Financial software intelligent testing method, system, device and medium

Abstract

The application discloses a kind of financial software intelligent test method, system, equipment and medium, comprising the following steps: S1. the test case library of financial scene adaptation is built;S2. start UI automation test, simulate user on target client financial APP operation process, collect the customer information of client in test process, customer information includes UI hierarchical structure information, function call data and running state data;S3. based on multidimensional comparison detection mechanism identifies defect, compares the customer information of to-be-tested client with reference datum multidimensionally, and combines algorithm to determine abnormal item, and is marked as abnormal node;S4. according to the characteristics of financial scene, through intelligent gray scale test, formulate gray scale test strategy, through dynamic stress test and multidimensional monitoring, detect the performance and stability defects of client under complex scene;S5. based on preset evaluation system, the identified defect is graded and processed, and generates the detection report containing defect information and optimization suggestion.

Description

Technical Field

[0001] This invention belongs to the field of software testing technology, and in particular relates to a method, system, device and medium for intelligent testing of financial software. Background Technology

[0002] Financial apps serve as the core platform for users to conduct financial transactions and access market information. The stability of their client-side operations, the accuracy of their functions, and the security of their data directly impact user asset safety and the smooth operation of the financial market. However, existing financial app client defect detection solutions have several shortcomings: incomplete coverage of detection scenarios, making it difficult to adapt to complex scenarios such as fluctuating financial market conditions and high-concurrency trading; limited detection accuracy, with traditional comparison methods susceptible to misjudgments due to device differences and complex functional logic; simplistic gray-scale testing strategies, often employing fixed stress modes, failing to simulate the dynamic characteristics of the financial market; and a lack of a scientific grading system for defect handling, resulting in chaotic repair priorities and impacting detection and optimization efficiency.

[0003] Therefore, there is an urgent need for an automated client defect detection solution that is adaptable to the characteristics of financial scenarios, comprehensive, accurate and efficient. Summary of the Invention

[0004] This application addresses the problems existing in the prior art by proposing an intelligent testing method, system, device, and medium for financial software. Through the coordinated use of UI automated testing, multi-dimensional comparative detection, and intelligent gray-scale testing, combined with the deep application of multiple algorithm models, it achieves comprehensive detection of defects in multiple dimensions such as UI compatibility, functional consistency, data accuracy, and performance stability, without any blind spots in detection.

[0005] To achieve the above objectives, this application provides the following technical solution: Firstly, a method for intelligent testing of financial software includes the following steps: S1. Constructing a test case library adapted to financial scenarios, the test case library covering core financial business scenarios such as market data viewing, transaction operations, account management, and data refresh, supporting test case classification management, template reuse, and dynamic updates; S2. Initiating UI automated testing, simulating the user's operation process on the target client's financial APP, and collecting customer information from the client during the testing process, including UI hierarchy structure information, function call data, and running status data; S3. Identifying defects based on a multi-dimensional comparison detection mechanism, comparing the customer information of the client under test with reference benchmark data in multiple dimensions, and using a node matching algorithm, dynamic error judgment algorithm, and functional consistency deviation rate algorithm to determine abnormal items and mark them as abnormal. Node; The multi-dimensional comparison and detection mechanism includes UI compatibility verification, functional consistency verification, and financial data accuracy verification; S4. Develop intelligent gray-scale testing strategies based on the characteristics of financial scenarios, construct a dedicated gray-scale testing scenario library for financial scenarios including market fluctuations, high-frequency trading, large-scale concurrency, and cross-regional data synchronization, execute dynamic stress testing through layered stress testing algorithms, achieve distributed testing by combining load distribution balancing algorithms, and complete multi-dimensional monitoring through log correlation analysis algorithms to detect performance and stability defects of the client in complex scenarios; S5. Based on a preset evaluation system that includes defect severity, impact scope, probability of occurrence, and financial business relevance, perform at least three levels of classification processing on the identified defects, and generate a detection report that includes defect location, type, level, cause analysis, and optimization suggestions.

[0006] Optionally, the UI hierarchy information includes multiple nodes and their corresponding attributes, including at least one of node coordinates and resource identifiers; the function call data includes at least one of function number call logs and transaction process response data; and the running status data includes at least one of CPU utilization, memory utilization, and data transmission latency.

[0007] Optionally, the dimensional comparison detection mechanism includes: S31. Using a node matching algorithm to perform node matching on the UI hierarchical structure information, identifying unmatched points and matched points with abnormal attributes; converting node coordinates based on the device screen size correlation, and verifying UI compatibility defects through a dynamic error judgment algorithm; S32. Comparing function call data to identify defects, using a function consistency deviation rate algorithm; S33. For the financial market data in the running status data and the transaction data in the function call data, using a data error verification algorithm to verify the accuracy of key data, ensuring that the errors in transaction amount and position quantity comply with financial industry standards.

[0008] Optional, intelligent gray-scale testing includes: S41. Building a gray-scale test scenario library specifically for financial scenarios and gradually increasing test pressure using a layered stress testing algorithm; S42. Introducing a load balancing mechanism to achieve distributed testing and optimizing test task allocation using a load distribution balancing algorithm; S43. Identifying hidden abnormal events by combining log correlation analysis algorithms.

[0009] Optionally, the tiered stress test algorithm dynamically adjusts the load increase rate through the pressure attenuation coefficient. The pressure is gradually increased in each tiered stress test level, and the load intensity of the i-th layer is determined by the load intensity of the (i-1)-th layer, the initial load increase coefficient, the pressure attenuation coefficient, and the value of i.

[0010] Optionally, the preset evaluation system uses a comprehensive defect scoring algorithm to achieve quantitative grading, as shown in the following formula: (7) Where F is the overall score; The weight of the k-th indicator can be configured according to the needs of financial institutions. The score is the quantitative score of the k-th indicator, ranging from 1 to 5 points. Based on the value of the comprehensive score F, the defects are divided into five levels: Level 1 defects are F≥4.5 points, Level 2 defects are 3.5≤F<4.5 points, Level 3 defects are 2.5≤F<3.5 points, Level 4 defects are 1.5≤F<2.5 points, and Level 5 defects are F<1.5 points.

[0011] Secondly, the present invention provides an intelligent testing system for financial software, used to implement the intelligent testing method for financial software as described in the first aspect, comprising: a test case management module, used to construct and manage a test case library adapted to financial scenarios, wherein the test case library covers test cases for core financial business scenarios such as market data viewing, transaction operations, account management, and data refresh, and supports test case classification management, template reuse, and dynamic updates; a UI automation testing module, used to simulate the user's operation process on a financial APP on a target client, and collect customer information of the client during the testing process, wherein the customer information includes UI hierarchical structure information, function call data, and running status data; a multi-dimensional comparison and detection module, used to compare the customer information of the client under test with reference benchmark data in multiple dimensions, and combine node matching algorithm, dynamic error judgment algorithm, and functional consistency deviation rate algorithm to determine abnormal items and mark them as abnormal nodes; the multi-dimensional comparison and detection module... The testing mechanism includes UI compatibility verification, functional consistency verification, and financial data accuracy verification. The intelligent gray-scale testing module is used to formulate intelligent gray-scale testing strategies based on the characteristics of financial scenarios. It constructs a dedicated gray-scale testing scenario library for financial scenarios, including market fluctuations, high-frequency trading, large-scale concurrency, and cross-regional data synchronization. Dynamic stress testing is performed through a layered stress testing algorithm, combined with a load balancing algorithm to achieve distributed testing. Multi-dimensional monitoring is completed through log correlation analysis algorithms to detect performance and stability defects of the client under complex scenarios. The defect classification and processing module is used to classify identified defects into at least three levels based on a preset evaluation system that includes defect severity, impact scope, probability of occurrence, and financial business relevance. It generates a testing report containing defect location, type, level, cause analysis, and optimization suggestions. The report management module manages the testing reports, supporting report viewing, downloading, and defect tracking.

[0012] Optionally, the intelligent gray-scale testing module includes a scenario configuration unit, a stress testing unit, a load balancing unit, and a monitoring and analysis unit; the stress testing unit executes a layered stress testing algorithm, the load balancing unit executes a load distribution balance algorithm, and the monitoring and analysis unit executes a log association confidence model.

[0013] Thirdly, the present invention provides a computer device, the computer device including a memory, a processor and a computer program, wherein when the computer program is executed by the processor, it implements the intelligent testing method for financial software as described in the first aspect.

[0014] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the intelligent testing method for financial software as described in the first aspect.

[0015] The beneficial effects of this application are as follows: (1) This application simulates client operation through UI automation testing, integrates multi-dimensional comparison detection and intelligent gray-scale testing, and uses improved node matching algorithm, dynamic error judgment model, hierarchical stress test algorithm and log association confidence model to achieve comprehensive and efficient identification of APP client defects in financial scenarios.

[0016] (2) Use intelligent grayscale testing to verify client performance and compatibility under high concurrency and extreme market conditions; output detection results and optimization suggestions through defect classification model.

[0017] (3) This application is applicable to multiple system versions and various financial business scenarios, significantly improving detection efficiency and accuracy, and ensuring stable operation of the APP after it is launched. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the application scenario of the intelligent testing method for financial software according to Embodiment 1 of this application. Figure 2 This is a schematic diagram of the intelligent testing method for financial software according to Embodiment 1 of this application; Figure 3 This is a schematic diagram of the multi-dimensional comparison detection mechanism in Embodiment 1 of this application; Figure 4 This is a schematic diagram of intelligent grayscale testing according to Embodiment 1 of this application; Figure 5 This is a schematic diagram of the intelligent testing method for financial software according to Embodiment 1 of this application; Figure 6 This is a schematic diagram of the intelligent testing system for financial software according to Embodiment 2 of this application; Figure 7 This is a structural diagram of the electronic device shown in Embodiment 3 of this application.

[0019] Figure label: 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] like Figure 1The diagram illustrates the application scenario of intelligent testing methods for financial software. These methods are applied to client-side testing scenarios for financial apps such as stocks, funds, and futures. Before app version iterations, new system adaptations, and new business launches, they can quickly complete UI, functional, and performance defect detection for clients across multiple systems (iOS / Android / HarmonyOS). They are particularly well-suited for core financial scenarios such as high-concurrency market conditions and high-frequency trading, simulating client performance under extreme market volatility, accurately identifying compatibility and stability defects, and helping financial institutions ensure user experience and transaction security after app launch.

[0022] Example 1: As Figures 2-5 As shown, a method for intelligent testing of financial software includes the following steps: S1. Construct a test case library adapted to financial scenarios. The test case library covers test cases for core financial business scenarios such as market data viewing, trading operations, account management, and data refresh. It supports test case classification management, template reuse, and dynamic updates. S2. Start UI automated testing to simulate the user's operation process of the financial APP on the target client and collect customer information of the client during the test. The customer information includes UI hierarchy structure information, function call data and running status data. S3. Based on a multi-dimensional comparison detection mechanism, defects are identified by comparing the customer information of the client under test with the reference benchmark data in multiple dimensions. Anomalies are identified by combining node matching algorithm, dynamic error judgment algorithm, and functional consistency deviation rate algorithm, and marked as abnormal nodes. The multi-dimensional comparison detection mechanism includes UI compatibility verification, functional consistency verification, and financial data accuracy verification. S4. Develop intelligent gray-scale testing strategies based on the characteristics of financial scenarios, build a dedicated gray-scale testing scenario library for financial scenarios including market fluctuations, high-frequency trading, large-scale concurrency, and cross-regional data synchronization, execute dynamic stress testing through layered stress testing algorithms, achieve distributed testing by combining load distribution balancing algorithms, and complete multi-dimensional monitoring through log correlation analysis algorithms to detect performance and stability defects of the client in complex scenarios. S5. Based on a pre-defined evaluation system that includes the severity of defects, scope of impact, probability of occurrence, and relevance to financial business, identify defects and perform at least three levels of classification processing to generate a detection report that includes the location, type, level, cause analysis, and optimization suggestions of the defects.

[0023] Based on the above steps, this embodiment achieves comprehensive detection of defects across multiple dimensions, including UI compatibility, functional consistency, data accuracy, and performance stability, through the coordinated use of automated UI testing, multi-dimensional comparative detection, and intelligent gray-scale testing, combined with the deep application of multiple algorithm models, leaving no blind spots in detection. Furthermore, focusing on the characteristics of financial scenarios, algorithm parameters can be flexibly configured according to financial business needs, constructing a dedicated gray-scale testing scenario library and evaluation system to accurately match the testing requirements of various financial apps such as stocks and funds, adapting to multiple system versions and complex scenarios. Quantitative calculations of various algorithm models reduce the misjudgment rate caused by device differences and scenario complexity; layered stress testing and load balancing mechanisms improve gray-scale testing efficiency; automated testing replaces manual operation, significantly reducing detection costs and improving defect identification and repair efficiency. Through intelligent gray-scale testing, the performance bottlenecks, stability defects, data synchronization defects, and fault tolerance defects of financial app clients under complex scenarios such as high concurrency, large data volumes, and extreme market conditions are comprehensively detected, providing accurate basis for client optimization.

[0024] In this embodiment, in step S1, a test case library is constructed based on the business characteristics of the financial APP, covering core scenarios such as market data viewing, transaction operations, account management, and data refresh. The test case library supports management by business type, provides test case template reuse functionality, and can be dynamically optimized according to updates to financial business rules and scenario expansions, ensuring that test cases can adapt to the testing needs of different types of financial APPs.

[0025] In this embodiment, step S2 involves activating the UI automated testing module to simulate the complete user operation process on the target client (which includes mainstream system versions such as iOS, Android, and HarmonyOS) based on a test case library. The simulated operations cover typical scenarios such as app launch, account login, market data query, transaction instruction input and confirmation, position query, fund transfer, and secure account logout. Operation events and data transmission instructions are injected into the client system through system interface calls, achieving accurate reproduction of real user operations and financial business processes. The runtime-related data includes UI hierarchy structure information, function call data, and runtime status data. The UI hierarchy structure information contains multiple nodes and corresponding attributes, including node coordinates and resource identifiers. The function call data includes function number call logs and transaction process response data.

[0026] In this embodiment, the multi-dimensional comparison detection mechanism in step S3 includes: S31. An improved node matching algorithm is used to match nodes in the UI hierarchy structure information to identify unmatched points and matched points with abnormal attributes. Node coordinates are converted based on the device screen size correlation, and UI compatibility defects are verified using a dynamic error judgment algorithm. Specifically, an improved node matching algorithm is used to construct node attribute feature vectors. The vector dimensions include attributes such as node coordinates, resource identifiers, and function association identifiers. The similarity between the node to be tested and the reference node is calculated by weighted summation, as shown in the following formula: (1) in, Let be the weight coefficient of the i-th attribute; S represents the similarity between the node to be tested and the reference node in the i-th attribute; n is the number of attribute dimensions. When S ≥ the preset matching threshold, the node is considered to be a match; otherwise, it is considered an unmatched node.

[0027] Specifically, for matched points, the estimated node coordinates are obtained by converting the reference node coordinates based on the device screen size ratio. A dynamic error judgment algorithm is used to check for UI compatibility defects. The threshold is adjusted by calculating the Euclidean distance and combining it with scene sensitivity. The specific formula is as follows: (2) Where d is the actual node coordinate. Compared with the predicted coordinates Euclidean distance; is the basic error threshold; k is the scene sensitivity coefficient; when the distance exceeds the adjusted threshold, it is marked as a UI compatibility abnormal node.

[0028] S32. To identify defects in the function call data, a function consistency deviation rate algorithm is used to compare the deviation rates of function response indicators from different clients and versions with the reference benchmark indicators. Function response indicators include transaction instruction response time, market data refresh frequency, and function call success rate. The specific formula for the deviation rate algorithm is shown below: (3) in, The functional response indicators of the client under test; As a reference benchmark, when It was determined to be a functional inconsistency defect.

[0029] S33. For financial market data in the running status data and transaction data in the function call data, a data error verification algorithm is used to verify the accuracy of key data, ensuring that the errors in data such as transaction amount and position quantity meet the financial industry standards.

[0030] In this embodiment, step S4, intelligent gray-scale testing, is a crucial step in ensuring the stable operation of financial apps in complex real-world scenarios. It involves a comprehensive, multi-layered gray-scale testing system tailored to the high concurrency, high sensitivity, and strong real-time characteristics of financial transactions. Specifically, intelligent gray-scale testing includes: S41. Construct a dedicated gray-scale testing scenario library for financial scenarios, covering key and extreme scenarios throughout the entire financial business process. The gray-scale testing scenario library includes market fluctuation scenarios (covering different market conditions such as sharp rises, falls, and sideways consolidation, simulating client data processing scenarios under different fluctuation amplitudes and frequencies), high-frequency trading scenarios (simulating the operation mode of professional traders buying and selling multiple times in a short period, testing the client's trading instruction response speed and concurrent processing capabilities), large-scale concurrency scenarios (including scenarios where tens of thousands of users log in simultaneously, query market data, and submit trading instructions, simulating user behavior during peak periods such as the opening and closing of the financial market), and cross-regional data synchronization scenarios (testing the data transmission latency and synchronization consistency between servers and clients in different regions, adapting to the nationwide and even global service needs of financial apps). Each scenario is configured with corresponding test parameters and evaluation standards to ensure the relevance and effectiveness of the testing. Specifically, this embodiment uses a layered stress testing algorithm for confidence gray-scale testing. This algorithm dynamically adjusts the load increase rate through a stress attenuation coefficient to avoid client misjudgments caused by sudden load changes. Layered stress testing gradually increases the stress in stages: basic load, medium load, peak load, and extreme load. The load strength of the i-th layer is determined by the load strength of the (i-1)-th layer, the initial load increase coefficient, the pressure attenuation coefficient, and the value of i. The specific formula of the layered stress test algorithm is as follows: (4) in, Let the load strength be the i-th layer; For the load strength of the (i-1)th layer, This is the initial load boost factor; is the pressure attenuation coefficient; i is the load level.

[0031] S42. A load balancing mechanism is introduced to implement distributed testing, and a load distribution balancing algorithm is used to optimize test task allocation. The algorithm quantifies the degree of load balancing by calculating the deviation between the load of each device and the average load. Specifically, the formula for the load distribution balancing algorithm is as follows: (5) in, For balance; Let j be the load of the j-th device; is the average load; m is the number of devices; when < The load redistribution is triggered when the preset load balancing threshold is reached.

[0032] S43. Identify hidden anomaly events by combining log correlation analysis algorithms. Specifically, a log correlation analysis algorithm is used to perform in-depth analysis of multi-source logs generated during the gray-scale testing process. These multi-source logs include system logs, transaction logs, network logs, and client operation logs. A log correlation confidence model is introduced to quantify the correlation strength between logs, as shown in the following formula: (6) Where C represents the association confidence of the two log types; This represents the number of log entries that appear simultaneously. , These represent the total number of log entries in the two categories, respectively. The scene correlation coefficient; when C > When a pre-set threshold is set, an abnormal correlation is identified.

[0033] In this embodiment, step S5 includes a preset evaluation system comprising core evaluation indicators such as defect severity, scope of impact, probability of occurrence, and relevance to financial business. Each indicator is scored out of 1-5 points. The preset evaluation system employs a comprehensive defect scoring algorithm, the specific formula of which is shown below: (7) Where F represents the overall score; The weight of the k-th indicator can be configured according to the needs of financial institutions. This is the quantitative score for the k-th indicator.

[0034] Specifically, in this embodiment, defects are divided into five levels based on the comprehensive score F: Level 1 defect (F≥4.5 points, fatal defect), Level 2 defect (3.5≤F<4.5 points, serious defect), Level 3 defect (2.5≤F<3.5 points, general defect), Level 4 defect (1.5≤F<2.5 points, minor defect), and Level 5 defect (F<1.5 points, optimization suggestion).

[0035] The system generates differentiated handling suggestions for defects of different levels. Level 1 defects trigger an emergency repair process, requiring immediate processing and retesting. Level 2 defects are included in the iterative repair plan and completed within a specified time. Levels 3 to 5 defects are optimized step-by-step according to business priorities. The final result is a detection report containing defect location, type, level, cause analysis, and optimization suggestions, supporting report viewing, downloading, and defect tracking. For example, a performance bottleneck defect in the HarmonyOS client under high concurrency scenarios, with a severity score of S1=5, an impact range score of S2=4, an occurrence probability score of S3=4, and a financial business relevance score of S4=5, has a comprehensive score of 4.7, thus being classified as a Level 1 defect and requiring the triggering of an emergency repair process. Similarly, a UI mismatch defect in the "Buy Confirmation Button" of the iOS client, with a comprehensive score of F=3.6, is classified as a Level 2 defect and included in the iterative repair plan.

[0036] Example 2: Figure 6 As shown, this embodiment provides a financial software intelligent testing system for implementing the financial software intelligent testing method described in Embodiment 1, including: The test case management module is used to build and manage a test case library adapted to financial scenarios. The test case library covers test cases for core financial business scenarios such as market data viewing, transaction operations, account management, and data refresh. It supports test case classification management, template reuse, and dynamic updates. It supports the writing, classification, modification, deletion, and template reuse of test cases. Test cases can be dynamically optimized according to financial business rules and scenario expansion to meet the testing needs of different types of financial apps. The UI automation testing module is used to simulate the user's operation process on the target client, specifically the operation process of a financial APP on the target client. It collects customer information from the client during the testing process, including UI hierarchy structure information, function call data, and runtime status data. Specifically, it injects operation events and data transmission instructions through system interface calls to collect client-related data (including UI hierarchy structure information, function call data, and runtime status data). It supports multi-system version client testing and automated regression testing of core scenarios. The multi-dimensional comparison and detection module is used to compare the relevant data of the client under test with the reference benchmark data in multiple dimensions. It combines node matching algorithm, dynamic error judgment algorithm, and functional consistency deviation rate algorithm to identify abnormal items and mark them as abnormal nodes. The multi-dimensional comparison and detection mechanism includes UI compatibility verification, functional consistency verification, and financial data accuracy verification. That is, it has built-in core algorithm models such as improved node matching algorithm, dynamic error judgment algorithm, and functional consistency deviation rate algorithm to identify UI compatibility defects, functional consistency defects, and data accuracy defects and mark abnormal nodes. The intelligent gray-scale testing module is used to formulate intelligent gray-scale testing strategies based on the characteristics of financial scenarios. It constructs a dedicated gray-scale testing scenario library for financial scenarios, including market fluctuations, high-frequency trading, large-scale concurrency, and cross-regional data synchronization. Dynamic stress testing is executed through a layered stress testing algorithm, distributed testing is achieved by combining a load balancing algorithm, and multi-dimensional monitoring is completed through log correlation analysis algorithms to detect performance and stability defects of the client under complex scenarios. The intelligent gray-scale testing module includes a scenario configuration unit, a stress testing unit, a load balancing unit, and a monitoring and analysis unit. The scenario configuration unit is used to build and manage the dedicated gray-scale testing scenario library for financial scenarios; the stress testing unit executes a layered stress testing algorithm to smoothly increase test pressure; the load balancing unit executes a load balancing algorithm to optimize test task allocation; and the monitoring and analysis unit monitors the device's operating status in real time, executes a log correlation confidence model, and identifies hidden abnormal events. The defect classification and processing module is used to classify identified defects into at least three levels based on a preset evaluation system that includes defect severity, scope of impact, probability of occurrence, and relevance to financial business. It generates a detection report that includes defect location, type, level, cause analysis, and optimization suggestions. In other words, based on the preset evaluation system, the module uses a defect comprehensive scoring algorithm to quantitatively score and classify the identified defects, generate differentiated processing suggestions, and integrate defect information and processing suggestions to generate a detection report. The report management module is used to manage test reports, supporting the viewing, downloading, sharing and exporting of reports, recording the report execution status and historical data, and providing defect tracking functions to facilitate collaboration between testers and developers to promote defect repair.

[0037] Example 3: Figure 7 As shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0038] The communication bus can be used to enable communication between the various components mentioned above.

[0039] The user interface may include buttons, and optional user interfaces may also include standard wired interfaces and wireless interfaces.

[0040] The network interface may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0041] The processor may include one or more processing cores. It connects various parts of the electronic device via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in memory, and accessing data stored in memory to perform various functions and process data. Optionally, the processor can be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0042] The memory may include RAM or ROM. Optionally, the memory may include a non-transitory computer-readable medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor. The memory, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an evaluation application. The processor can be used to call the evaluation application stored in the memory and execute the steps of the intelligent testing method for financial software mentioned in the foregoing embodiments.

[0043] Example 4: This example provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the above-described instructions. Figure 1 One or more steps in the illustrated embodiment. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0044] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital versatile discs (DVDs)), or semiconductor media (e.g., solid-state drives (SSDs)).

[0045] Those skilled in the art will understand that all or part of the processes in the method of Embodiment 1 described above can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and the implementation scheme can be combined arbitrarily.

[0046] Example 5: This example uses multiple versions of a large brokerage's stock trading app as an example. It constructs a test case library of over 100 financial scenarios, performs automated UI testing and collects data on iOS 17, Android 14, and HarmonyOS 4.0. Through multi-dimensional comparative testing, it quantitatively identifies defects such as mismatched iOS UI, incompatible Android UI, and functional deviations in the HarmonyOS version. Then, through layered gray-scale testing, it locates performance bottlenecks in the HarmonyOS version. Finally, it uses quantitative scoring to classify defects into three levels (Level 1, 2, and 3) to drive targeted fixes. Existing technologies typically rely on manual methods and simple tools, manually simulating client operations (covering only 1-2 system versions), and relying on manual recording for data collection, lacking specific configurations for financial scenarios.

[0047] As shown in Table 1 below, this embodiment is compared with the data of the prior art:

[0048] As shown in Table 1, this application completes client-side defect detection through five steps: building a test case library, executing automated UI testing, multi-dimensional comparative detection, intelligent gray-scale testing, and defect classification and handling. Ultimately, it locates and classifies UI, functional, and performance defects in different versions, promoting targeted fixes. Compared to existing technologies, it offers more comprehensive coverage, higher accuracy, and over 90% efficiency improvement.

[0049] The above-described specific embodiments are preferred embodiments of a financial software intelligent testing method, system, device, and medium of this application, and are not intended to limit the specific scope of this application. The scope of this application includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. A method for intelligent testing of financial software, characterized in that, Includes the following steps: S1. Construct a test case library adapted to financial scenarios. The test case library covers test cases for core financial business scenarios such as market data viewing, trading operations, account management, and data refresh. It supports test case classification management, template reuse, and dynamic updates. S2. Start UI automated testing to simulate the user's operation process of the financial APP on the target client and collect customer information of the client during the test. The customer information includes UI hierarchy structure information, function call data and running status data. S3. Based on a multi-dimensional comparison detection mechanism, defects are identified by comparing the customer information of the client under test with the reference benchmark data in multiple dimensions. Anomalies are identified by combining node matching algorithm, dynamic error judgment algorithm, and functional consistency deviation rate algorithm, and marked as abnormal nodes. The multi-dimensional comparison detection mechanism includes UI compatibility verification, functional consistency verification, and financial data accuracy verification. S4. Develop intelligent gray-scale testing strategies based on the characteristics of financial scenarios, build a dedicated gray-scale testing scenario library for financial scenarios including market fluctuations, high-frequency trading, large-scale concurrency, and cross-regional data synchronization, execute dynamic stress testing through layered stress testing algorithms, achieve distributed testing by combining load distribution balancing algorithms, and complete multi-dimensional monitoring through log correlation analysis algorithms to detect performance and stability defects of the client in complex scenarios. S5. Based on a pre-defined evaluation system that includes the severity of defects, scope of impact, probability of occurrence, and relevance to financial business, identify defects and perform at least three levels of classification processing to generate a detection report that includes the location, type, level, cause analysis, and optimization suggestions of the defects.

2. The intelligent testing method for financial software according to claim 1, characterized in that, The UI hierarchy information includes multiple nodes and corresponding attributes, the attributes of which include at least one of node coordinates and resource identifiers; the function call data includes at least one of function number call logs and transaction process response data; the running status data includes at least one of CPU utilization, memory utilization, and data transmission latency.

3. The intelligent testing method for financial software according to claim 1, characterized in that, The multi-dimensional comparison detection mechanism includes: S31. A node matching algorithm is used to match nodes in the UI hierarchy structure information to identify unmatched points and matched points with abnormal attributes; the node coordinates are converted based on the device screen size correlation, and UI compatibility defects are verified through a dynamic error judgment algorithm. S32. Identify defects by comparing function call data and using a function consistency deviation rate algorithm; S33. For the financial market data in the running status data and the transaction data in the function call data, a data error verification algorithm is used to verify the accuracy of key data to ensure that the errors in transaction amount and position quantity meet the financial industry standards.

4. The intelligent testing method for financial software according to claim 1, characterized in that, The intelligent grayscale test includes: S41. Construct a dedicated gray-scale test scenario library for financial scenarios, and gradually increase the test pressure using a layered stress test algorithm; S42. Introduce a load balancing mechanism to implement distributed testing, and use a load distribution balance algorithm to optimize test task allocation; S43. Identify hidden abnormal events by combining log correlation analysis algorithms.

5. The intelligent testing method for financial software according to claim 4, characterized in that, The layered stress test algorithm dynamically adjusts the load increase rate through the pressure attenuation coefficient. The pressure is gradually increased in each layer of the layered stress test. The load intensity of the i-th layer is determined by the load intensity of the (i-1)-th layer, the initial load increase coefficient, the pressure attenuation coefficient, and the value of i.

6. The intelligent testing method for financial software according to claim 1, characterized in that, The preset evaluation system uses a comprehensive defect scoring algorithm to achieve quantitative grading, as shown in the following formula: （7） Where F represents the overall score; The weight of the k-th indicator can be configured according to the needs of financial institutions. The score is the quantitative score of the k-th indicator, ranging from 1 to 5 points. Based on the value of the comprehensive score F, the defects are divided into five levels: Level 1 defects are F≥4.5 points, Level 2 defects are 3.5≤F<4.5 points, Level 3 defects are 2.5≤F<3.5 points, Level 4 defects are 1.5≤F<2.5 points, and Level 5 defects are F<1.5 points.

7. A financial software intelligent testing system, characterized in that, The method for implementing the intelligent testing method for financial software as described in any one of claims 1-6 includes: The test case management module is used to build and manage a test case library adapted to financial scenarios. The test case library covers test cases for core financial business scenarios such as market data viewing, trading operations, account management, and data refresh, and supports test case classification management, template reuse, and dynamic updates. The UI automation testing module is used to simulate the user's operation process on the target client's financial APP and collect customer information from the client during the testing process. The customer information includes UI hierarchy structure information, function call data and running status data. The multi-dimensional comparison and detection module is used to compare the customer information of the client under test with the reference benchmark data in multiple dimensions, and to identify abnormal items by combining the node matching algorithm, dynamic error judgment algorithm, and functional consistency deviation rate algorithm, and mark them as abnormal nodes; the multi-dimensional comparison and detection mechanism includes UI compatibility verification, functional consistency verification, and financial data accuracy verification. The intelligent gray-scale testing module is used to formulate intelligent gray-scale testing strategies based on the characteristics of financial scenarios. It builds a dedicated gray-scale testing scenario library for financial scenarios, including market fluctuations, high-frequency trading, large-scale concurrency, and cross-regional data synchronization. It executes dynamic stress testing through layered stress testing algorithms, achieves distributed testing by combining load distribution balancing algorithms, and completes multi-dimensional monitoring through log correlation analysis algorithms to detect performance and stability defects of the client in complex scenarios. The defect classification and processing module is used to classify identified defects into at least three levels based on a preset evaluation system that includes defect severity, scope of impact, probability of occurrence, and relevance to financial business, and generate a detection report that includes defect location, type, level, cause analysis, and optimization suggestions. The report management module is used to manage test reports and supports viewing, downloading, and defect tracking.

8. The intelligent testing system for financial software according to claim 7, characterized in that, The intelligent gray-scale testing module includes a scenario configuration unit, a stress testing unit, a load balancing unit, and a monitoring and analysis unit. The stress testing unit executes a layered stress testing algorithm, the load balancing unit executes a load distribution balance algorithm, and the monitoring and analysis unit executes a log association confidence model.

9. A computer device, the computer device comprising a memory, a processor, and a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent testing method for financial software as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the intelligent testing method for financial software as described in any one of claims 1-6.

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