Mobile phone application performance automatic test system and method based on artificial intelligence

Abstract

The invention discloses a mobile phone application performance automatic test system and method based on artificial intelligence, and relates to the technical field of electric digital data processing. The system comprises a sampling condition monitoring module, an automatic sampling adjustment module and a test feedback module. The mobile phone application performance is automatically tested based on the artificial intelligence algorithm in the current test period, the sampling condition of the test data is monitored in real time, then whether automatic sampling adjustment is executed or not is determined according to the sampling frequency adaptation judgment result, and if yes, automatic sampling adjustment is executed. If yes, the data noise condition of the test data is monitored after automatic sampling adjustment, test feedback is carried out after data noise degree judgment, whether sampling secondary adjustment is carried out or not is determined, and otherwise, the data sampling condition continues to be monitored, so that the adaptation degree of the test condition of the test data and the sampling frequency is improved; the problem that in the prior art, in the process of testing the application performance of the mobile phone, the adaptation degree between the sampling frequency and the corresponding testing condition is low is solved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of electric digital data processing, in particular to a mobile phone application performance automatic test system and method based on artificial intelligence. BACKGROUND

[0002] The existing artificial intelligence driven mobile phone performance test system is generally composed of a data acquisition layer, a scene driving layer, an AI (Artificial Intelligence) analysis and modeling layer and an optimization and feedback layer. The data acquisition layer uses a Hook system interface, a hardware monitoring API and performance monitoring tools provided by Android / iOS (iPhone Operating System) to collect indicators such as CPU (Central Processing Unit), memory, GPU (Graphics Processing Unit), power consumption, FPS (Frames Per Second), network delay and the like. The scene driving layer, that is, test case execution, intelligently generates operation steps by combining an AI model (reinforcement learning, behavior prediction model) to cover more user usage paths through automatic scripts or simulated user behaviors. Common frameworks include Appium, UIAutomator (User Interface Automation Test Framework, provided by Android) and XCUITest (Xcode UI Test, Xcode User Interface Test Framework, provided by Apple). The AI analysis and modeling layer uses AI algorithms to analyze the collected data, including anomaly detection (LSTM-Long Short-Term Memory, AutoEncoder to capture performance fluctuations), convergence analysis (training model to evaluate whether performance optimization tends to be stable), root cause positioning (using graph models or feature importance analysis to find performance bottlenecks) to predict the performance of the application under different hardware environments and different loads. The optimization and feedback layer automatically generates performance optimization suggestions or directly adjusts the sampling frequency, task scheduling strategy and power consumption control parameters based on the AI analysis results, supports a closed-loop optimization from testing to analysis to adjustment and then to retesting.

[0003] For example, the Chinese invention patent with the publication number CN115145797A discloses a method, device and equipment for application performance testing and a storage medium, which comprises the following steps: based on the running state monitoring information of the device where the application to be tested is located, a preset number of target processes running in the device are determined; the running occupancy rate of each target process in the device is determined to obtain the performance test data of the application to be tested.

[0004] For example, the Chinese invention patent with publication number CN109710521B discloses a multimedia application performance test method, device, computer equipment and storage medium, which comprises: obtaining a multimedia application test request, wherein the test request includes the identification of the target multimedia application; starting the target multimedia application corresponding to the identification of the target multimedia application in the current device; obtaining the performance parameters of the current device and the first operation information of the target multimedia application during the running of the target multimedia application; determining the performance of the target multimedia application in the current device according to the performance parameters of the current device and the first operation information of the target multimedia application The above technology at least has the following technical problems: Due to the complex environment of mobile phones, such as system processes, network fluctuations and background tasks, the performance indicators (CPU, power consumption, etc.) fluctuate significantly, leading the artificial intelligence to mistakenly consider data noise as abnormal. At the same time, mobile operating systems have high dynamics, their background processes are uncontrollable, and they also have resource recycling and scheduling mechanisms, which limit data collection. If the sampling frequency is higher, short-term fluctuations may occur, misleading the artificial intelligence to amplify the corresponding abnormalities, and vice versa, which may miss critical abnormal points. Therefore, how to match the sampling frequency during the testing process of the performance of the mobile phone application with the corresponding test situation is one of the problems to be solved. SUMMARY

[0005] To solve the technical problem of low adaptation of sampling frequency to corresponding test situation during the testing process of the performance of the mobile phone application in the prior art, the embodiments of the present application provide an artificial intelligence-based mobile phone application performance automatic testing system and method. The technical solution is as follows: On the one hand, an artificial intelligence-based mobile phone application performance automatic testing system is provided, which comprises a sampling condition monitoring module, an automatic sampling adjustment module and a test feedback module. The sampling condition monitoring module is used to automatically test the performance of the mobile phone application based on artificial intelligence algorithm in the current test period, and to monitor the sampling condition of the corresponding test data in real time to make sampling frequency adaptation determination to determine the degree of fit between the sampling frequency and the current test condition. The automatic sampling adjustment module is used to determine whether to perform automatic sampling adjustment for improving sampling adaptation based on the result of sampling frequency adaptation determination. The test feedback module is used to monitor the data noise condition of the test data after the automatic sampling adjustment if the automatic sampling adjustment is performed, and to make corresponding test feedback after making data noise degree determination, and to determine whether to perform sampling secondary adjustment, otherwise to continue monitoring the data sampling condition of the automatic testing of the performance of the mobile phone application.

[0006] In another aspect, the application provides an artificial intelligence-based automatic testing method for mobile phone application performance, which comprises the following steps: automatically testing the performance of a mobile phone application based on an artificial intelligence algorithm in a current testing cycle, and monitoring the sampling of the corresponding test data in real time to determine the sampling frequency adaptation, so as to determine the degree of fit between the sampling frequency and the current testing situation; determining whether to perform automatic sampling adjustment for improving the sampling adaptation degree according to the result of the sampling frequency adaptation determination; if the automatic sampling adjustment is performed, monitoring the data noise of the test data after the automatic sampling adjustment, and performing corresponding test feedback after determining the degree of data noise, and determining whether to perform secondary sampling adjustment, otherwise, continue to monitor the data sampling of the automatic testing of the performance of the mobile phone application.

[0007] The technical scheme provided by the embodiments of the application has at least the following beneficial effects: 1. In the process of automatically testing the performance of a mobile phone application based on an artificial intelligence algorithm in a current testing cycle, the sampling of the corresponding test data is monitored in real time, so that the sampling frequency adaptation is determined, and the degree of fit between the sampling frequency and the current testing situation is determined, which helps to more accurately quantify the sampling accuracy of the test data, and then determines whether to perform automatic sampling adjustment according to the result of the sampling frequency adaptation determination, which helps to improve the adaptation of the sampling frequency and the testing situation, and if the automatic sampling adjustment is performed, the data noise of the test data is monitored after the automatic sampling adjustment, the optimization effect on the data noise after optimization is understood in time, and then the optimization is continuously improved, corresponding test feedback is performed after determining the degree of data noise, so that the sampling situation of the test data is reflected in time, and it is determined whether to perform secondary sampling adjustment, the closed-loop regulation and control of the sampling frequency is realized, and then the adaptation of the testing situation of the test data and the sampling frequency is improved, otherwise, the data sampling of the automatic testing of the performance of the mobile phone application is continuously monitored, and the continuity of the monitoring of the sampling situation of the test data is ensured.

[0008] 2. First, the test data is collected based on each initial test sampling frequency, and the test sampling frequency applicable analysis parameter of the current testing cycle is obtained, which helps to more accurately understand the current testing situation, then the sampling frequency analysis factor is obtained according to the test sampling frequency applicable analysis parameter, so as to more accurately quantify the demand of the test data of the current testing cycle for the sampling frequency, and then the corresponding optimized sampling frequency interval is obtained based on the mapping of the sampling frequency analysis factor, which helps to automatically determine whether the sampling frequency meets the current testing situation, if the initial test sampling frequency belongs to the corresponding optimized sampling frequency interval, it means that the current sampling frequency is consistent with the testing situation, then the sampling accuracy is optimized, which helps to further improve the sampling accuracy of the test data, otherwise, the adaptive automatic sampling adjustment is performed, so as to ensure that the sampling frequency is consistent with the sampling demand of the test data.

[0009] 3、After the automatic sampling adjustment is performed, the data noise of the test data is monitored and the corresponding data noise evaluation parameter is obtained, which helps to more intuitively understand the current noise condition, then the data noise evaluation parameter is compared with the extracted data noise judgment parameter and the corresponding sampling frequency secondary adjustment score is recorded, not only realizing the automatic judgment of the qualified degree of data noise, but also more accurately quantifying the influence degree of data noise, finally the sampling frequency secondary adjustment score cumulative value is counted to determine the degree of data noise, so as to realize the automatic decision whether to perform secondary adjustment, which not only improves the efficiency of system decision, but also further improves the sampling precision of test data.

[0010] 4、If the initial test sampling frequency is lower than the minimum value of the optimized sampling frequency interval, the to-be-increased gap amount between the initial test sampling frequency and the minimum value of the optimized sampling frequency interval is obtained, which realizes the quantification of the degree of sampling frequency to be increased and the automatic increase of sampling adjustment with limited increase amplitude, which not only limits the increase amplitude of the sampling frequency, but also helps to ensure the stability of the sampling frequency increase processing, if the initial test sampling frequency is higher than the maximum value of the optimized sampling frequency interval, the to-be-decreased gap amount between the initial test sampling frequency and the maximum value of the optimized sampling frequency interval is obtained, which quantifies the degree of sampling frequency to be decreased and automatically reduces the sampling adjustment with limited decrease amplitude, so as to limit the decrease amplitude of the sampling frequency and further reduce the influence of sampling frequency adjustment on system stability. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0012] Figure 1 is a structural schematic diagram of the mobile phone application performance automatic test system based on artificial intelligence provided by the embodiment of the present application; Figure 2 is a process schematic diagram of sampling frequency adaptation determination provided by the embodiment of the present application; Figure 3 is a process schematic diagram of monitoring the data noise of test data provided by the embodiment of the present application; Figure 4 is a process schematic diagram of the mobile phone application performance automatic test method based on artificial intelligence provided by the embodiment of the present application. DETAILED DESCRIPTION

[0013] The technical solutions in the present application will be described below with reference to the drawings.

[0014] In the embodiments of the present application, the words such as "example", "for example" are used to represent an example, illustration, or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0015] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. "Of", "corresponding" and "corresponding" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0016] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0017] In order to make the technical problems, technical schemes and advantages to be solved by the present application more clear, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0018] The embodiments of the present application provide an artificial intelligence-based mobile application performance automatic testing system and method. As shown in the structural schematic diagram of the artificial intelligence-based mobile application performance automatic testing system, the system comprises a sampling condition monitoring module, an automatic sampling adjustment module and a test feedback module. Figure 1 The sampling condition monitoring module is configured to perform automatic testing on the mobile application performance based on an artificial intelligence algorithm in a current test period, and to monitor the sampling condition of the corresponding test data in real time to determine the sampling frequency adaptation and to determine the degree of fit between the sampling frequency and the current test condition.

[0019] The automatic sampling adjustment module is configured to determine whether to perform automatic sampling adjustment for improving the sampling adaptation degree according to the specific result of the sampling frequency adaptation determination.

[0020] The test feedback module is configured to monitor the data noise condition of the test data after the automatic sampling adjustment if the automatic sampling adjustment is performed, to perform corresponding test feedback after the data noise degree determination, and to determine whether to perform secondary sampling adjustment, otherwise to continue monitoring the data sampling condition of the automatic testing of the mobile application performance.

[0021] ​In this embodiment, the performance of the mobile phone application is tested automatically based on an artificial intelligence algorithm, and the sampling of the test data is monitored in real time. The matching degree between the sampling frequency and the current test scene can be dynamically perceived, and the problems of over-sampling (waste of resources) or under-sampling (missing performance fluctuations) caused by a fixed sampling frequency can be avoided, thereby ensuring that the sampling frequency is highly consistent with the actual test requirements. According to the result of the sampling frequency adaptation determination, it is determined whether automatic sampling adjustment is needed, the sampling frequency is adaptively adjusted, the system can automatically optimize the sampling strategy according to the volatility and stability of the performance data, thereby improving the accuracy and efficiency of sampling, and reducing the cost of manual intervention. After automatic sampling adjustment, the noise level of the sampling data is continuously monitored, data noise determination is performed, test feedback is then given, and it is determined whether to perform secondary sampling adjustment. Through feedback and further adjustment of the data noise, the stability and reliability of the sampling data are ensured, the accuracy and reproducibility of the performance test results are improved, and excessive redundant sampling is avoided, thereby enhancing the intelligence and robustness of the test system.

[0022] As shown in Figure 2 FIG. 1 is a flowchart of a process for determining the sampling frequency adaptation provided by an embodiment of the present application. The specific logic is as follows: based on each initial test sampling frequency, each test data is collected, and the test sampling frequency adaptation analysis parameters of the current test period are obtained. The sampling frequency analysis factor is obtained according to the test sampling frequency adaptation analysis parameters, and the corresponding optimized sampling frequency interval is mapped and output based on the sampling frequency analysis factor; the initial test sampling frequency is matched with the corresponding optimized sampling frequency interval: if the initial test sampling frequency belongs to the corresponding optimized sampling frequency interval, the sampling accuracy is optimized to improve the sampling accuracy of the test data, otherwise the adaptive automatic sampling adjustment is performed; wherein the specific content of the sampling accuracy optimization is as follows: The median of the optimized sampling frequency interval is denoted as the optimized sampling median, and the optimized-initial sampling gap value is obtained by quantifying the difference between the optimized sampling median and the initial test sampling frequency. The optimized-initial sampling gap value is compared with the set sampling median error range: if the optimized-initial sampling gap value belongs to the sampling median error range, the sampling accuracy optimization is not performed, otherwise the corresponding best sampling frequency adjustment amplitude is obtained by quantifying the distance of the optimized-initial sampling gap value. If the optimized-initial sampling gap value is greater than the optimization direction critical value, the increase adjustment is performed based on the best sampling frequency adjustment amplitude, and if the optimized-initial sampling gap value is less than the optimization direction critical value, the decrease adjustment is performed based on the best sampling frequency adjustment amplitude. Through the above process, the adaptation degree of the test situation and the data sampling frequency is more accurately quantified, and the inadaptation situation can be adjusted in time, thereby improving the sampling accuracy.

[0023] Further, the specific process of determining the sampling frequency adaptation is as follows: R1 collects test data based on each initial test sampling frequency and obtains the applicable analysis parameters for the test sampling frequency of the current test cycle. The applicable analysis parameters for the test sampling frequency include CPU utilization, memory usage, power consumption, and FPS.

[0024] It should be added that if the phone's operating system is Android, CPU usage can be obtained using the `top / dumpsys cpuinfo` command and `adb shell dumpsys meminfo`.<package_name> Metrics such as PSS, RSS, and Heap are obtained to represent memory usage. Power consumption is obtained via `adb shell dumpsys batterystats`, and FPS is obtained via `adb shell dumpsys gfxinfo`.<package_name> If the phone system is iOS, CPU usage is obtained using the Xcode Instruments command-line tool, memory usage is obtained through performance diagnostics in Xcode Organizer, power consumption is obtained through the power consumption report provided by Xcode, and FPS is obtained through Instruments (Core Animation tool).

[0025] R2, based on the applicable analysis parameters of the test sampling frequency, obtains the sampling frequency analysis factor used to quantify the sampling frequency requirements of the test data in the current test cycle. Based on the sampling frequency analysis factor in a pre-constructed sampling frequency projection sequence used to fit the mapping relationship between the sampling frequency analysis factor and the optimized sampling frequency interval, the corresponding optimized sampling frequency interval is output.

[0026] Specifically, the sampling frequency projection sequence is pre-built in a preset database. The sampling frequency analysis factors are input into the trained sampling frequency projection sequence to output the corresponding optimized sampling frequency range. The training data used in this sampling frequency projection sequence comes from sampling frequency analysis factors acquired in historical time periods, as well as optimized sampling frequency ranges set by professional technicians based on empirical rules. This data is used to fit the mapping relationship between the sampling frequency analysis factors and the optimized sampling frequency ranges, thereby enabling a more accurate analysis of the sampling frequency fit.

[0027] R3 matches the initial test sampling frequency with the corresponding optimized sampling frequency range: if the initial test sampling frequency belongs to the corresponding optimized sampling frequency range, it means that the current sampling frequency matches the test situation, and the sampling accuracy is optimized to improve the sampling accuracy of the test data; otherwise, the adaptive automatic sampling adjustment is executed.

[0028] In the embodiment, based on the initial sampling frequency, the analysis parameters (CPU, memory, power consumption, FPS) are collected and obtained, the rationality of the sampling frequency can be comprehensively evaluated from multiple performance indicators, the sampling frequency determination is comprehensive and representative, and the deviation caused by a single indicator is avoided; meanwhile, the sampling frequency analysis factor is mapped to the optimized sampling frequency interval, the pre-constructed sampling frequency projection sequence is used to realize the accurate mapping between the sampling frequency requirement and the optimized sampling interval, the test requirements in different scenes can be adaptively met, the scientificity and intelligent level of the determination are improved, the matching and determination of the initial sampling frequency and the optimized interval are matched, and it is quickly judged whether the current sampling frequency is reasonable through the matching mechanism, if it is consistent, only the sampling precision optimization is performed, if it is not consistent, the automatic sampling adjustment is triggered, so that the dynamic matching of the sampling frequency and the test scene is realized, the sampling precision is further improved in the matching, the automatic adjustment is performed in the non-matching, the sampling can meet the requirement of performance fluctuation capture, and resource waste caused by oversampling is avoided, and the balance between test efficiency and sampling precision is realized.

[0029] Further, the specific acquisition method of the sampling frequency analysis factor is as follows: R21, the data of the test sampling frequency suitable analysis parameter is normalized, and the pre-stored test sampling frequency suitable analysis proportion is extracted, the test sampling frequency suitable analysis proportion includes the CPU occupancy proportion, the memory occupancy proportion, the power consumption proportion and the FPS proportion.

[0030] It should be explained that the test sampling frequency suitable analysis proportion is extracted based on the test sampling frequency mapping matching table in the preset database, the test sampling frequency mapping matching table is a data table reflecting the mapping relationship between the test sampling frequency suitable analysis parameter and the corresponding test sampling frequency suitable analysis proportion, the real-time test sampling frequency suitable analysis parameter is input into the test sampling frequency mapping matching table, and the corresponding CPU occupancy proportion, memory occupancy proportion, power consumption proportion and FPS proportion are output, which respectively represent the influence degree of CPU occupancy, memory occupancy, power consumption and FPS on the sampling frequency analysis factor, so that a more accurate sampling frequency analysis factor is obtained.

[0031] R22, the sampling frequency analysis factor is obtained by coupling the test sampling frequency suitable analysis parameter after the weighting operation based on the test sampling frequency suitable analysis proportion.

[0032] The specific limit expression of the sampling frequency analysis factor is as follows: ; In the formula, x a represents the CPU occupancy, x b represents the memory occupancy, x c represents the power consumption, and x dFPS, i a CPU, i b Memory, i c Power, i d FPS, i

[0033] In the embodiment, the algorithm combines the test sampling frequency applicable analysis parameters and the corresponding test sampling frequency applicable analysis proportions to comprehensively analyze the sampling frequency analysis factor, wherein, as the CPU occupancy, memory occupancy, power consumption and FPS increase, the test situation of the current test period may change faster, and thus the demand for the sampling frequency of the test data may be higher, and the corresponding sampling frequency analysis factor may increase accordingly. Meanwhile, the test sampling frequency applicable analysis parameters are also related to each other, for example, when the CPU frequency increases and the core number increases, the power consumption increases because the current and voltage load is larger, when the memory is tight, the CPU needs to spend more time on garbage collection, memory allocation or page swapping in and out, which leads to an increase in CPU occupancy, and excessive power consumption leads to device heating, triggering thermal throttling to reduce FPS, and when the memory is insufficient or the CPU is overloaded, the application will appear to be stuck, leading to a decrease in FPS. Through the above analysis, it is helpful to more accurately understand the adaptation degree between the test situation and the sampling frequency, so as to take corresponding optimization adjustment according to the analysis result, and thus improve the adaptation degree between the sampling demand of the test data and the actual sampling frequency.

[0034] Further, the specific content of the sampling precision optimization is as follows: R31, the median value of the optimized sampling frequency interval is recorded as the optimized sampling median value, and the optimized-initial sampling gap value is obtained by quantifying the difference between the optimized sampling median value and the initial test sampling frequency, which represents the subtraction operation between the optimized sampling median value and the initial test sampling frequency.

[0035] R32, the optimized-initial sampling gap value is compared with the preset sampling median error range, if the optimized-initial sampling gap value belongs to the sampling median error range, the sampling precision optimization is not performed, otherwise the distance quantization value of the optimized-initial sampling gap value is input into the pre-fitted sampling frequency fitting function to output the corresponding optimal sampling frequency adjustment amplitude; wherein, the distance quantization value represents taking the absolute value of the optimized-initial sampling gap value.

[0036] It needs to be explained that the sampling frequency fitting function is obtained by linear fitting of the linear function according to the sampling frequency fitting training data, wherein the sampling frequency fitting training data includes the distance quantization value of the optimized-initial sampling gap value and the optimal sampling frequency adjustment amplitude set by the preset staff, first, a linear function (such as a linear function y=ax+b) is assumed based on the sampling frequency fitting training data, then the least square method is used to solve the parameters (i.e. the parameters a and b in the aforementioned linear function), and finally numpy.polyfit() and / or scipy.optimize.curve_fit() in Python is used for fitting to obtain the corresponding sampling frequency fitting function, which is used to fit the mapping relationship between the distance quantization value of the optimized-initial sampling gap value and the optimal sampling frequency adjustment amplitude.

[0037] R33, if the optimized-initial sampling gap value is greater than the optimization direction critical value, the initial test sampling frequency is adjusted based on the optimal sampling frequency adjustment amplitude; wherein the optimization direction critical value is set by a professional test technician in advance and stored in a preset database, and 0 is taken as the optimization direction critical value.

[0038] R34, if the optimized-initial sampling gap value is less than the optimization direction critical value, the initial test sampling frequency is adjusted based on the optimal sampling frequency adjustment amplitude.

[0039] In this embodiment, by calculating the gap between the optimized sampling median value and the initial sampling frequency, the deviation degree of the current sampling frequency from the optimal sampling interval can be accurately quantified, providing a scientific basis for subsequent adjustment; the sampling median error range is introduced for judgment, and the corresponding error tolerance range is set, which can avoid unnecessary frequent adjustment when the gap is small, thereby maintaining system stability and reducing the overhead caused by sampling frequency jitter; through the pre-fitted sampling frequency function, the gap value is converted into the optimal adjustment amplitude, realizing the accurate adjustment of the sampling frequency, ensuring that the adjustment amplitude is neither insufficient nor excessive, thereby improving the accuracy and reliability of sampling optimization; at the same time, according to the comparison result of the gap value and the critical value, the sampling frequency is respectively executed to be increased or decreased, which can flexibly adapt to different performance test requirements, ensure that the sampling frequency adjustment has directionality and pertinence, and improve the effectiveness of the test result.

[0040] Further, the specific process of executing the adaptive automatic sampling adjustment is as follows: In one aspect, if the initial test sampling frequency is lower than the minimum value of the optimized sampling frequency interval, a to-be-increased gap amount between the initial test sampling frequency and the minimum value of the optimized sampling frequency interval is obtained, and a corresponding increase amplitude limit for limiting the increase amplitude of the sampling frequency is automatically increased to adjust the sampling frequency; wherein the to-be-increased gap amount is a difference between the initial test sampling frequency and the minimum value of the optimized sampling frequency interval.

[0041] It should be noted that the increase amplitude limit is preset by a professional test technician based on specific requirements of mobile phone application performance testing, and is stored in a preset database.

[0042] On the other hand, if the initial test sampling frequency is higher than the maximum value of the optimized sampling frequency interval, a to-be-decreased gap amount between the initial test sampling frequency and the maximum value of the optimized sampling frequency interval is obtained, and a corresponding decrease amplitude limit for limiting the decrease amplitude of the sampling frequency is automatically decreased to adjust the sampling frequency; wherein the to-be-decreased gap amount is a difference between the initial test sampling frequency and the maximum value of the optimized sampling frequency interval.

[0043] It should be noted that the decrease amplitude limit is preset by a professional test technician based on specific requirements of mobile phone application performance testing, and is stored in a preset database.

[0044] In this embodiment, the increase adjustment when the sampling frequency is lower than the minimum value of the optimized interval can automatically calculate the gap and reasonably increase the sampling frequency when the sampling frequency is insufficient to reflect the performance fluctuation, so as to avoid missing key performance data due to insufficient sampling, thereby ensuring the integrity and accuracy of performance testing; the decrease adjustment when the sampling frequency is higher than the maximum value of the optimized interval can automatically identify and decrease the amplitude under control when the sampling frequency is too high to waste system resources, thereby avoiding the additional overhead caused by oversampling, improving test efficiency and system energy efficiency; through the amplitude limit constraint, the test process is prevented from being unstable due to one-time adjustment of the sampling frequency, gradual adjustment is realized, and the safety and robustness of the sampling adjustment are improved.

[0045] Further, the specific steps of the automatic increase sampling adjustment are as follows: T1, if the to-be-increased gap amount is not greater than the increase amplitude limit, the initial test sampling frequency is increased by the to-be-increased gap amount in the next test period.

[0046] Specifically, the to-be-increased gap amount is input into a preset increase mapping set to output a corresponding increase amplitude, the increase amplitude is multiplied by the initial test sampling frequency to obtain a corresponding optimized increase sampling frequency, and sampling is performed based on the optimized increase sampling frequency. The increase mapping set is a data set reflecting the mapping relationship between the to-be-increased gap amount and the increase amplitude, and the increase amplitude training data in the historical data is used for training. The increase amplitude training data includes the to-be-increased gap amount in the historical time period and the increase amplitude set by the professional test technicians according to the experience rule, so as to output a more accurate increase amplitude, and then more accurately adjust the initial test sampling frequency.

[0047] T2, if the to-be-increased gap amount is greater than the increase amplitude limit, the initial test sampling frequency is increased by the increase amplitude limit in the next test period, and a difference between the to-be-increased gap amount and the increase amplitude limit is obtained to obtain an increase difference.

[0048] It should be explained that the increase amplitude limit is input into a preset increase amplitude projection sequence to obtain a corresponding increase limit amplitude, the increase limit amplitude is multiplied by the initial test sampling frequency to obtain a corresponding optimized increase limit sampling frequency, and sampling is performed based on the optimized increase limit sampling frequency. The increase amplitude projection sequence is pre-constructed in the preset database, and the increase amplitude limit is input into the trained increase amplitude projection sequence to output a corresponding increase limit amplitude. The training data used by the increase amplitude projection sequence is obtained from the increase amplitude limit in the historical period and the increase limit amplitude set by the professional technicians according to the experience rule, which is used to fit the mapping relationship between the increase amplitude limit and the increase limit amplitude, so as to more accurately adjust the initial test sampling frequency.

[0049] T3, the increase difference is input into a test period mapping table of the increase difference and the test period reduction ratio in the database to output a corresponding test period reduction ratio.

[0050] It should be noted that the test period mapping table is pre-stored in the preset database, and is used to reflect the mapping relationship between the increase difference and the test period reduction ratio, and is trained based on the test period reduction training data. The test period reduction training data includes the increase difference in the preset time period in the historical data and the test period reduction ratio set by the professional test technicians according to the experience rule. In use, the real-time obtained increase difference is input into the test period mapping table, and the corresponding test period reduction ratio is output, so that the reduction of the test period is realized.

[0051] T4, reducing the test period of the automatic test of the performance of the mobile phone application based on artificial intelligence by the test period reduction ratio; the reduction means that the test period reduction ratio is multiplied by the test period.

[0052] In the embodiment, when the gap to be increased does not exceed the amplitude limit, the equal amount of increase can be directly performed in the next test period, ensuring the rapid response and real-time of the sampling frequency adjustment, and improving the sensitivity of the test; and when the gap to be increased exceeds the amplitude limit, the limit is increased and the remaining difference is recorded, avoiding the system instability caused by the one-time adjustment of the sampling frequency, and maintaining the continuity and controllability of the adjustment through the accumulated difference; at the same time, through the preset difference-test period reduction ratio mapping table, the remaining increase difference is converted into the shortening of the test period, so as to realize the accelerated convergence of the test, and ensure that the more accurate data can be obtained through the higher sampling frequency when the adjustment is insufficient; finally, by shortening the test period, the automatic performance test based on artificial intelligence can run at a higher frequency, improving the sampling accuracy and performance fluctuation capture ability, thereby improving the accuracy and robustness of the test result.

[0053] Further, the specific steps of automatically reducing the sampling adjustment are as follows: D1, if the gap to be reduced is not greater than the reduction amplitude limit, the initial test sampling frequency is reduced by the gap to be reduced in the next test period.

[0054] Specifically, the gap to be reduced is input into the preset reduction mapping set to output the corresponding reduction amplitude, the reduction amplitude and the initial test sampling frequency are multiplied to obtain the corresponding optimized reduction sampling frequency, and sampling is performed based on the optimized reduction sampling frequency, wherein the reduction mapping set is a data set reflecting the mapping relationship between the gap to be reduced and the reduction amplitude, the reduction amplitude training data in the historical data is used for training, the reduction amplitude training data includes the gap to be reduced in the historical period and the reduction amplitude set by the professional test technicians according to the experience rule, so as to output more accurate reduction amplitude, and then more accurately adjust the initial test sampling frequency.

[0055] D2, if the gap to be reduced is greater than the reduction amplitude limit, the initial test sampling frequency is reduced by the reduction amplitude limit in the next test period, and the difference between the gap to be reduced and the reduction amplitude limit is obtained to obtain the reduction difference.

[0056] It needs to be explained that the reduction amplitude limit is input into the pre-set reduction amplitude projection sequence projection to obtain the corresponding reduction limit amplitude, and the reduction limit amplitude is multiplied by the initial test sampling frequency to obtain the corresponding optimized reduction limit sampling frequency, and sampling is performed based on the optimized reduction limit sampling frequency; the reduction amplitude projection sequence is pre-constructed in the pre-set database, and the reduction amplitude limit is input into the trained reduction amplitude projection sequence to output the corresponding reduction limit amplitude. The training data of the reduction amplitude projection sequence comes from the reduction amplitude limit obtained in the historical period and the reduction limit amplitude set by the professional technical personnel according to the experience rule, which is used to fit the mapping relationship between the reduction amplitude limit and the reduction limit amplitude, so as to more accurately adjust the initial test sampling frequency.

[0057] D3, input the reduction difference into the test cycle mapping table of the reduction difference and the test cycle amplification ratio in the database to output the corresponding test cycle amplification ratio.

[0058] It needs to be explained that the test cycle mapping table is pre-stored in the pre-set database, which is used to reflect the mapping relationship between the reduction difference and the test cycle reduction ratio, which is trained based on the test cycle reduction training data, which includes the reduction difference in the pre-set time period in the historical data and the test cycle reduction ratio set by the professional test technical personnel according to the experience rule; in use, the real-time obtained reduction difference is input into the test cycle mapping table, and the corresponding test cycle reduction ratio is output, realizing the reduction processing of the test cycle.

[0059] D4, the test cycle of the automatic test of the performance of the mobile phone application based on artificial intelligence is amplified by the test cycle amplification ratio; the amplification processing means that the test cycle amplification ratio is multiplied by the test cycle.

[0060] In this embodiment, when the to-be-reduced difference amount does not exceed the reduction amplitude limit, the equal amount reduction can be directly performed in the next test cycle, ensuring the rapid adaptability of the sampling frequency and avoiding resource waste caused by oversampling; and when the to-be-reduced difference amount exceeds the reduction amplitude limit, the limit reduction and the remaining difference value are retained, avoiding the problem that the performance fluctuation is not fully captured due to the one-time reduction of the sampling frequency, while ensuring the gradualness and stability of the sampling adjustment; then through the pre-set difference-test cycle amplification ratio mapping table, the undigested reduction difference is converted into the extension of the test cycle, realizing the double dynamic optimization of the test cycle and the sampling frequency, reducing the sampling overhead while maintaining the integrity of the data collection; by extending the test cycle, the automatic performance test based on artificial intelligence can still grasp the performance trend under the lower sampling frequency, thereby improving the resource utilization rate of the test process, and further improving the sampling accuracy of the test data.

[0061] As Figure 3 shown, is the flow chart of monitoring the data noise condition of test data provided by the embodiment of the application, and the specific logic is: monitoring the data noise condition of test data and obtaining the corresponding data noise evaluation parameter, comparing the data noise evaluation parameter with the extracted data noise judgment parameter, if the variance is greater than the maximum variance limit value, recording the sampling frequency secondary adjustment score as the effective adjustment value, otherwise recording the sampling frequency secondary adjustment score as the invalid adjustment value; if the coefficient of variation is greater than the maximum coefficient of variation limit value, recording the sampling frequency secondary adjustment score as the effective adjustment value, otherwise recording the sampling frequency secondary adjustment score as the invalid adjustment value; if the signal-to-noise ratio is less than the maximum signal-to-noise ratio limit value, recording the sampling frequency secondary adjustment score as the effective adjustment value, otherwise recording the sampling frequency secondary adjustment score as the invalid adjustment value; and the sampling frequency secondary adjustment score cumulative value is counted for data noise degree judgment; through the above process, not only the data noise condition after optimization adjustment is more accurately quantified, but also the system is more helpful for the automatic decision of secondary adjustment.

[0062] Further, the specific process of monitoring the data noise condition of test data is as follows: First, monitoring the data noise condition of test data and obtaining the corresponding data noise evaluation parameter, the data noise evaluation parameter including variance, coefficient of variation and signal-to-noise ratio.

[0063] Specifically, the mean value of a sampling data (such as 60 FPS samples in 1s) is calculated, and the corresponding variance is obtained by calculating the sum of squared deviations, and in the AI system, the sliding window variance is generally used, and on the basis of variance calculation, the corresponding coefficient of variation is obtained by dividing the standard deviation by the mean value, and finally the mean value of the collection index is taken as the signal strength, the standard deviation is taken as the noise strength, and the signal strength and the noise strength are operated by ratio to obtain the corresponding signal-to-noise ratio.

[0064] Second, comparing the data noise evaluation parameter with the extracted data noise judgment parameter, the data noise judgment parameter including the maximum variance limit value, the maximum coefficient of variation limit value and the minimum signal-to-noise ratio limit value.

[0065] It should be noted that the data noise judgment parameter is extracted from the preset database, and is usually pre-set by professional test technicians according to experience rules.

[0066] Third, if the variance is greater than the maximum variance limit value, the sampling frequency secondary adjustment score is recorded as the effective adjustment value, otherwise the sampling frequency secondary adjustment score is recorded as the invalid adjustment value; wherein the effective adjustment value is usually 1, and the invalid adjustment value is usually 0.

[0067] It should be noted that the greater the variance, the greater the data noise fluctuation may be, when the variance is greater than the variance limit value, it indicates that the sampling frequency may need to be adjusted, and the corresponding sampling frequency secondary adjustment fraction is the effective adjustment value, and the variance is not greater than the maximum variance limit value, which indicates that it may not need to be adjusted, and the corresponding sampling frequency secondary adjustment fraction is the invalid adjustment value.

[0068] In the fourth step, if the coefficient of variation is greater than the maximum coefficient of variation limit value, the sampling frequency secondary adjustment fraction is recorded as an effective adjustment value, otherwise the sampling frequency secondary adjustment fraction is recorded as an invalid adjustment value. In the fifth step, if the signal-to-noise ratio is less than the maximum signal-to-noise ratio limit value, the sampling frequency secondary adjustment fraction is recorded as an effective adjustment value, otherwise the sampling frequency secondary adjustment fraction is recorded as an invalid adjustment value. In the sixth step, the sampling frequency secondary adjustment fraction cumulative value is counted for data noise degree determination.

[0069] In this embodiment, by introducing three types of parameters of variance, coefficient of variation and signal-to-noise ratio at the same time, the sampling data noise level can be comprehensively evaluated from different angles, avoiding the deviation caused by single index determination, thereby improving the comprehensiveness and reliability of noise determination; By comparing the evaluation parameters with the maximum / minimum limit value, quantitative noise determination can be realized, which improves the scientificity and objectivity of the determination process; And the effective / invalid marking method of the secondary adjustment fraction can clearly quantify the influence of different noise determination results on the sampling frequency adjustment, ensuring the operability and traceability of the adjustment determination; Through the accumulation of multiple index results, multi-factor comprehensive determination can be realized, which helps to reduce the risk of misjudgment caused by single index abnormality, thereby improving the stability and accuracy of data noise degree determination.

[0070] Further, the specific process of data noise degree determination is as follows: If the sampling frequency secondary adjustment fraction cumulative value is greater than the pre-set adjustment fraction threshold value, the initial test sampling frequency is adjusted again, the secondary adjustment ratio is obtained based on the current sampling frequency secondary adjustment fraction cumulative value, the initial test sampling frequency is adjusted again through the secondary adjustment ratio, and the data noise of the feedback current test period is determined.

[0071] The adjustment score threshold is preset by a professional test technician, and the professional test technician generally sets it according to experience rules and standard requirements of the sampling frequency. In addition, the secondary adjustment ratio is matched and obtained from a preset secondary adjustment mapping table, the sampling frequency secondary adjustment score cumulative value is input into the secondary adjustment mapping table, and the corresponding secondary adjustment ratio is output. The sampling is performed according to the product of the secondary adjustment ratio and the initial test sampling frequency. The secondary adjustment mapping table is a data table used to fit the mapping relationship between the sampling frequency secondary adjustment score cumulative value and the secondary adjustment ratio, and is obtained after the secondary adjustment mapping table is trained by secondary adjustment training data. The secondary adjustment training data includes the sampling frequency secondary adjustment score cumulative value obtained in a historical time period and the secondary adjustment ratio set by the professional test technician based on experience rules.

[0072] If the sampling frequency secondary adjustment score cumulative value is not greater than the preset adjustment score threshold, no secondary adjustment is performed, the automatic test process of the mobile phone application performance in the next test period is continued to be monitored, and the data noise in the current test period is fed back as qualified.

[0073] In the embodiment, by comparing the sampling frequency secondary adjustment score cumulative value with the preset threshold, the quantization threshold control of the data noise can be realized, unnecessary frequent adjustment can be avoided, and the stability of the system operation can be ensured. When the noise degree exceeds the threshold, the secondary adjustment ratio obtained by mapping the score cumulative value is used to adjust the sampling frequency, so that the adjustment amplitude is proportional to the noise severity, the targeted and adaptive optimization is realized, and the controllability and traceability of the test process are improved. Even if the secondary adjustment is not triggered, the subsequent test period is also monitored to realize dynamic and continuous monitoring of the test process, and the sampling frequency can be kept matched with the test scene for a long time.

[0074] As shown in Figure 4 , the flowchart of the mobile phone application performance automatic test method based on artificial intelligence provided by the embodiment of the present application is shown in Figure 4 , and the specific steps are as follows: In the current test period, the mobile phone application performance is automatically tested based on an artificial intelligence algorithm, and the sampling of the corresponding test data is monitored in real time to determine the sampling frequency adaptation.

[0075] The specific result of the sampling frequency adaptation determination is used to determine whether to perform the automatic sampling adjustment for improving the sampling adaptation.

[0076] If the automatic sampling adjustment is performed, the data noise of the test data after the automatic sampling adjustment is monitored, and corresponding test feedback is performed after the data noise degree is determined, and whether to perform the sampling secondary adjustment is determined, otherwise the data sampling of the automatic test of the mobile phone application performance is continuously monitored.

[0077] The above-described embodiments can be implemented in whole or in part by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the flow or function according to the embodiments of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through a wired (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0078] It should be understood that the term "and / or" herein merely describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone, wherein A and B can be singular or plural. In addition, the character " / " herein generally represents that the associated objects before and after it are in an "or" relationship, but it can also represent an "and / or" relationship, which can be understood according to the context before and after it.

[0079] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of these items, including any combination of single item or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, and c can be single or multiple.

[0080] It should be understood that the size of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0081] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the devices, apparatuses and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0083] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0084] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0085] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0086] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0087] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An artificial intelligence-based automatic test system for performance of a mobile phone application, characterized by, The application comprises a sampling condition monitoring module, an automatic sampling adjustment module, and a test feedback module. The sampling condition monitoring module is used to automatically test the performance of a mobile phone application based on an artificial intelligence algorithm during a current test period, and to monitor the sampling condition of the corresponding test data in real time to make a sampling frequency adjustment determination to determine the degree of fit between the sampling frequency and the current test condition. The automatic sampling adjustment module is used to determine whether to perform automatic sampling adjustment to improve sampling adjustment based on the result of the sampling frequency adjustment determination. The test feedback module is used to monitor the data noise condition of the test data after the automatic sampling adjustment if the automatic sampling adjustment is performed, and to make corresponding test feedback after making a data noise degree determination, while deciding whether to perform secondary sampling adjustment, otherwise, continue to monitor the data sampling condition of the automatic test of the performance of the mobile phone application. The specific process of making the sampling frequency adjustment determination is as follows: 2.The AI-based mobile application performance automation testing system of claim 1, wherein Based on each initial test sampling frequency, each test data is collected, and test sampling frequency applicable analysis parameters of the current test period are obtained, including CPU occupancy, memory occupancy, power consumption, and FPS. According to the test sampling frequency applicable analysis parameters, a sampling frequency analysis factor is obtained to quantify the demand condition of the test data of the current test period for the sampling frequency, and based on the sampling frequency analysis factor, an optimized sampling frequency interval is output from a pre-constructed sampling frequency projection sequence used to fit the mapping relationship between the sampling frequency analysis factor and the optimized sampling frequency interval. The initial test sampling frequency is matched with the corresponding optimized sampling frequency interval: If the initial test sampling frequency belongs to the corresponding optimized sampling frequency interval, the sampling precision is optimized to improve the sampling precision of the test data, otherwise, the adaptive automatic sampling adjustment is performed. The specific acquisition method of the sampling frequency analysis factor is as follows: 3.The AI-based mobile application performance automation testing system of claim 2, wherein The test sampling frequency applicable analysis parameters are subjected to data normalization processing, and pre-stored test sampling frequency applicable analysis proportions are extracted, including CPU occupancy proportion, memory occupancy proportion, power consumption proportion, and FPS proportion. After weighting operation of the test sampling frequency applicable analysis parameters based on the test sampling frequency applicable analysis proportions, the sampling frequency analysis factor is coupled. The specific content of the sampling precision optimization is as follows: 4.The AI-based mobile application performance automation testing system of claim 2, wherein The median value of the optimized sampling frequency interval is obtained and denoted as an optimized sampling median value, and the optimized sampling median value is subjected to difference quantization with the initial test sampling frequency to obtain an optimized-initial sampling gap value. The optimized-initial sampling gap value is compared with a set sampling median value error range, if the optimized-initial sampling gap value belongs to the sampling median value error range, the sampling precision optimization is not performed, otherwise, the distance quantization value of the optimized-initial sampling gap value is input into a pre-fitted sampling frequency fitting function to output a corresponding optimal sampling frequency adjustment amplitude. If the optimized-initial sampling gap value is greater than the optimized direction critical value, the initial test sampling frequency is adjusted based on the optimal sampling frequency adjustment amplitude. ​ 5. If the optimized-initial sampling gap value is less than the optimization direction threshold value, the initial test sampling frequency is adjusted by a decrease adjustment based on the optimal sampling frequency adjustment amplitude. 6.The AI-based mobile application performance automation testing system of claim 2, wherein The specific process of the adaptive sampling adjustment of the execution adaptation is as follows: If the initial test sampling frequency is lower than the minimum value of the optimization sampling frequency interval, the to-be-increased gap amount between the initial test sampling frequency and the minimum value of the optimization sampling frequency interval is obtained, and the corresponding increase amplitude limit amount for limiting the sampling frequency increase amplitude is automatically increased for sampling adjustment; If the initial test sampling frequency is higher than the maximum value of the optimization sampling frequency interval, the to-be-decreased gap amount between the initial test sampling frequency and the maximum value of the optimization sampling frequency interval is obtained, and the corresponding decrease amplitude limit amount for limiting the sampling frequency decrease amplitude is automatically decreased for sampling adjustment. 7.The AI-based mobile application performance automation testing system of claim 5, wherein The specific steps of the automatic sampling adjustment are as follows: If the to-be-increased gap amount is not greater than the increase amplitude limit amount, the initial test sampling frequency is increased by the to-be-increased gap amount in the next test period; If the to-be-increased gap amount is greater than the increase amplitude limit amount, the initial test sampling frequency is increased by the increase amplitude limit amount in the next test period, and the difference amount between the to-be-increased gap amount and the increase amplitude limit amount is obtained to obtain an increase difference value; The increase difference value is input into a test period mapping table established with the increase difference value and a test period reduction ratio, to output a corresponding test period reduction ratio; The test period is reduced by the test period reduction ratio. 8.The AI-based mobile application performance automation testing system of claim 5, wherein, The specific steps of the automatic sampling adjustment are as follows: If the to-be-increased gap amount is not greater than the increase amplitude limit amount, the initial test sampling frequency is increased by the to-be-increased gap amount in the next test period; If the to-be-increased gap amount is greater than the increase amplitude limit amount, the initial test sampling frequency is increased by the increase amplitude limit amount in the next test period, and the difference amount between the to-be-increased gap amount and the increase amplitude limit amount is obtained to obtain an increase difference value; The increase difference value is input into a test period mapping table established with the increase difference value and a test period reduction ratio, to output a corresponding test period reduction ratio; The test period is reduced by the test period reduction ratio. 9.The AI-based mobile application performance automation testing system of claim 1, wherein The specific process of monitoring the data noise condition of the test data is as follows: The data noise condition of the test data is monitored, and a corresponding data noise evaluation parameter is obtained, the data noise evaluation parameter including a variance, a coefficient of variation, and a signal-to-noise ratio; The data noise evaluation parameter is compared with an extracted data noise judgment parameter, the data noise judgment parameter including a variance maximum limit value, a coefficient of variation maximum limit value, and a signal-to-noise ratio minimum limit value; If the variance is greater than the variance maximum limit value, the sampling frequency secondary adjustment score is recorded as an effective adjustment value, otherwise the sampling frequency secondary adjustment score is recorded as an invalid adjustment value; If the coefficient of variation is greater than the coefficient of variation maximum limit value, the sampling frequency secondary adjustment score is recorded as an effective adjustment value, otherwise the sampling frequency secondary adjustment score is recorded as an invalid adjustment value; If the signal-to-noise ratio is less than the maximum signal-to-noise ratio limit, the sampling frequency secondary adjustment fraction is recorded as an effective adjustment value, otherwise the sampling frequency secondary adjustment fraction is recorded as an invalid adjustment value; The sampling frequency secondary adjustment fraction cumulative value is counted to determine the degree of data noise. 10.The AI-based mobile application performance automation testing system of claim 8, wherein, The specific process of determining the degree of data noise is as follows: If the sampling frequency secondary adjustment fraction cumulative value is greater than the preset adjustment fraction threshold, the initial test sampling frequency is adjusted again, the secondary adjustment ratio is obtained based on the current sampling frequency secondary adjustment fraction cumulative value, the initial test sampling frequency is adjusted again through the secondary adjustment ratio, and the data noise of the current test period is fed back; If the sampling frequency secondary adjustment fraction cumulative value is not greater than the preset adjustment fraction threshold, no secondary adjustment is performed, and the next test period of the automatic test process of the mobile application performance is continued to be monitored, and the data noise of the current test period is fed back.

11. A method for automated testing of performance of a mobile phone application based on artificial intelligence, characterized in that, The specific steps are as follows: In the current test period, the performance of the mobile application is automatically tested based on the artificial intelligence algorithm, and the sampling of the corresponding test data is monitored in real time to determine the sampling frequency adaptation to determine the degree of fit between the sampling frequency and the current test situation; The result of the sampling frequency adaptation determination is used to determine whether to perform the automatic sampling adjustment for improving the sampling adaptation; If the automatic sampling adjustment is performed, the data noise of the test data is monitored after the automatic sampling adjustment, and the corresponding test feedback is performed after the degree of data noise is determined, and it is determined whether to perform the sampling secondary adjustment, otherwise the data sampling situation of the automatic test of the mobile application performance is continued to be monitored.

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