CN108446224B - Performance analysis method of application program on mobile terminal and storage medium - Google Patents
Performance analysis method of application program on mobile terminal and storage medium Download PDFInfo
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Abstract
The invention provides a performance analysis method and a storage medium of an application program on a mobile terminal, wherein the method comprises the following steps: presetting a class and a method for embedding points; after the system is started, automatically traversing the source code of the existing application, and acquiring the corresponding target class and method according to the preset class and method to perform code injection; and after the code injection is finished, starting a performance detection service process to detect the class and the method of the target. The invention can realize the automatic and comprehensive monitoring of the running state of the target program and the performance analysis; the efficiency of statistical analysis is high, and the accuracy of analysis can be ensured; furthermore, an analysis report can be generated, and errors generated by the existing tracking system performance tool can be greatly reduced.
Description
Technical Field
The invention relates to the field of terminal application management, in particular to a performance analysis method and a storage medium of an application program on a mobile terminal.
Background
At present, many performance analysis tools, such as the common leak company, are available on the mobile side for detecting memory leaks. The best performance tool is used throughout the mobile-end market in the hotlining GT-HOME framework that integrates the lakkarary powerful memory leak analysis library, and uses the wireshark library for packet capture, among others.
In addition, there is ddmlib library provided by *** official, which can perform remote interactive operation with PC and some performance analysis, and upload the generated performance analysis log to the automatic test platform and display it on the automatic test platform.
However, the tools all have a common point that the tools all need to generate a Trace file by using an android system for analysis. The Trace file of the android system is a log file generated through a system embedded point, and comprises starting time, performance records of HTTP communication and the like. Then, if a host needs to bring up a stack of components and the underlying classes and methods of some of the host's original android systems are proxied, it is difficult to ensure the accuracy of the data reports generated after performance monitoring by these existing automated integrated analysis tools. Moreover, in the recent situation of automatic tool operation, many data generated after performance analysis are wrong, so that developers can be lost in performance analysis.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method and the storage medium for analyzing the performance of the application program on the mobile terminal are provided, the embedded point of the system for monitoring the performance by automatically traversing the target application in the running period can be realized in an automatic injection mode, and the monitoring accuracy is improved.
In order to solve the technical problems, the invention adopts the technical scheme that:
a performance analysis method for an application program on a mobile terminal comprises the following steps:
presetting a class and a method for embedding points;
after the system is started, automatically traversing the source code of the existing application, and acquiring the corresponding target class and method according to the preset class and method to perform code injection;
and after the code injection is finished, starting a performance detection service process to detect the class and the method of the target.
The invention provides another technical scheme as follows:
a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements all the steps included in the method for analyzing the performance of an application program on a mobile terminal.
The invention has the beneficial effects that: according to the method, the characteristics of the bottom layer of the Android system are fully utilized, the bottom source code is automatically traversed during the running period of the system, and a performance monitoring point trace is injected into a target class and a method; the resident performance detection service is utilized to realize the analysis and monitoring of the running condition and the performance of the target application program in an all-around and real-time manner in the system running process based on the trace of the monitoring point, so that the error of a performance tool of a manual tracking system is greatly reduced, and the accuracy of the performance analysis of the application program on the mobile terminal is obviously improved.
Drawings
FIG. 1 is a flowchart illustrating a method for analyzing performance of an application on a mobile terminal according to the present invention;
fig. 2 is a flowchart illustrating a method for analyzing performance of an application on a mobile terminal according to an embodiment of the present invention.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
The most key concept of the invention is as follows: automatically traversing the bottom source code during the operation of the system, and injecting a performance monitoring point trace into the target class and the method; and analyzing and monitoring the running state and the performance of the target application program in an all-around and real-time manner in the running process of the system by using a resident performance detection service based on the trace of the monitoring point.
The technical terms related to the invention are explained as follows:
referring to fig. 1, the present invention provides a method for analyzing performance of an application program on a mobile terminal, including:
presetting a class and a method for embedding points;
after the system is started, automatically traversing the source code of the existing application, and acquiring the corresponding target class and method according to the preset class and method to perform code injection;
and after the code injection is finished, starting a performance detection service process to detect the class and the method of the target.
From the above description, the beneficial effects of the present invention are: the invention fully utilizes the characteristics of the bottom layer of the system, realizes the analysis of the running state and the performance of the monitoring target program in all aspects in the running process by reading the binary system of the bottom layer and injecting the performance monitoring point-Trace into the binary system during the running period of the system, thereby generating a performance analysis report and greatly reducing the error generated by a tool for artificially tracking the performance of the system. Wherein the characteristics of the system bottom layer refer to: after each application is installed, the system allocates an independent storage space for storing byte code files, resource files, configuration files and the like. When the application program is in the initial running stage, the content in the byte code is automatically read and put into the class loader. And the classes in the class loader are all replaceable, so the implementation mode of the scheme is to implant preset code into a method where a target object in the class loader is located by using a JNI (calling the art library and the dalik library of android) mode during running.
Further, the code injection is realized by executing a preset script;
the preset script comprises the preset class and method and the corresponding analysis type to be injected;
the analysis types comprise memory consumption, electric quantity consumption, application program starting, package analysis and the self-starting times of the system.
According to the description, the automatic injection behavior is realized through the execution of the script, so that the automatic tracking is realized, and the error caused by artificial tracking is avoided.
Further, the code injection includes:
the system automatically generates a corresponding monitoring point trace corresponding to each target class and method in execution, and sends the monitoring point trace to a performance detection service process.
As can be seen from the above description, the association of the target class and method with the performance detection service process is achieved.
Further, the starting performance detection service process detects the class and the method of the target, specifically:
after a performance detection service process is started, the performance detection service process carries out calculation according to the received class of a target in current system execution and a monitoring point trace corresponding to a method;
and the performance detection service process carries out statistics and analysis according to the calculation result to generate a performance index report.
From the above description, the performance detection service can realize the analysis of the running state and performance of the target program in all directions, and meanwhile, the accuracy of the analysis result can be ensured.
Further, the starting performance detection service process detects the class and the method of the target, specifically:
the performance detection service process maintains a trace queue to store the received trace of the monitoring point;
acquiring a trace of a monitoring point from the trace queue according to a preset time interval;
grouping and classifying the obtained monitoring point trace according to the class of the target corresponding to the monitoring point trace and the analysis type injected by the method, and simultaneously combining the monitoring point trace in the group to obtain a monitoring array;
and carrying out statistics and analysis according to the monitoring array to generate a performance index report.
Further, the starting performance detection service process carries out grouping and classification according to the analysis type, the method name, the object name and the parent method name corresponding to the monitoring point trace.
According to the description, the monitoring points of the same type are classified, and the analysis efficiency is effectively improved.
Further, before performing statistics and analysis according to the monitoring array, the method further includes:
comparing each monitoring point trace in the monitoring array according to the corresponding analysis type, method name, object name and parent method name, and judging whether the same monitoring point trace has different trace states;
if yes, carrying out time consumption statistics on the same monitoring point trace, and generating a new monitoring point trace to replace the same monitoring point trace;
and acquiring a new monitoring array.
It can be known from the above description that, because each monitoring point trace has a score of the states of the start trace and the end trace, and nesting between trace nodes occurs, through comparison statistics, different trace states of the same monitoring point trace can be integrated, thereby improving the accuracy of the statistical result again.
The invention provides another technical scheme as follows:
a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements all the steps included in the method for analyzing the performance of an application program on a mobile terminal.
As can be understood from the above description, those skilled in the art can understand that all or part of the processes in the above technical solutions can be implemented by instructing related hardware through a computer program, where the program can be stored in a computer-readable storage medium, and when executed, the program can include the processes of the above methods.
Example one
The embodiment provides a performance analysis method for an application program on a mobile terminal, which can monitor the existing application componentization of the mobile terminal (such as an intelligent mobile terminal like a mobile phone and a tablet), detect operations such as memory consumption, CPU consumption, electric quantity and application program time consumption of a plug-in from a system, and generate a performance index report from counted data.
Specifically, referring to fig. 2, the method of the present embodiment may include the following steps:
s1: writing preset script
The embodiment adopts an automatic code injection mode to realize the performance monitoring of the program. Specifically, a script can be written in advance, a class and a method which need to be subjected to point burying are configured in the script, and automatic code injection of the class and the method of the target is realized through the execution of the script. That is, the script is used to inject preset classes and methods for embedding points during system startup.
The preset script comprises a target method name and a class name of an injection source code and a type needing to be injected; the type refers to an analysis type, and the content includes: memory consumption, power consumption, APP startup, packet parsing, and the like, as well as the number of times of system self-startup.
S2: and after the system is started to run, running the preset script, automatically traversing the binary source code of the existing application program according to the script, and finding out the class and the method of the target pre-configured in the script to inject the code.
The code injection refers to a preset section of code which can be inserted into a program, namely injection monitoring points for monitoring the class and the method of the target. Preferably into the cephalad and caudal portions of the target method.
Specifically, the code injection includes a class and a method corresponding to each target, automatically generates a monitoring point trace, and sends the generated monitoring point trace to the performance detection service process for real-time monitoring.
S3: and after the code injection is finished, starting a performance detection service process to detect the class and the method of the target.
Preferably, when the target class and the method are injected successfully, the preset script sends an initialized instruction to the system; after the system receives the initialization completion instruction sent by the script,
preferably, it will also detect whether the current system process is in the main process, if it is, then start the performance detection service process PerformanceStaticeMonitor. The purpose of this step is to obtain performance analysis data after all monitoring points are counted. .
The performance detection service process is monitored in a code embedding mode. The application performance is generally obtained by receiving monitoring information fed back by monitoring points injected into target methods and classes in the current system and then analyzing the monitoring information.
Specifically, after the performance detection service process is started, the execution includes:
s31: and maintaining a trace queue for storing the received trace of the monitoring point.
S32: the performance detection service process is responsible for adding the received monitoring point trace into the trace queue for executing performance statistics according to the follow-up timing.
Specifically, after receiving the trace of the monitoring point, the performance detection service process starts to read the memory, the electric quantity and the CPU consumed by the target application program process and updates the memory, the electric quantity and the CPU into the trace of the monitoring point, and places the trace of the monitoring point into a trace queue.
S33: and the performance detection service process acquires the trace of the monitoring point from the trace queue according to a preset time interval (corresponding to an analysis period).
S34: grouping and classifying the acquired trace of the monitoring point according to a target class corresponding to the trace of the monitoring point and an analysis type (configured in a script in advance) injected by the method during code injection; then, the trace points in each group are merged to obtain a TraceCL object, namely at least one monitoring array.
Specifically, the performance detection service process takes out monitoring point trace from the trace queue regularly according to a time interval preset by a system, and classifies, namely groups, the monitoring point trace according to the performance type, the method name, the object name and the parent method name in the monitoring point trace; and then, combining the trace points of the same group (the same type) into a TraceCL object for subsequent statistical analysis. Wherein, the parent method name refers to the method name of the last monitoring point trace; the merging processing mode is to create a TraceCL object, and the object contains the start node attribute of the trace and the end node attribute of the trace.
Preferably, each monitoring point trace has a score of the states of starting and ending traces, and nesting between trace nodes occurs, so that in the execution process (after the step S34), the performance detection service process compares whether there are different trace states of the same trace point according to the performance type, method name, object name and parent method name comparison in the trace point, and if yes, the two trace points are subjected to time-consuming statistics, and a new trace cl object is generated after the statistics. The TraceCL object includes a performance type, a method name, an object name, a parent method name, memory consumption, CPU consumption, power consumption and the like before the method is executed, and memory consumption, CPU consumption, power consumption and the like after the method is executed.
S35: the performance detection service process places the TraceCL objects into the Trace queue to be counted (which is different from the queue in S31 and is a new queue maintained in addition), sorts the objects, classifies and analyzes the objects again, and generates a performance index report according to the results of the counting and analyzing.
Specifically, the performance detection service process sorts according to the synchronization characteristics of the Trace, screens out the first element in the TraceCL object corresponding to the performance type in each monitoring point Trace, and inputs the first element as a target into the Excel table.
Preferably, since the performance testing service process is monitored in real time, before inserting the Excel table, the performance testing service process will reserve a queue before inserting the piece of Execl last, for detecting whether to cover or insert the piece of Execel table.
When the target program is broken down, the performance detection service process reads the preset self-starting times, detects whether the process of the target program is still running, kills the target process if the process of the target program is still running, then executes the starting target application program, and enters the times that the target program is started;
and the performance detection service process automatically inserts a header into the Excel table according to the target times, wherein the header is 'Test _' + times every time the header is generated.
The performance detection service process automatically inserts the application program name, the performance type and the details into the Excel table according to the performance type of the Trace.
The performance detection service process automatically inserts a header of 'version/equipment information' into the Execel form according to the current system type, and inserts the model and the system version of the current mobile terminal.
The performance detection service process inserts the result into the item corresponding to the corresponding header according to the final result generated by the TraceCL object, and finally forms a complete test report.
The method is different from other existing application performance analysis schemes in that the method makes full use of the characteristics of the bottom layer of the android system, and achieves automatic traversal of target classes and methods during operation of the system to perform embedded points of performance monitoring; and a resident performance monitoring service is used for counting and analyzing the performance in real time, and a performance index report is generated at a client, so that errors generated by a performance tool of a manual tracking system are greatly reduced.
Example two
In a first embodiment, a computer-readable storage medium is provided, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements all the steps included in the method for analyzing performance of an application program on a mobile terminal according to the first embodiment.
It will be understood by those skilled in the art that all or part of the processes in the first embodiment may be implemented by hardware instructions of a computer program, and the computer program may be stored in a computer-readable storage medium, and when executed, may include the processes of the methods described above.
In summary, the performance analysis method and the storage medium for the application program on the mobile terminal provided by the invention can not only realize automatic and comprehensive monitoring of the running state of the target program and performance analysis; the efficiency of statistical analysis is high, and the accuracy of analysis can be ensured; furthermore, an analysis report can be generated, and errors generated by the existing tracking system performance tool can be greatly reduced.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (6)
1. A performance analysis method for an application program on a mobile terminal is characterized by comprising the following steps:
presetting a class and a method for embedding points;
after the system is started, automatically traversing the source code of the existing application, and acquiring the corresponding target class and method according to the preset class and method to perform code injection;
after the code injection is completed, a performance detection service process is started to detect the class and the method of the target, and the method specifically comprises the following steps:
the performance detection service process maintains a trace queue to store the received trace of the monitoring point;
acquiring a trace of a monitoring point from the trace queue according to a preset time interval;
grouping and classifying the obtained monitoring point trace according to the class of the target corresponding to the monitoring point trace and the analysis type injected by the method, and simultaneously combining the monitoring point trace in the group to obtain a monitoring array;
counting and analyzing according to the monitoring array to generate a performance index report;
before statistics and analysis are carried out according to the monitoring array, the method further comprises the following steps:
comparing each monitoring point trace in the monitoring array according to the corresponding analysis type, method name, object name and parent method name, and judging whether the same monitoring point trace has different trace states;
if yes, carrying out time consumption statistics on the same monitoring point trace, and generating a new monitoring point trace to replace the same monitoring point trace;
and acquiring a new monitoring array.
2. The method for analyzing the performance of an application program on a mobile terminal as claimed in claim 1, wherein said code injection is implemented by executing a preset script;
the preset script comprises the preset class and method and the corresponding analysis type to be injected;
the analysis types comprise memory consumption, electric quantity consumption, application program starting, package analysis and the self-starting times of the system.
3. The method of claim 1, wherein the code injection comprises:
the system automatically generates a corresponding monitoring point trace corresponding to each target class and method in execution, and sends the monitoring point trace to a performance detection service process.
4. The method for analyzing the performance of the application program on the mobile terminal according to claim 3, wherein the step of starting the performance detection service process to detect the class and the method of the target specifically comprises:
after a performance detection service process is started, the performance detection service process carries out calculation according to the received class of a target in current system execution and a monitoring point trace corresponding to a method;
and the performance detection service process carries out statistics and analysis according to the calculation result to generate a performance index report.
5. The method according to claim 1, wherein the service process for initiating performance detection performs grouping classification according to the analysis type, method name, object name and parent method name corresponding to the trace of the monitoring point.
6. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements all the steps of the method for analyzing performance of an application on a mobile terminal according to any one of claims 1 to 5.
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| CN110457211B (en) * | 2019-07-23 | 2022-05-06 | 腾讯科技（深圳）有限公司 | Script performance test method, device and equipment and computer storage medium |
| CN114328166A (en) * | 2020-09-30 | 2022-04-12 | 阿里巴巴集团控股有限公司 | AB test algorithm performance information acquisition method and device and storage medium |
| CN112306803A (en) * | 2020-10-29 | 2021-02-02 | 金蝶云科技有限公司 | Performance monitoring method and related equipment |
| CN112631891A (en) * | 2021-01-05 | 2021-04-09 | 网易（杭州）网络有限公司 | Performance analysis method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104375938A (en) * | 2014-11-20 | 2015-02-25 | 工业和信息化部电信研究院 | Dynamic behavior monitoring method and system for Android application program |
| CN104915287A (en) * | 2014-03-11 | 2015-09-16 | 富士施乐实业发展（中国）有限公司 | Method and system for unit testing |
| CN106897607A (en) * | 2015-12-17 | 2017-06-27 | 北京奇虎科技有限公司 | A kind of method for monitoring application program and device |
| CN106897609A (en) * | 2015-12-17 | 2017-06-27 | 北京奇虎科技有限公司 | The method and device that a kind of application program to dynamic load is monitored |
| CN107423203A (en) * | 2017-04-19 | 2017-12-01 | 浙江大学 | Non-intrusion type Hadoop applied performance analysis apparatus and method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070261044A1 (en) * | 2006-05-04 | 2007-11-08 | Jonathan Clark | Chained Hook Function Serving Multiple Versions Of Identically Named Dynamically Loaded Libraries |
| CN102789419B (en) * | 2012-07-20 | 2015-04-15 | 中国人民解放军信息工程大学 | Software fault analysis method based on multi-sample difference comparison |
| CN102831345B (en) * | 2012-07-30 | 2015-01-28 | 西北工业大学 | Injection point extracting method in SQL (Structured Query Language) injection vulnerability detection |
| CN107358106A (en) * | 2017-07-11 | 2017-11-17 | 北京奇虎科技有限公司 | Leak detection method, Hole Detection device and server |
-
2018
- 2018-03-06 CN CN201810182243.XA patent/CN108446224B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104915287A (en) * | 2014-03-11 | 2015-09-16 | 富士施乐实业发展（中国）有限公司 | Method and system for unit testing |
| CN104375938A (en) * | 2014-11-20 | 2015-02-25 | 工业和信息化部电信研究院 | Dynamic behavior monitoring method and system for Android application program |
| CN106897607A (en) * | 2015-12-17 | 2017-06-27 | 北京奇虎科技有限公司 | A kind of method for monitoring application program and device |
| CN106897609A (en) * | 2015-12-17 | 2017-06-27 | 北京奇虎科技有限公司 | The method and device that a kind of application program to dynamic load is monitored |
| CN107423203A (en) * | 2017-04-19 | 2017-12-01 | 浙江大学 | Non-intrusion type Hadoop applied performance analysis apparatus and method |
Non-Patent Citations (2)
| Title |
|---|
| "Android进程so注入Hook java方法";墨鱼菜鸡;《https://www.cnblogs.com/csnd/p/11800653.html》;20161222;第1页 * |
| "一种易部署的Android APP动态行为监控方法";王学强 等;《中国科学院大学学报》;20150930;第32卷(第5期);第690-691页 * |
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