Search

CN-121070747-B - Automatic testing system and method for mobile phone application performance based on artificial intelligence

CN121070747BCN 121070747 BCN121070747 BCN 121070747BCN-121070747-B

Abstract

The invention discloses an automatic testing system and method for mobile phone application performance 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 adjusting module and a test feedback module. According to the invention, automatic test is carried out on the application performance of the mobile phone based on an artificial intelligent algorithm in the current test period, the sampling condition of test data is monitored in real time, then whether automatic sampling adjustment is carried out or not is determined according to the sampling frequency adaptation judging result, if yes, the data noise condition of the test data is monitored after the automatic sampling adjustment so as to carry out test feedback after the data noise degree judgment, and meanwhile, whether sampling secondary adjustment is carried out or not is determined, otherwise, the sampling condition of the data is continuously monitored, the adaptation degree of the testing condition of the test data and the sampling frequency is improved, and the problem that the adaptation degree of the sampling frequency and the corresponding testing condition in the testing process of the application performance of the mobile phone is low in the prior art is solved.

Inventors

  • Lai Hexing

Assignees

  • 大唐盛世(深圳)通信有限公司

Dates

Publication Date
20260508
Application Date
20250905

Claims (8)

  1. 1. The mobile phone application performance automatic test system based on the artificial intelligence is characterized by comprising a sampling condition monitoring module, an automatic sampling adjusting module and a test feedback module; The sampling condition monitoring module is used for automatically testing the application performance of the mobile phone based on an artificial intelligent algorithm in the current test period, and monitoring the sampling condition of corresponding test data in real time so as to carry out sampling frequency adaptation judgment to judge the coincidence degree of the sampling frequency and the current test condition; The specific process of sampling frequency adaptation judgment is that 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, wherein the test sampling frequency applicable analysis parameters comprise CPU occupancy rate, memory occupancy rate, power consumption and FPS; obtaining a sampling frequency analysis factor for quantifying the requirement condition of test data of a current test period on sampling frequency according to a test sampling frequency applicable analysis parameter, outputting a corresponding optimized sampling frequency interval based on a sampling frequency projection sequence which is pre-constructed and used for fitting a mapping relation between the sampling frequency analysis factor and the optimized sampling frequency interval by the sampling frequency analysis factor; The method comprises the specific contents of sampling precision optimization, namely acquiring a median value of an optimized sampling frequency interval, marking the median value as an optimized sampling median value, carrying out difference quantization on the optimized sampling median value and an initial test sampling frequency to obtain an optimized-initial sampling gap value, comparing the optimized-initial sampling gap value with a set sampling median error range, if the optimized-initial sampling gap value belongs to the sampling median error range, not carrying out sampling precision optimization, otherwise, inputting a distance quantization value of the optimized-initial sampling gap value into a pre-fitted sampling frequency fitting function to output a corresponding optimal sampling frequency adjustment amplitude, and if the optimized-initial sampling gap value is larger than an optimized direction critical value, carrying out improvement adjustment on the initial test sampling frequency based on the optimal sampling frequency adjustment amplitude, and if the optimized-initial sampling gap value is smaller than the optimized direction critical value, carrying out reduction adjustment on the initial test sampling frequency based on the optimal sampling frequency adjustment amplitude; the automatic sampling adjustment module is used for determining whether to execute automatic sampling adjustment for improving sampling adaptation degree according to the sampling frequency adaptation judgment result; And the test feedback module is used for monitoring the data noise condition of the test data after the automatic sampling adjustment if the automatic sampling adjustment is executed, carrying out corresponding test feedback after the data noise degree judgment, and simultaneously determining whether the sampling secondary adjustment is carried out or not, otherwise, continuously monitoring the data sampling condition of the automatic test of the application performance of the mobile phone.
  2. 2. The automated testing system for mobile phone application performance based on artificial intelligence according to claim 1, wherein the specific acquisition mode of the sampling frequency analysis factor is as follows: Carrying out data normalization processing on the test sampling frequency applicable analysis parameters, and extracting prestored test applicable analysis specific gravity, wherein the test applicable analysis specific gravity comprises CPU occupancy rate specific gravity, memory occupancy rate specific gravity, power consumption specific gravity and FPS specific gravity; and weighting the test sampling frequency applicable analysis parameters based on the test sampling frequency applicable analysis proportion, and then coupling to obtain a sampling frequency analysis factor.
  3. 3. The automated mobile phone application performance testing system based on artificial intelligence according to claim 1, wherein the specific process of performing the adaptive automated sampling adjustment is as follows: If the initial test sampling frequency is lower than the minimum value of the optimized sampling frequency interval, acquiring a difference to be improved between the initial test sampling frequency and the minimum value of the optimized sampling frequency interval, and carrying out automatic improvement sampling adjustment on the corresponding improvement amplitude limit for limiting the improvement amplitude of the sampling frequency; if the initial test sampling frequency is higher than the maximum value of the optimized sampling frequency interval, acquiring the difference to be reduced between the initial test sampling frequency and the maximum value of the optimized sampling frequency interval, and carrying out automatic reduction sampling adjustment on the corresponding reduction amplitude limit used for limiting the reduction amplitude of the sampling frequency.
  4. 4. The automated mobile phone application performance testing system based on artificial intelligence according to claim 3, wherein the specific steps of the automated sample enhancement adjustment are as follows: if the difference to be improved is not greater than the improvement amplitude limit, the initial test sampling frequency is improved according to the difference to be improved in the next test period; If the difference to be improved is larger than the amplitude-improving limit, the initial test sampling frequency is improved according to the amplitude-improving limit in the next test period, and the difference between the difference to be improved and the amplitude-improving limit is obtained to obtain an improved difference; Inputting the improved difference value into a test period mapping table which is established with the improved difference value and the test period reduction ratio so as to output the corresponding test period reduction ratio; and performing reduction processing on the test period through the test period reduction ratio.
  5. 5. The automated mobile phone application performance testing system based on artificial intelligence according to claim 3, wherein the specific steps of the automated downsampling adjustment are as follows: if the difference to be reduced is not greater than the amplitude reduction limit, reducing the initial test sampling frequency according to the difference to be reduced in the next test period; If the difference to be reduced is larger than the reduction amplitude limit, reducing the initial test sampling frequency according to the reduction amplitude limit in the next test period, and obtaining a difference between the difference to be reduced and the reduction amplitude limit to obtain a reduction difference; Inputting the reduced difference value into a test period mapping table which is established with the reduced difference value and the test period amplification ratio so as to output the corresponding test period amplification ratio; And amplifying the test period based on automatic test of the application performance of the mobile phone by the artificial intelligence according to the test period amplification proportion.
  6. 6. The automated testing system for mobile phone application performance based on artificial intelligence according to claim 1, wherein the specific process of monitoring the data noise condition of the test data is as follows: monitoring the data noise condition of test data and obtaining corresponding data noise evaluation parameters, wherein the data noise evaluation parameters comprise variance, variation coefficient and signal to noise ratio; comparing the data noise evaluation parameter with the extracted data noise judgment parameter, wherein the data noise judgment parameter comprises a variance maximum limit value, a variation coefficient maximum limit value and a signal to noise ratio minimum limit value; If the variance is larger than the maximum variance limit value, recording the secondary adjustment score of the sampling frequency as an effective adjustment value, otherwise recording the secondary adjustment score of the sampling frequency as an ineffective adjustment value; if the variation coefficient is larger than the maximum limit value of the variation coefficient, recording the secondary adjustment fraction of the sampling frequency as an effective adjustment value, otherwise recording the secondary adjustment fraction of the sampling frequency as an ineffective adjustment value; if the signal-to-noise ratio is smaller than the maximum limiting value of the signal-to-noise ratio, recording the secondary adjustment score of the sampling frequency as an effective adjustment value, otherwise recording the secondary adjustment score of the sampling frequency as an ineffective adjustment value; and counting the sampling frequency and secondarily adjusting the score accumulated value to judge the degree of the data noise.
  7. 7. The automated testing system for mobile phone application performance based on artificial intelligence according to claim 6, wherein the specific process of determining the degree of data noise is as follows: if the sampling frequency secondary adjustment score accumulated value is larger than a preset adjustment score threshold value, performing secondary adjustment on the initial test sampling frequency, mapping the current sampling frequency secondary adjustment score accumulated value to obtain a secondary adjustment proportion, performing secondary adjustment on the initial test sampling frequency through the secondary adjustment proportion, and severely feeding back data noise of the current test period; If the accumulated value of the secondary adjustment fraction of the sampling frequency is not greater than the preset adjustment fraction threshold value, secondary adjustment is not performed, an automatic test process of the application performance of the mobile phone in the next test period is continuously monitored, and the data noise of the current test period is fed back to be qualified.
  8. 8. The automatic testing method for the application performance of the mobile phone based on the artificial intelligence is applied to the automatic testing system for the application performance of the mobile phone based on the artificial intelligence as claimed in any one of claims 1 to 7, and is characterized by comprising the following specific steps: Automatically testing the application performance of the mobile phone based on an artificial intelligent algorithm in the current test period, and monitoring the sampling condition of corresponding test data in real time to carry out sampling frequency adaptation judgment so as to judge the coincidence degree of the sampling frequency and the current test condition; determining whether to perform an automated sampling adjustment for improving the sampling adaptation degree by a result of the sampling frequency adaptation determination; If the automatic sampling adjustment is executed, monitoring the data noise condition of the test data after the automatic sampling adjustment, carrying out corresponding test feedback after judging the data noise degree, and simultaneously determining whether to carry out sampling secondary adjustment or not, otherwise, continuously monitoring the data sampling condition of the automatic test of the application performance of the mobile phone.

Description

Automatic testing system and method for mobile phone application performance based on artificial intelligence Technical Field The invention relates to the technical field of electric digital data processing, in particular to an automatic testing system and method for mobile phone application performance based on artificial intelligence. Background The existing artificial intelligent driving mobile phone performance Test system generally comprises a data acquisition layer, a scene driving layer, an AI (ARTIFICIAL INTELLIGENCE, artificial intelligent) analysis and modeling layer and a tuning and feedback layer, wherein the data acquisition layer acquires indexes such as CPU (Central Processing Unit ), memory, GPU (Graphics Processing Unit, graphic processor), power consumption, FPS (FRAMES PER seconds), network delay and the like by using a performance monitoring tool provided by a Hook system interface, a hardware monitoring API and an Android/iOS (iPhone Operating System, apple mobile operating system); the scene driving layer, namely Test case execution, intelligently generates operation steps through an automatic script or simulating user behaviors and combines an AI model (reinforcement learning and behavior prediction model), covers more user use paths, the common framework comprises Appium, UIAutomator (user interface automation Test framework, android official supply) and XCUITest (Xcode UI Test, xcode user interface Test framework, apple official supply), the AI analysis and modeling layer uses an AI algorithm to analyze collected data, and comprises an anomaly detection (LSTM-Long Short-Term Memory network, autoEncoder capture performance fluctuation), convergence analysis (training model evaluation performance optimization trend stability), root positioning (performance bottleneck finding by using a graph model or feature importance analysis) to predict the performance of the application under different hardware environments and different loads, and the tuning and feedback layer automatically generates performance optimization suggestions according to AI analysis results or directly adjusts sampling frequency, analysis and feedback layer, task scheduling strategy, power consumption control parameters, support test to analysis, then to adjustment, then to closed loop optimization of retest. The method, the device, the equipment and the storage medium for testing the application performance are disclosed in China patent publication No. CN115145797A, and comprise the steps of determining a preset number of target processes running in equipment based on running state monitoring information of the equipment where the application to be tested is located, and determining the running occupancy rate of each target process in the equipment to obtain performance test data of the application to be tested. The method, the device, the computer equipment and the storage medium for testing the performance of the multimedia application are disclosed in China patent publication No. CN109710521B, and comprise the steps of obtaining a multimedia application test request, starting a target multimedia application corresponding to the identification of the target multimedia application in the current equipment, obtaining performance parameters of the current equipment and first operation information executed by the target multimedia application in the running process of the target multimedia application, and determining the performance of the target multimedia application in the current equipment according to the performance parameters of the current equipment and the first operation information executed by the target multimedia application The above technology has at least the following technical problems: Because the mobile phone environment is complex, such as system processes, network fluctuation and background tasks, performance indexes (CPU, power consumption and the like) are obviously fluctuated, so that artificial intelligence is mistakenly used as data noise abnormality, meanwhile, a mobile operating system is high in dynamic property, the background processes are uncontrollable, and the mobile operating system is further provided with a resource recovery and scheduling mechanism, so that limitation is generated on data acquisition, short-term fluctuation can be generated if the sampling frequency is higher, the corresponding abnormality is misled and artificial intelligence is amplified, otherwise, key abnormal points can be missed, and therefore, how to enable the sampling frequency in the testing process of mobile phone application performance to be matched with the corresponding testing condition is one of the problems to be solved urgently. Disclosure of Invention In order to solve the technical problem that the sampling frequency and the corresponding test condition in the test process of the mobile phone application performance in the prior art are low in adaptation degree, the embodiment of the inventio