CN110659185A - Mobile APP user experience monitoring method based on real user perception - Google Patents
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Abstract
The invention discloses a mobile APP user experience monitoring method based on real user perception, which is realized by an APP embedded monitoring SDK installed on an Android or IOS operating system; when the APP runs, the monitoring SDK acquires data in real time in the running process of the APP and reports the data according to a certain time interval, and the reported data is sent to a server data processing end for data analysis and processing and is displayed in a platform report; and when the APP is switched to the background or quits, the monitoring SDK stops data acquisition and reporting.
Description
Technical Field
The invention relates to a mobile APP monitoring technology, in particular to a mobile APP user experience monitoring method based on real user perception.
Background
At present, with the popularization of smart phones and the upgrading of mobile network technologies, the importance of mobile end APPs is increasingly appearing. The APP needs to be continuously iteratively upgraded as hardware upgrades, operating system upgrades, and user requirements change. When the APP is upgraded, BUGs such as a card pause flash retreat sometimes occur to the APP due to various reasons, and engineers need to repair the APP according to user feedback. At present, the active feedback of the user is mainly relied on, but the active feedback of the user can objectively bring inconvenience to the user, and in addition, the problem cannot be reflected faithfully and accurately due to the level limit of the user.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a mobile APP user experience monitoring method based on real user perception.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a mobile APP user experience monitoring method based on real user perception is realized by an APP embedded monitoring SDK installed on an Android or IOS operating system; when the APP runs, the monitoring SDK acquires data in real time in the running process of the APP and reports the data according to a certain time interval, and the reported data is sent to a server data processing end for data analysis and processing and is displayed in a platform report; and when the APP is switched to the background or quits, the monitoring SDK stops data acquisition and reporting.
Preferably, the contents displayed by the platform report include response time, response time quantile value, HTTP response time, HTTP response map, HTTP error rate or network error rate, active device number and recent event; the response time is obtained by subdividing the minimum time granularity into 1 hour to display the response time, newly adding a minimum value (min), a maximum value (max) and an average value (avg), and the maximum value is a value after aggregation.
The response time quantile value is obtained by counting the response time quantile value in a certain period of time and knowing the distribution rule of the response time; the HTTP response time is the trend that the time from the request receiving to the response returning of the server changes along with the time; the HTTP response map is a performance map which shows the average HTTP response time of each province in the form of a Chinese map; the HTTP error rate or the network error rate is a historical curve which shows the currently applied HTTP error rate and the network fault rate by using a stacking area diagram; the active equipment number is an active equipment number curve of the current application; the recent event is the event of the recent warning and serious problem which is currently applied.
Preferably, when the monitoring SDK is applied to an Android operating system, the monitoring SDK collects performance data in real time by embedding performance monitoring codes in the app codes through byte code technology at compile time or hook technology at runtime.
Preferably, when the SDK is applied to an IOS (operation operating system), the SDK can be realized by replacing a Method corresponding to a selector during running by using the runtime characteristic of objective-c through a Method Swwizle technology, so that the purpose of hooking the Method is achieved; at the beginning of the starting of the app, the monitoring SDK executes the swwinkle operation on the corresponding method, so that when a function after the swwinkle operation is called, a user-defined function corresponding to the monitoring SDK is called firstly, some data acquisition operations are executed in the function of the monitoring SDK, and then the function of the monitoring SDK is called back to the original function, and the logic of the original application program cannot be influenced.
Preferably, the data collected by the monitoring SDK includes: the APP comprises running time of an APP foreground, an HTTP request target URL, an HTTP status code, response time, byte number of response content, a response header, a system error code of a network failure, a memory stack when an application crashes, a memory stack when an application has an ANR condition, a radio operator code, a current network type, a device name, a manufacturer name, an operating system name and version, geographical location information, an APP name, a bundleId, a version number, APP operation data, such as APP startup times.
Preferably, after Crash occurs in APP, monitoring that the SDK immediately collects Crash stack information, Crash interaction tracks, user context information and user-defined additional information and reports the Crash stack information, the Crash interaction tracks, the user context information and the user-defined additional information to the server, the Crash information can be checked in a report form after two to three minutes, and when data uploading fails, Crash data can be cached until the Crash stack information is uploaded to the server after next initialization.
Preferably, the content displayed by the platform report comprises the related information of iOS card pause, including card pause real-time trend, card pause detail list, card pause detail tracking, crash list, crash data summarization, crash history list, dSYM/Mapping file management and crash tracking; the real-time tendency of the card pause shows the occurrence situation of the iOS card pause within a certain time period, and data such as the number of card pause, influence on users, the card pause ratio and the like are counted; the katon detail list shows all katon records occurring in a time period, including ID, problems, occurrence time, times, influence users, proportion and state information, and can be clicked for deep viewing; the katton details include: tracking the details of the morton: recording information such as the jamming problem, times, state and the like; list table: recording each single sample data, and recording information of UserID, application version, SDK version, system version and equipment information; stuck trajectory: recording an operation track from the moment when the jamming occurs to the moment when the jamming occurs, and knowing a jamming occurrence scene; thread information: capturing thread information when the card pause occurs, and marking the position of the card pause process; network request: capturing network request information when the card pause occurs, recording key information such as URL (uniform resource locator), request parameters, request time, return state and the like, providing custom additional information and context information to help better analyze the card pause scene; and (3) performing Calton statistics: performing statistic analysis on the card from the equipment model and the operating system dimension; the total number of crashes in the period of crash list display, version, equipment and operating system distribution; the crash data summarizing and displaying curve shows the occurrence situation of the App crash in a selected time period, and the crash rate, the crash times and the App starting times of each time point are recorded; counting the total found bugs, the number of collapse occurrences, the number of influencing users and the number of repairing problems; the crash history record list comprises all records of crash occurrence, including App versions, occurrence time periods, number, crash occupation ratio and repair states; BUGS in the list shows: a user-defined tag function is added for a user to mark the specific crash; the dSYM/Mapping file management shows a dSYM/Mapping file of the management application to restore the crash stack information; said crash tracking comprises displaying details of the crash; adding a UserID to help quickly locate problem users; recording a crash track, tracking a crash call stack, and positioning thread information; the function of the breadcrumbs is added, the user can add the breadcrumbs in any initialized code to mark events or actions, and the user can know that the code logic before the user crashes is combined with the crash track to better reproduce the crash scene of the user; the user-defined log function is added, so that research and development can be facilitated and a debugging log can be provided when a crash happens; through a custom interface, acquiring user-defined additional information to help quickly position and solve problems; the context information helps to analyze the occurrence scenario; the statistical chart reveals different dimensional collapse conditions.
Preferably, the content displayed by the platform report includes ANR real-time trends, ANR information collection, an ANR detail list, and ANR detail tracking; the ANR real-time trend shows the trend of the ANR user quantity and the ANR rate changing along with time by a broken line graph; the ANR information is used for summarizing and counting the total ANR number, the number of influencing users and the ANR rate in a selected time; the ANR detail list lists relevant information (ID, crash information, APP version, period, quantity, proportion and state) of all ANR in a selected time, the ANR detail tracking comprises displaying ANR details, adding a UserID to help quickly locate problem users, capturing ANR thread information, system logs, ANR Trace and ANR message to help analyze ANR errors, obtaining user-defined additional information through a user-defined interface to help quickly locate and solve problems, helping analyzing occurrence scenes by context information, and showing ANR conditions with different dimensions by a statistical chart.
The invention has the advantages that: and (3) light weight deployment: and (3) installing the SDK corresponding to the platform (supporting iOS and Android), and acquiring the monitoring data within a few minutes. The performance is almost lossless: only at the necessary positions, the embedding is carried out, and the influence on the application performance is basically negligible. Intelligent data: and carrying out multi-dimensional analysis on the monitoring data such as network, region, equipment, access and the like, and providing an intelligent analysis report. Opening an interface: through the API, developers can customize the display mode of the performance data. Self-defining an interface: and opening an API (application programming interface) interface, enabling a user to define the interface by himself, and monitoring user-defined data. The problem of quick positioning: the specific cause of performance degradation can be quickly located by analyzing the acquired performance data. Code level positioning: and visually positioning the specific error-reporting URL and stack information in the crash. Millisecond alarm: and summarizing the monitoring data in real time, and triggering an email and a short message alarm notification immediately after the monitoring data exceeds an alarm threshold value. The safety is high: two solutions are provided, including that a user can perform data confusion setting and audit uploaded data, and except that the data collection service of the user does not access other network services, only the performance of the system is monitored, and the interactive data and the behavior of the system are not analyzed.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The claimed invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.
Example one
Referring to fig. 1, the method for monitoring user experience of mobile APP based on real user perception in the present embodiment is implemented by an APP embedded monitoring SDK installed on an IOS operating system.
The monitoring SDK can realize the replacement of the Method corresponding to the selector during the operation by using the runtime characteristic of object-c through a Method Swwizle technology, thereby achieving the purpose of hooking the Method; at the beginning of the starting of the app, the monitoring SDK executes the swwinkle operation on the corresponding method, so that when a function after the swwinkle operation is called, a user-defined function corresponding to the monitoring SDK is called firstly, some data acquisition operations are executed in the function of the monitoring SDK, and then the function of the monitoring SDK is called back to the original function, and the logic of the original application program cannot be influenced. When the APP runs, the monitoring SDK acquires data in real time in the running process of the APP and reports the data according to a certain time interval, and the reported data is sent to a server data processing end for data analysis and processing and is displayed in a platform report; and when the APP is switched to the background or quits, the monitoring SDK stops data acquisition and reporting.
After Crash occurs to APP, monitoring that the SDK immediately collects Crash stack information, Crash interaction tracks, user context information and user-defined additional information and reports the Crash stack information, the Crash interaction tracks, the user context information and the user-defined additional information to a server, the Crash information can be checked in a report form after two to three minutes, data uploading failure occurs, and Crash data can be cached until the Crash stack information is uploaded to the server after next initialization.
In this embodiment, the data collected by the monitoring SDK includes: the APP comprises running time of an APP foreground, an HTTP request target URL, an HTTP status code, response time, byte number of response content, a response header, a system error code of a network failure, a memory stack when an application crashes, a memory stack when an application has an ANR condition, a radio operator code, a current network type, a device name, a manufacturer name, an operating system name and version, geographical location information, an APP name, a bundleId, a version number, APP operation data, such as APP startup times.
In this embodiment, the content displayed by the platform report includes response time, a response time quantile value, HTTP response time, an HTTP response map, an HTTP error rate or a network error rate, the number of active devices, and a recent event; the response time is obtained by subdividing the minimum time granularity to be 1 hour for response time display, newly adding a minimum value (min), a maximum value (max) and an average value (avg), and the maximum value is a value after aggregation; the response time quantile value is obtained by counting the response time quantile value in a certain period of time and knowing the distribution rule of the response time; the HTTP response time is the trend that the time from the request receiving to the response returning of the server changes along with the time; the HTTP response map is a performance map which shows the average HTTP response time of each province in the form of a Chinese map; the HTTP error rate or the network error rate is a historical curve which shows the currently applied HTTP error rate and the network fault rate by using a stacking area diagram; the active equipment number is an active equipment number curve of the current application; the recent event is the event of the recent warning and serious problem which is currently applied.
In addition, the contents displayed by the platform report in this embodiment further include the related information of iOS katton, including katton real-time trend, katton detail list, katton detail tracking, crash list, crash data summarization, crash history list, dSYM/Mapping file management, and crash tracking; the real-time tendency of the card pause shows the occurrence situation of the iOS card pause within a certain time period, and data such as the number of card pause, influence on users, the card pause ratio and the like are counted; the katon detail list shows all katon records occurring in a time period, including ID, problems, occurrence time, times, influence users, proportion and state information, and can be clicked for deep viewing; the katton details include: tracking the details of the morton: recording information such as the jamming problem, times, state and the like; list table: recording each single sample data, and recording information of UserID, application version, SDK version, system version and equipment information; stuck trajectory: recording an operation track from the moment when the jamming occurs to the moment when the jamming occurs, and knowing a jamming occurrence scene; thread information: capturing thread information when the card pause occurs, and marking the position of the card pause process; network request: capturing network request information when the card pause occurs, recording key information such as URL (uniform resource locator), request parameters, request time, return state and the like, providing custom additional information and context information to help better analyze the card pause scene; and (3) performing Calton statistics: performing statistic analysis on the card from the equipment model and the operating system dimension; the total number of crashes in the period of crash list display, version, equipment and operating system distribution; the crash data summarizing and displaying curve shows the occurrence situation of the App crash in a selected time period, and the crash rate, the crash times and the App starting times of each time point are recorded; counting the total found bugs, the number of collapse occurrences, the number of influencing users and the number of repairing problems; the crash history record list comprises all records of crash occurrence, including App versions, occurrence time periods, number, crash occupation ratio and repair states; BUGS in the list shows: a user-defined tag function is added for a user to mark the specific crash; the dSYM/Mapping file management shows a dSYM/Mapping file of the management application to restore the crash stack information; said crash tracking comprises displaying details of the crash; adding a UserID to help quickly locate problem users; recording a crash track, tracking a crash call stack, and positioning thread information; the function of the breadcrumbs is added, the user can add the breadcrumbs in any initialized code to mark events or actions, and the user can know that the code logic before the user crashes is combined with the crash track to better reproduce the crash scene of the user; the user-defined log function is added, so that research and development can be facilitated and a debugging log can be provided when a crash happens; through a custom interface, acquiring user-defined additional information to help quickly position and solve problems; the context information helps to analyze the occurrence scenario; the statistical chart reveals different dimensional collapse conditions.
Example two
In the embodiment of the method for monitoring the user experience of the mobile APP based on the real user perception, the method is realized by an APP embedded monitoring SDK installed on an Android operating system. The monitoring SDK implants a performance monitoring code in the app code through a byte code technology during compiling or a hook technology during running so as to collect performance data in real time.
When the APP runs, the monitoring SDK acquires data in real time in the running process of the APP and reports the data according to a certain time interval, and the reported data is sent to a server data processing end for data analysis and processing and is displayed in a platform report; and when the APP is switched to the background or quits, the monitoring SDK stops data acquisition and reporting. In this embodiment, the data collected by the monitoring SDK includes: the APP comprises running time of an APP foreground, an HTTP request target URL, an HTTP status code, response time, byte number of response content, a response header, a system error code of a network failure, a memory stack when an application crashes, a memory stack when an application has an ANR condition, a radio operator code, a current network type, a device name, a manufacturer name, an operating system name and version, geographical location information, an APP name, a bundleId, a version number, APP operation data, such as APP startup times.
After Crash occurs to APP, monitoring that the SDK immediately collects Crash stack information, Crash interaction tracks, user context information and user-defined additional information and reports the Crash stack information, the Crash interaction tracks, the user context information and the user-defined additional information to a server, the Crash information can be checked in a report form after two to three minutes, data uploading failure occurs, and Crash data can be cached until the Crash stack information is uploaded to the server after next initialization.
The contents displayed by the platform report comprise response time, response time quantile values, HTTP response time, an HTTP response map, HTTP error rate or network error rate, active equipment number and recent events; the response time is obtained by subdividing the minimum time granularity to be 1 hour for response time display, newly adding a minimum value (min), a maximum value (max) and an average value (avg), and the maximum value is a value after aggregation; the response time quantile value is obtained by counting the response time quantile value in a certain period of time and knowing the distribution rule of the response time; the HTTP response time is the trend that the time from the request receiving to the response returning of the server changes along with the time; the HTTP response map is a performance map which shows the average HTTP response time of each province in the form of a Chinese map; the HTTP error rate or the network error rate is a historical curve which shows the currently applied HTTP error rate and the network fault rate by using a stacking area diagram; the active equipment number is an active equipment number curve of the current application; the recent event is the event of the recent warning and serious problem which is currently applied.
In this embodiment, the content displayed by the platform report further includes an ANR real-time trend, an ANR information summary, an ANR detail list, and an ANR detail tracking; the ANR real-time trend shows the trend of the ANR user quantity and the ANR rate changing along with time by a broken line graph; the ANR information is used for summarizing and counting the total ANR number, the number of influencing users and the ANR rate in a selected time; the ANR detail list lists relevant information (ID, crash information, APP version, period, quantity, proportion and state) of all ANR in a selected time, the ANR detail tracking comprises displaying ANR details, adding a UserID to help quickly locate problem users, capturing ANR thread information, system logs, ANR Trace and ANR message to help analyze ANR errors, obtaining user-defined additional information through a user-defined interface to help quickly locate and solve problems, helping analyzing occurrence scenes by context information, and showing ANR conditions with different dimensions by a statistical chart.
The above-described embodiments are merely preferred embodiments of the present invention, which is not intended to limit the present invention in any way. Those skilled in the art can make many changes and modifications to the disclosed embodiments, or modify equivalent embodiments to practice the disclosed embodiments, without departing from the scope of the disclosed embodiments. Therefore, equivalent variations made according to the idea of the present invention should be covered within the protection scope of the present invention without departing from the contents of the technical solution of the present invention.
Claims (8)
1. A mobile APP user experience monitoring method based on real user perception is characterized in that: the method is realized by an APP embedded monitoring SDK installed on an Android or IOS operating system; when the APP runs, the monitoring SDK acquires data in real time in the running process of the APP and reports the data according to a certain time interval, and the reported data is sent to a server data processing end for data analysis and processing and is displayed in a platform report; and when the APP is switched to the background or quits, the monitoring SDK stops data acquisition and reporting.
2. The method of claim 1, wherein the method comprises the following steps: the contents displayed by the platform report comprise response time, response time quantile values, HTTP response time, an HTTP response map, HTTP error rate or network error rate, active equipment number and recent events; the response time is obtained by subdividing the minimum time granularity to be 1 hour for response time display, newly adding a minimum value (min), a maximum value (max) and an average value (avg), and the maximum value is a value after aggregation;
the response time quantile value is obtained by counting the response time quantile value in a certain period of time and knowing the distribution rule of the response time; the HTTP response time is the trend that the time from the request receiving to the response returning of the server changes along with the time; the HTTP response map is a performance map which shows the average HTTP response time of each province in the form of a Chinese map; the HTTP error rate or the network error rate is a historical curve which shows the currently applied HTTP error rate and the network fault rate by using a stacking area diagram; the active equipment number is an active equipment number curve of the current application; the recent event is the event of the recent warning and serious problem which is currently applied.
3. The method of claim 1, wherein the method comprises the following steps: when the monitoring SDK is applied to an Android operating system, a performance monitoring code is implanted into an app code through a byte code technology during compiling or a hook technology during running so as to collect performance data in real time.
4. The method of claim 1, wherein the method comprises the following steps: when the SDK is applied to an IOS (operation operating system), the monitoring SDK can replace a Method corresponding to a selector during running by using the runtime characteristic of object-c through a Method Swwizle technology, and the purpose of hooking the Method is achieved; at the beginning of the starting of the app, the monitoring SDK executes the swwinkle operation on the corresponding method, so that when a function after the swwinkle operation is called, a user-defined function corresponding to the monitoring SDK is called firstly, some data acquisition operations are executed in the function of the monitoring SDK, and then the function of the monitoring SDK is called back to the original function, and the logic of the original application program cannot be influenced.
5. The method of claim 1, wherein the method comprises the following steps: the data collected by the monitoring SDK comprises the following data: the APP comprises running time of an APP foreground, an HTTP request target URL, an HTTP status code, response time, byte number of response content, a response header, a system error code of a network failure, a memory stack when an application crashes, a memory stack when an application has an ANR condition, a radio operator code, a current network type, a device name, a manufacturer name, an operating system name and version, geographical location information, an APP name, a bundleId, a version number, APP operation data, such as APP startup times.
6. The method of claim 1, wherein the method comprises the following steps: after Crash occurs to APP, monitoring that the SDK immediately collects Crash stack information, Crash interaction tracks, user context information and user-defined additional information and reports the Crash stack information, the Crash interaction tracks, the user context information and the user-defined additional information to a server, the Crash information can be checked in a report form after two to three minutes, data uploading failure occurs, and Crash data can be cached until the Crash stack information is uploaded to the server after next initialization.
7. The method for mobile APP user experience monitoring based on real user perception according to claim 1 or 4, characterized by: the contents displayed by the platform report comprise related information of iOS card pause, including card pause real-time trend, card pause detail list, card pause detail tracking, crash list, crash data summarization, crash history record list, dSYM/Mapping file management and crash tracking; the real-time tendency of the card pause shows the occurrence situation of the iOS card pause within a certain time period, and data such as the number of card pause, influence on users, the card pause ratio and the like are counted; the katon detail list shows all katon records occurring in a time period, including ID, problems, occurrence time, times, influence users, proportion and state information, and can be clicked for deep viewing; the katton details include: tracking the details of the morton: recording information such as the jamming problem, times, state and the like; list table: recording each single sample data, and recording information of UserID, application version, SDK version, system version and equipment information; stuck trajectory: recording an operation track from the moment when the jamming occurs to the moment when the jamming occurs, and knowing a jamming occurrence scene; thread information: capturing thread information when the card pause occurs, and marking the position of the card pause process; network request: capturing network request information when the card pause occurs, recording key information such as URL (uniform resource locator), request parameters, request time, return state and the like, providing custom additional information and context information to help better analyze the card pause scene; and (3) performing Calton statistics: performing statistic analysis on the card from the equipment model and the operating system dimension; the total number of crashes in the period of crash list display, version, equipment and operating system distribution; the crash data summarizing and displaying curve shows the occurrence situation of the App crash in a selected time period, and the crash rate, the crash times and the App starting times of each time point are recorded; counting the total found bugs, the number of collapse occurrences, the number of influencing users and the number of repairing problems; the crash history record list comprises all records of crash occurrence, including App versions, occurrence time periods, number, crash occupation ratio and repair states; BUGS in the list shows: a user-defined tag function is added for a user to mark the specific crash; the dSYM/Mapping file management shows a dSYM/Mapping file of the management application to restore the crash stack information; said crash tracking comprises displaying details of the crash; adding a UserID to help quickly locate problem users; recording a crash track, tracking a crash call stack, and positioning thread information; the function of the breadcrumbs is added, the user can add the breadcrumbs in any initialized code to mark events or actions, and the user can know that the code logic before the user crashes is combined with the crash track to better reproduce the crash scene of the user; the user-defined log function is added, so that research and development can be facilitated and a debugging log can be provided when a crash happens; through a custom interface, acquiring user-defined additional information to help quickly position and solve problems; the context information helps to analyze the occurrence scenario; the statistical chart reveals different dimensional collapse conditions.
8. The method for mobile APP user experience monitoring based on real user perception according to claim 1 or 5, characterized by: the contents displayed by the platform report comprise ANR real-time trends, ANR information collection, an ANR detail list and ANR detail tracking; the ANR real-time trend shows the trend of the ANR user quantity and the ANR rate changing along with time by a broken line graph; the ANR information is used for summarizing and counting the total ANR number, the number of influencing users and the ANR rate in a selected time; the ANR detail list lists relevant information (ID, crash information, APP version, period, quantity, proportion and state) of all ANR in a selected time, the ANR detail tracking comprises displaying ANR details, adding a UserID to help quickly locate problem users, capturing ANR thread information, system logs, ANR Trace and ANR message to help analyze ANR errors, obtaining user-defined additional information through a user-defined interface to help quickly locate and solve problems, helping analyzing occurrence scenes by context information, and showing ANR conditions with different dimensions by a statistical chart.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111538638A (en) * | 2020-04-28 | 2020-08-14 | 北京思特奇信息技术股份有限公司 | iOS end application program performance monitoring method and device |
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| CN111538638A (en) * | 2020-04-28 | 2020-08-14 | 北京思特奇信息技术股份有限公司 | iOS end application program performance monitoring method and device |
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| CN117076281A (en) * | 2023-10-13 | 2023-11-17 | 晨达(广州)网络科技有限公司 | Software quality assessment method based on deep learning |
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