalance.

[0024] When the balance index is higher than the upper threshold, high-frequency failed test cases are located through association rule mining. If the defect recurrence rate of a certain test item exceeds the preset alarm value, it is determined to be a scenario defect-type imbalance.

[0025] Furthermore, locating test case vulnerabilities or protocol implementation deviations through the defect root cause analysis engine includes:

[0026] Perform automated log parsing on failed test cases to extract key error codes and protocol interaction timings; match historical defect patterns through knowledge graphs, and directly recommend repair solutions if the match is successful; if it is a new defect, start the symbolic execution engine to generate a minimized reproduction path and update it to the test case library.

[0027] An intelligent PON gateway test management system, applying the test method for intelligent PON gateway testing, comprises: a test data management module, a test strategy engine module, a test execution module, a data analysis module, and a communication module;

[0028] The test data management module, test strategy engine module, test execution module, and data analysis module are respectively connected to the communication module for communication;

[0029] The test data management module includes a historical database module and a feature extraction engine, supporting SQL / NoSQL hybrid storage and real-time streaming analysis;

[0030] The test strategy engine module integrates a machine learning model library and an optimization algorithm library to achieve dynamic generation and self-evolution of test strategies;

[0031] The test execution module is composed of a physical test equipment controller, a virtualized resource manager, and a simulation platform scheduler, and supports multi-dimensional resource collaboration;

[0032] The data analysis module includes a balance index calculation module, a root cause analysis engine and a visual decision dashboard, providing full life cycle monitoring of the test.

[0033] The beneficial effect of the present invention is that: through the distributed test resource scheduling module, physical test equipment, virtualized test environment and hybrid simulation platform resources are intelligently allocated according to the priority weight coefficient in the initial test strategy, avoiding idle or excessive resource occupation.

[0034] To address resource shortage imbalances, the system generates a dynamic resource scheduling strategy based on the resource gap analysis model, automatically calling backup test equipment or expanding the cloud resource pool to ensure test continuity and avoid test interruptions or delays due to resource bottlenecks. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart of a test method for testing an intelligent PON gateway;

[0036] Figure 2 A test system for intelligent PON gateway testing. DETAILED DESCRIPTION

[0037] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0038] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0039] like Figure 1As shown, the test method for intelligent PON gateway testing of the present invention includes the following steps:

[0040] Step 1: Establish a test history database and generate test characteristic parameters based on the test cycle and historical test data; the test characteristic parameters include test scenario coverage, device compatibility indicators, and historical defect distribution models; and generate an initial test strategy based on the test characteristic parameters and preset test objectives;

[0041] Step 2: Allocate test resources according to the initial test strategy through a distributed test resource scheduling module to execute the automated test case set; the test resources include hardware test tools, a virtualized test environment, and a simulated business traffic model;

[0042] Step 3: Collect test execution result data and calculate the test balance index; the test balance index is a weighted comprehensive value of test coverage, device performance stability, and protocol consistency indicators; if the test balance index is within a preset threshold range, the test is determined to have passed and the process proceeds to step 7; otherwise, the process proceeds to step 4;

[0043] Step 4: Determine the type of test imbalance based on the deviation between the test balance index and the threshold; if the imbalance is caused by insufficient test resources, proceed to step 5; if the imbalance is caused by a test scenario defect, proceed to step 6;

[0044] Step 5: Generate a dynamic resource scheduling strategy based on the resource gap analysis model, call backup test equipment or cloud-based test resource pool to supplement test capacity, and return to step 3;

[0045] Step 6: Use the root cause analysis engine to locate test case vulnerabilities or protocol implementation deviations, generate test case optimization instructions or device firmware repair solutions, update the test strategy, and return to step 3.

[0046] Step 7: Output the test report and archive it to the test history database to complete the intelligent PON gateway test.

[0047] Among them, establish a test history database

[0048] Define a test cycle as a configurable time window that contains multiple rounds of iterative test data. For example, you can set the test cycle to one week, one month, or one software version iteration cycle. The specific configuration can be flexible based on actual testing needs.

[0049] Extract key features from historical test data, including: Test scenario coverage at a time granularity: Count the coverage of functional tests, performance tests, and security tests by test phase. For example, within a test cycle, count the percentage of functional test points covered in each test phase to the total number of functional test points, the percentage of test cases that meet expected performance test indicators to the total number of performance test cases, and the percentage of test scenarios that pass security tests to the total number of security test scenarios.

[0050] Device compatibility indicators: Record the protocol interaction success rate and abnormal response code distribution of OLT / ONU devices from different manufacturers. During the test, the test details the percentage of successful protocol interactions between devices from different manufacturers, as well as the frequency and distribution of various abnormal response codes when abnormal responses occur.

[0051] Defect distribution model: Builds a correlation matrix based on historical defect data, combining defect type, device model, and test phase. By collcating and analyzing historical defect data, defects are categorized and counted by type (e.g., functional defects, performance defects, security defects), device model (OLT / ONU devices from different manufacturers and models), and test phase (e.g., R&D test phase, production test phase, acceptance test phase, etc.). This correlation matrix is ​​constructed to intuitively understand the types of defects that are prone to occur in different devices at different test phases.

[0052] Cluster analysis of these key features is performed using machine learning algorithms (such as K-means and hierarchical clustering) to generate dynamically adjusted test priority weights. Based on the clustering results, the similarities and differences between different key features are analyzed, and corresponding weights are assigned to different types of test scenarios, device compatibility indicators, and defect distribution areas. These weights are dynamically adjusted as test data is continuously updated to reflect actual test requirements and changes in device characteristics.

[0053] Generate an initial test strategy based on the test characteristic parameters (test scenario coverage, device compatibility indicators, and historical defect distribution model) and preset test objectives. Preset test objectives can be set based on product requirements, industry standards, and user expectations, such as requiring functional test coverage to reach more than 95%, device compatibility testing to cover mainstream manufacturers' equipment, and a historical high-frequency defect area repair rate of 100%. Based on the gap between the test characteristic parameters and the preset test objectives, combined with the generated test priority weight coefficient, formulate an initial test strategy to clarify the test focus, resource allocation, and test methods of each test phase.

[0054] Allocate test resources according to the initial test strategy through the distributed test resource scheduling module to execute the automated test case set

[0055] The test resources include hardware testing tools (such as network analyzers, signal generators, bit error meters, etc.), virtualized test environments (such as simulated network environments built based on virtualization technology, which can simulate different network topologies, traffic models, etc.) and simulated business traffic models (such as traffic models of different business types such as simulated high-definition video traffic and IoT device upload traffic).

[0056] Test resources are divided into three categories: physical test equipment, virtualized test environments, and hybrid simulation platforms. Physical test equipment primarily provides a realistic hardware test environment for testing the hardware performance and interface functions of intelligent PON gateways. Virtualized test environments offer flexible and adaptable test scenarios through software simulation, enabling rapid setup and adjustment of test environments, reducing testing costs. Hybrid simulation platforms combine the advantages of both physical test equipment and virtualized test environments, providing realistic hardware interactions while simulating complex network environments. This makes them suitable for comprehensive and integrated testing of intelligent PON gateways.

[0057] Physical test equipment, virtualized test environments, and hybrid simulation platforms are allocated based on the test priority weight coefficients in the initial test strategy. For test scenarios with high test priority and strict hardware environment requirements, more physical test equipment is allocated; for test scenarios that require rapid setup of multiple test scenarios and high test flexibility, more virtualized test environments are allocated; for scenarios requiring complex service simulation and multi-device collaborative testing, hybrid simulation platforms are allocated. Automated test case sets are executed according to the allocated test resources. These automated test case sets, generated based on the initial test strategy, cover test cases in various aspects, including functional testing, performance testing, and security testing, and can comprehensively and systematically verify the various functions of the intelligent PON gateway.

[0058] Collect test execution result data and calculate the test balance index

[0059] The test balance index is a weighted comprehensive value of test coverage, device performance stability, and protocol consistency indicators. The specific calculation formula is:

[0060] Where: The coverage rate of the test scenario; is the equipment performance stability coefficient; is the protocol consistency indicator; w1, w2, and w3 are configurable weight parameters, satisfying w1 + w2 + w3 = 1. The test scenario coverage achievement rate represents the ratio of the number of test scenarios actually completed to the number of planned test scenarios. The device performance stability coefficient is calculated by statistically analyzing various performance indicators (such as throughput, latency, and packet loss rate) during the test process, reflecting the performance stability of the device during long-term operation. The protocol consistency indicator is calculated by checking whether the device complies with relevant standards and specifications during protocol interaction, such as whether the protocol message format and interaction process are correct.

[0061] If the test balance index is within the preset threshold range, the test is considered passed and the process proceeds to step 7; otherwise, the process proceeds to step 4. The preset threshold range is determined based on factors such as product requirements, industry standards, and actual testing experience. For example, the qualified threshold for the balance index can be set to 0.8-1.0. When the calculated balance index is within this range, the test result of the intelligent PON gateway is considered to meet the requirements and the test has passed. Otherwise, it indicates that there is a problem in the test process and further analysis and processing is required.

[0062] Determine the test imbalance type based on the deviation between the test balance index and the threshold

[0063] When the balance index is lower than the lower threshold, the resource gap dimension is analyzed through a decision tree model. The decision tree model is constructed based on historical test data and expert experience. It inputs the deviation between the balance index and the lower threshold and other relevant test data (such as test resource usage, test scenario execution, etc.), and outputs the resource gap dimension analysis results. If the utilization rate of physical test equipment exceeds 90% and the virtual resource pool fails to expand, it is determined to be an imbalance caused by insufficient resources. In this case, since the test resources cannot meet the test requirements, some test scenarios cannot be fully executed, the test coverage is insufficient, and the test balance index is affected.

[0064] When the balance index exceeds the upper threshold, association rule mining is used to locate frequently failing test cases. Association rule mining algorithms (such as the Apriori algorithm) analyze test execution result data to identify frequently failing test cases and the relationships between them. If the defect recurrence rate for a test item exceeds the preset alarm value, it is determined to be an imbalance caused by a scenario defect. This indicates that the intelligent PON gateway has a design or implementation flaw in the specific test scenario, resulting in test failure in that scenario and affecting the test balance index.

[0065] If the imbalance is caused by insufficient test resources, proceed to step 5

[0066] Generate a dynamic resource scheduling strategy based on the resource gap analysis model. The resource gap analysis model comprehensively considers factors such as the resource requirements of the current test task, the type and quantity of available test resources, and resource utilization efficiency. Through mathematical modeling and algorithm optimization, it generates the optimal resource scheduling strategy.

[0067] Calling on backup test equipment or cloud-based test resource pools to supplement testing capabilities. Backup test equipment can be pre-prepared, idle hardware test equipment. When local test resources are insufficient, these devices can be promptly called upon to participate in testing. The cloud-based test resource pool provides a wealth of virtualized test resources that can be dynamically allocated and adjusted according to actual needs. Resources in the cloud-based test resource pool can be remotely called over the network to quickly supplement testing capabilities.

[0068] After resource replenishment is completed, return to step 3, re-collect test execution result data, calculate the test balance index, and conduct a new round of test evaluation.

[0069] If the imbalance is caused by a defect in the test scenario, proceed to step 6

[0070] The root cause analysis engine is used to locate test case vulnerabilities or protocol implementation deviations. The root cause analysis engine integrates multiple analysis techniques and algorithms, including:

[0071] Perform automated log parsing on failed test cases to extract key error codes and protocol interaction timing. By deeply analyzing the log files generated during the test, we can identify the key error messages that led to the test failure and the timing relationships between devices during the protocol interaction process, providing data support for subsequent root cause analysis.

[0072] Match historical defect patterns through the knowledge graph. The knowledge graph stores a large amount of historical defect data, solutions, and related technical knowledge. The characteristics of the current failed test case are matched with the information in the knowledge graph. If a match is successful, a repair solution is directly recommended to improve problem solving efficiency.

[0073] If the defect is new, the symbolic execution engine is activated to generate a minimized reproducible path and update it to the test case library. The symbolic execution engine performs symbolic analysis on the program, automatically generating the minimized input sequence and execution path that led to the defect, helping developers quickly locate the root cause of the problem. At the same time, the newly discovered defect and its reproducible path are updated to the test case library for targeted verification in subsequent testing to prevent similar issues from recurring.

[0074] Based on the root cause analysis results, test case optimization instructions or device firmware repair plans are generated. If the problem lies in the test case design, test case optimization instructions are generated to modify and improve the test case to increase its coverage and effectiveness. If the problem lies in the device firmware implementation, a device firmware repair plan is generated to guide developers in repairing and upgrading the device firmware.

[0075] After updating the test strategy, return to step 3 and re-execute the test process to verify the repair effect and ensure that the test balance index meets the requirements.

[0076] Output test reports and archive them to the test history database to complete the intelligent PON gateway test

[0077] The test report includes a test overview (test purpose, test scope, test environment, etc.), test results (execution of each test scenario, achievement of test indicators, etc.), defect analysis (type, number, severity and repair status of defects found), test conclusions (whether the test passed, existing problems and improvement suggestions, etc.), etc.

[0078] The test reports are archived in the test history database to provide a reference for subsequent testing work. At the same time, as new test data continues to accumulate, the test history database will be continuously updated and improved, providing more accurate data support for the dynamic adjustment of subsequent test strategies and the optimal allocation of test resources, thereby achieving continuous improvement and optimization of test methods.

[0079] like Figure 2 As shown, an intelligent PON gateway test management system applies the method of locating test case vulnerabilities or protocol implementation deviations through a defect root cause analysis engine, including: a test data management module, a test strategy engine module, a test execution module, a data analysis module, and a communication module.

[0080] Test Data Management Module: This module includes a historical database module and a feature extraction engine, supporting SQL / NoSQL hybrid storage and real-time streaming analysis. The historical database module stores and manages various data generated during the testing process, including test cases, test results, defect reports, and device information. Using a SQL / NoSQL hybrid storage approach, it ensures efficient query and management of structured data while meeting the storage requirements of unstructured data. The feature extraction engine analyzes and processes historical data in real time, extracting key features to provide data support for the generation and optimization of test strategies.

[0081] Test Strategy Engine Module: This module integrates a machine learning model library and an optimization algorithm library to enable dynamic generation and self-evolution of test strategies. The machine learning model library stores a variety of machine learning algorithm models, such as decision tree models, cluster analysis models, and association rule mining models. Based on different testing requirements and data characteristics, appropriate models are selected for analysis and prediction. The optimization algorithm library contains various optimization algorithms, such as genetic algorithms and particle swarm optimization algorithms, for optimizing and adjusting test strategies, ensuring that test strategies can dynamically adapt to actual conditions and continuously improve test efficiency and quality.

[0082] The test execution module, consisting of a physical test equipment controller, a virtualized resource manager, and a simulation platform scheduler, supports multi-dimensional resource collaboration. The physical test equipment controller manages and controls the operation of physical test equipment, enabling remote operation and status monitoring. The virtualized resource manager centrally manages and allocates virtualized test environments, dynamically creating and destroying them based on test requirements. The simulation platform scheduler coordinates the operation of various components within the hybrid simulation platform, ensuring that the simulated service traffic model accurately simulates the actual network environment. The collaborative work of these three components enables efficient utilization and flexible scheduling of multi-dimensional test resources.

[0083] Data Analysis Module: This module includes a balance index calculation module, a root cause analysis engine, and a visual decision dashboard, providing full-lifecycle monitoring for testing. The balance index calculation module calculates the test balance index based on collected test execution result data using a preset formula, providing quantitative metrics for test result evaluation. The root cause analysis engine conducts in-depth analysis of issues encountered during testing, identifies their root causes, and generates appropriate solutions. The visual decision dashboard displays various test data in intuitive charts and reports, allowing testers and managers to understand test progress, results, and existing issues in real time, providing strong support for decision-making.

[0084] Example 1: Application of the test method in a certain operator's intelligent PON gateway upgrade test scenario

[0085] A provincial operator planned to upgrade the firmware of over 100,000 smart PON gateway devices in its existing network. The upgrade involved optimizing the OLT protocol stack, enhancing ONU terminal security, and expanding IPv6 dual-stack functionality. Because the existing network included OLT / ONU models from 12 manufacturers and contained a large number of legacy devices (with protocol versions lower than ITU-T G.984.4), automated testing was required to verify upgrade compatibility and functional reliability. Traditional testing methods presented the following challenges:

[0086] Insufficient test coverage: Only tests on devices from mainstream manufacturers, omitting protocol differences from niche manufacturers;

[0087] Inefficient resource scheduling: Physical test bench occupancy reached 95%, resulting in queues of over 12 hours for new test tasks.

[0088] Delayed defect location: After an ONU model upgrade, registration failures frequently occurred. Manual troubleshooting took three days to determine the cause, a DHCPv6 handshake timing deviation.

[0089] Test method implementation steps

[0090] 1. Test history database construction

[0091] Test cycle definition: The test window is divided into weeks, and nearly six months of historical test data are accumulated, covering 387 test scenarios, 21 device models and 12,000 defect records.

[0092] Key feature extraction:

[0093] Time-granular test scenario coverage: Cluster analysis revealed that security test scenario coverage was only 65% ​​of functional test coverage, and abnormal response codes from one niche vendor's ONU accounted for 18% of the total.

[0094] Equipment compatibility indicators: A matrix of OLT / ONU protocol interaction success rates was established, revealing a 23% failure rate in the OMCI protocol handshake phase between vendor A's OLT and vendor B's ONU.

[0095] Defect distribution model: A defect type-device model-test phase correlation matrix was constructed, and the "DHCPv6 timing deviation" defect was located, which occurred 14 times during the registration phase of a specific ONU model (such as the ZTE-F670).

[0096] Dynamic weight generation: Based on machine learning algorithms (such as K-means++ clustering), the following priority weights are generated:

[0097] The weight of high-risk scenarios (such as protocol handshakes) is w1 = 0.5; the weight of compatibility-sensitive devices (such as niche manufacturers) is w2 = 0.3; the weight of historically high-frequency defect areas is w3 = 0.2.

[0098] 2. Initial test strategy generation

[0099] Resource Allocation:

[0100] The proportion of physical test benches allocated for high-risk scenarios (protocol handshake) has been increased to 60%, and dedicated simulation traffic models (such as generating malformed DHCPv6 messages) have been configured. Cross-validation use cases are automatically generated for compatibility-sensitive devices (Manufacturer A-OLT + Manufacturer B-ONU), including 1,000 OMCI protocol stress tests. The stress test duration for historically high-frequency defect areas (ZTE-F670 registration phase) has been increased to 48 hours, and the anomaly injection frequency has been increased to 5 times per minute.

[0101] 3. Distributed resource scheduling

[0102] Resource pool division: Physical test equipment: 20 OLT simulators, 50 ONU terminals; virtualized test environment: 100 virtual ONU instances built on OpenStack; hybrid simulation platform: supports hybrid topologies that link SDN controllers with real OLTs.

[0103] Dynamic Scheduling: When a backlog in the OMCI test queue for vendor B-ONUs is detected, 10 virtual ONU instances are automatically expanded, improving test execution efficiency by 40%. For ZTE-F670 registration phase testing, hybrid simulation platforms supporting IPv6 dual stacks are prioritized, reducing the time required for physical device switching.

[0104] 4. Test balance index calculation and decision-making

[0105] Balance index calculation: Test scenario coverage rate C = 92% (functional testing 100%, performance testing 90%, security testing 85%);

[0106] Device performance stability coefficient S = 0.95 (based on the registration success rate of ZTE-F670);

[0107] Protocol consistency index P = 0.88 (DHCPv6 timing deviation resulted in a penalty);

[0108] Balance index B = 0.5 × 0.92 + 0.3 × 0.95 + 0.2 × 0.88 = 0.921 (the preset threshold range is [0.85, 0.95]).

[0109] Imbalance type determination: Although the balance index meets the standard, the DHCPv6 timing deviation causes the protocol consistency index P to be lower than the historical benchmark value, triggering a scenario-defect imbalance.

[0110] 5. Defect root cause analysis and strategy optimization

[0111] Root cause analysis:

[0112] We extracted the key error code "0x1F" (DHCPv6 Advertise timeout) from the failure log and, using the knowledge graph to match historical defect patterns, located the issue where the ONU terminal failed to correctly trigger a Request response after receiving an Advertise message.

[0113] Generate a minimal reproducible path through the symbolic execution engine:

[0114]

[0115] Strategy Optimization:

[0116] Generate test case optimization instructions: Added exception injection test for the scenario where the option_ia_na field is missing;

[0117] Generate a device firmware repair solution: The manufacturer needs to add else branch processing logic in the ONU code to ensure that the request is resent before the timeout.

[0118] 6. Test report and archiving

[0119] The output report shows a 98.7% test pass rate, with the remaining 1.3% of defects primarily due to DHCPv6 timing deviations.

[0120] Resource utilization: physical device utilization rate is 82%, virtual resource utilization rate is 75%;

[0121] Bug fix suggestion: 3 manufacturers need to update their firmware versions.

[0122] Archive to the test history database, update the defect distribution model, and improve the pertinence of subsequent testing strategies.

[0123] Example 2: Smart Gateway Stress Test in Industrial PON Network Scenario

[0124] An energy company plans to deploy an industrial PON network and requires the intelligent gateway to support the following features:

[0125] Extremely low latency (<1ms) for power SCADA data transmission; stable operation in extreme temperatures of -40°C to +85°C; and compatibility with industrial protocols (such as Modbus TCP and IEC 61850).

[0126] Automated testing is required to verify the reliability of the gateway in industrial scenarios, but this faces the following challenges:

[0127] Complex test scenarios: 12 types of industrial business flows, including power dispatching and equipment inspection, need to be simulated;

[0128] Difficulty in environmental simulation: Traditional laboratories cannot reproduce extreme temperatures and electromagnetic interference;

[0129] High protocol consistency requirements: Industrial protocols are sensitive to timing, and a 1ms delay deviation may cause control failure.

[0130] Test method implementation steps

[0131] 1. Test history database construction

[0132] Test cycle definition: Divide the test window into months and collect historical test data in industrial scenarios, including:

[0133] Temperature range-equipment failure rate curve; industrial protocol message timing deviation distribution; electromagnetic interference intensity-bit error rate relationship.

[0134] Key feature extraction:

[0135] Time granularity test scenario coverage: It was found that the coverage of SCADA data transmission scenarios was only 60%, and electromagnetic interference simulation was not included;

[0136] Device compatibility indicator: The Modbus TCP response time of a certain gateway model exceeds the standard by 30% in a -40°C environment;

[0137] Defect distribution model: The "IEC 61850 message parsing error" defect was located and occurred eight times in a high-temperature environment.

[0138] Dynamic weight generation:

[0139] The weight of high-risk scenarios (such as SCADA data transmission) is w1 = 0.6; the weight of compatibility-sensitive equipment (such as low-temperature environments) is w2 = 0.25; and the weight of historical high-frequency defect areas is w3 = 0.15.

[0140] 2. Initial test strategy generation

[0141] Resource Allocation:

[0142] The proportion of hybrid simulation platforms allocated for high-risk scenarios (SCADA data transmission) has increased to 70%, and industrial protocol simulators have been configured (such as generating distorted Modbus TCP messages); compatibility-sensitive equipment (low-temperature environment) automatically generates temperature gradient test cases, including three levels of testing at -40°C, -20°C, and 0°C; the stress test duration for historical high-frequency defect areas (IEC 61850 message analysis) has been increased to 72 hours, and the abnormal injection frequency has been increased to 10 times / minute.

[0143] 3. Distributed resource scheduling

[0144] Resource pool division:

[0145] Physical testing equipment: industrial-grade environmental chamber (supporting -40°C to +85°C), electromagnetic interference simulator;

[0146] Virtualized test environment: Docker-based industrial protocol simulation container cluster;

[0147] Hybrid simulation platform: supports hybrid topologies that link SDN controllers with real industrial gateways.

[0148] Dynamic Scheduling: When a backlog of low-temperature environment testing is detected, the system automatically calls upon cloud-based test resource pools (such as AWS IoT Greengrass) to expand the virtual environment chamber. For IEC 61850 message parsing tests, the system prioritizes scheduling hybrid simulation platforms that support in-depth protocol analysis, reducing manual analysis time.

[0149] 4. Test balance index calculation and decision-making

[0150] Balance index calculation:

[0151] Test scenario coverage rate C = 88% (SCADA data transmission 95%, equipment inspection 80%, electromagnetic interference simulation 75%);

[0152] Equipment performance stability coefficient S = 0.90 (based on Modbus TCP response time at -40°C);

[0153] Protocol consistency index P = 0.85 (IEC 61850 message parsing errors result in deduction of points);

[0154] Balance index B = 0.6 × 0.88 + 0.25 × 0.90 + 0.15 × 0.85 = 0.881 (the preset threshold range is [0.80, 0.90]).

[0155] Imbalance type judgment:

[0156] Although the balance index meets the standard, the coverage of the electromagnetic interference simulation scenario is only 75%, and the IEC 61850 message parsing error causes the protocol consistency index P to be lower than the historical benchmark value, triggering a scenario defect-type imbalance.

[0157] 5. Defect root cause analysis and strategy optimization

[0158] Root cause analysis: Extracting the key error code "0x2A" (IEC 61850 GOOSE message verification failure) from the failure log, and combining it with the knowledge graph to match historical defect patterns, we located the failure of the gateway's CRC verification algorithm in an electromagnetic interference environment.

[0159] Generate a minimal reproducible path through the symbolic execution engine:

[0160]

[0161]

[0162] Strategy Optimization:

[0163] Generate test case optimization instructions: Added robustness test of CRC check algorithm in electromagnetic interference environment;

[0164] Generate device firmware repair solutions: Manufacturers need to add redundant check bits in CRC calculations and optimize the retransmission mechanism in electromagnetic interference scenarios.

[0165] 6. Test report and archiving

[0166] The output report shows:

[0167] The test pass rate was 97.5%, with the remaining 2.5% of defects concentrated in CRC checks under electromagnetic interference environments.

[0168] Resource utilization: physical device utilization rate is 78%, and virtual resource utilization rate is 82%;

[0169] Bug fix suggestion: 2 manufacturers need to update their firmware versions.

[0170] Archive to the test history database, update the defect distribution model, and improve the pertinence of subsequent testing strategies.

Claims

1. A test method for intelligent PON gateway testing, characterized in that: The steps include: Step 1: Establish a test history database and generate test characteristic parameters based on the test cycle and historical test data; the test characteristic parameters include test scenario coverage, device compatibility indicators, and historical defect distribution models; and generate an initial test strategy based on the test characteristic parameters and preset test objectives; Step 2: Allocate test resources according to the initial test strategy through a distributed test resource scheduling module to execute the automated test case set; the test resources include hardware test tools, a virtualized test environment, and a simulated business traffic model; Step 3: Collect test execution result data and calculate the test balance index; The test balance index is a weighted comprehensive value of test coverage, device performance stability, and protocol consistency indicators. If the test balance index is within a preset threshold range, the test is considered passed and the process proceeds to step 7; otherwise, the process proceeds to step 4. Step 4: determining the test imbalance type according to the deviation between the test balance index and the threshold; If the imbalance is caused by insufficient test resources, proceed to step 5; If the imbalance is caused by a defect in the test scenario, proceed to step 6; Step 5: Generate a dynamic resource scheduling strategy based on the resource gap analysis model, call backup test equipment or cloud-based test resource pool to supplement test capacity, and return to step 3; Step 6: Use the root cause analysis engine to locate test case vulnerabilities or protocol implementation deviations, generate test case optimization instructions or device firmware repair solutions, update the test strategy, and return to step 3. Step 7: Output the test report and archive it to the test history database to complete the intelligent PON gateway test.

2. The test method for intelligent PON gateway testing according to claim 1, characterized in that: The establishment of the test history database includes: Define the test cycle as a configurable time window that contains multiple rounds of iterative test data; extract key features from historical test data, including: time-granular test scenario coverage: statistical coverage of functional testing, performance testing, and security testing by test phase; device compatibility indicators: record the protocol interaction success rate and abnormal response code distribution of OLT / ONU devices from different manufacturers; defect distribution model: construct a correlation matrix of defect type, device model, and test phase based on historical defect data; and perform cluster analysis on the key features through machine learning algorithms to generate dynamically adjusted test priority weight coefficients.

3. The test method for intelligent PON gateway testing according to claim 2, characterized in that: The generating initial test strategy includes: Allocate basic test resources based on the test scenario coverage at a time granularity, prioritizing the test density of high-risk scenarios. Map device compatibility indicators into a multi-vendor device collaborative test matrix to automatically generate cross-validation test cases. Configure enhanced testing strategies for historically high-frequency defect areas based on defect distribution models, including increasing stress test duration and anomaly injection frequency.

4. The test method for intelligent PON gateway testing according to claim 3, characterized in that: The allocating of test resources according to the initial test strategy by the distributed test resource scheduling module includes: Test resources are divided into three categories: physical test equipment, virtualized test environment and hybrid simulation platform; physical test equipment, virtualized test environment and hybrid simulation platform are allocated according to the test priority weight coefficient in the initial test strategy.

5. The test method for intelligent PON gateway testing according to claim 1, characterized in that: The test balance index is: Where: The coverage rate of the test scenario is achieved; is the equipment performance stability coefficient; is the protocol consistency indicator; w1, w2, w3 are configurable weight parameters, satisfying w1+w2+w3=1.

6. The test method for intelligent PON gateway testing according to claim 1, characterized in that: The method for determining the type of test imbalance includes: When the balance index falls below the lower threshold, the resource gap dimension is analyzed using a decision tree model. If the utilization rate of physical test equipment exceeds 90% and the virtual resource pool fails to expand, it is determined to be a resource shortage imbalance. When the balance index is higher than the upper threshold, high-frequency failed test cases are located through association rule mining. If the defect recurrence rate of a certain test item exceeds the preset alarm value, it is determined to be a scenario defect-type imbalance.

7. The test method for intelligent PON gateway testing according to claim 1, characterized in that: The use of the root cause analysis engine to locate test case vulnerabilities or protocol implementation deviations includes: Perform automated log parsing on failed test cases to extract key error codes and protocol interaction timings; match historical defect patterns through knowledge graphs, and directly recommend repair solutions if the match is successful; if it is a new defect, start the symbolic execution engine to generate a minimized reproduction path and update it to the test case library.

8. An intelligent PON gateway test management system, characterized in that: The test method for intelligent PON gateway testing according to any one of claims 1 to 7 comprises: a test data management module, a test strategy engine module, a test execution module, a data analysis module, and a communication module; The test data management module, test strategy engine module, test execution module, and data analysis module are respectively connected to the communication module for communication; The test data management module includes a historical database module and a feature extraction engine, supporting SQL / NoSQL hybrid storage and real-time streaming analysis; The test strategy engine module integrates a machine learning model library and an optimization algorithm library to achieve dynamic generation and self-evolution of test strategies; The test execution module is composed of a physical test equipment controller, a virtualized resource manager, and a simulation platform scheduler, and supports multi-dimensional resource collaboration; The data analysis module includes a balance index calculation module, a root cause analysis engine and a visual decision dashboard, providing full life cycle monitoring of the test.

Images