CN111444110A - Data analysis method and device - Google Patents
Data analysis method and device Download PDFInfo
- Publication number
- CN111444110A CN111444110A CN202010294643.7A CN202010294643A CN111444110A CN 111444110 A CN111444110 A CN 111444110A CN 202010294643 A CN202010294643 A CN 202010294643A CN 111444110 A CN111444110 A CN 111444110A
- Authority
- CN
- China
- Prior art keywords
- optimized
- software program
- test case
- functional module
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 81
- 238000007405 data analysis Methods 0.000 title claims abstract description 22
- 238000012360 testing method Methods 0.000 claims abstract description 157
- 238000012216 screening Methods 0.000 claims abstract description 32
- 238000013507 mapping Methods 0.000 claims description 10
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 4
- 238000005457 optimization Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3604—Software analysis for verifying properties of programs
- G06F11/3612—Software analysis for verifying properties of programs by runtime analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3692—Test management for test results analysis
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention discloses a data analysis method and a device, wherein the method comprises the following steps: receiving a target error code; the target error code is obtained by screening error codes generated in the running process of the software program based on the frequency of the error codes; determining a functional module to be optimized in the software program based on the target error code, wherein the functional module is a module forming the program; acquiring a historical test case of a functional module to be optimized; analyzing the historical test case of the functional module to be optimized to determine the problems of the module to be optimized; and generating a new test case based on the problems of the module to be optimized. Therefore, functions to be optimized in the software program can be automatically identified, and the optimization method can be automatically generated, so that possible problems of the software program can be found in time, the software program can be timely adjusted, and the satisfaction degree of a user on the use of the program at the software position is improved.
Description
Technical Field
The invention relates to the field of testing, in particular to a data analysis method and device.
Background
Before a software program comes online, testing is usually required to perfect functions of each part of the software program. However, the online software program does not mean that the software program is perfect, and the software program needs to be updated continuously as the user demands and the data processing amount are increased continuously.
However, in the prior art, after the software program is online, it is usually determined whether the software program needs to be optimized by performing a questionnaire on a user, which functions of the software program need to be optimized cannot be automatically found, and how to optimize the software program cannot be known. In such a situation, the prior art method for determining whether the software program needs to be optimized is not only inefficient, but also cannot make timely adjustments to the software program, which greatly affects the satisfaction of the user with the use of the software program.
Disclosure of Invention
In view of this, the embodiment of the present invention discloses a data analysis method and apparatus, which achieve the purposes of automatically identifying a function to be optimized of a software program and automatically generating an optimization method.
The embodiment of the invention discloses a data analysis method, which comprises the following steps:
receiving a target error code; the target error code is obtained by screening error codes generated in the running process of the software program based on the frequency of the error codes;
determining a functional module to be optimized in the software program based on the target error code; the functional module is a module for forming the software program;
acquiring a historical test case of the functional module to be optimized;
analyzing the historical test case of the functional module to be optimized to determine the problems of the module to be optimized;
and generating a new test case based on the problems of the module to be optimized.
Optionally, the error codes include error codes generated by the program in the front end during the running process and error codes generated in the log.
Optionally, the method further includes:
receiving a first error code; the first error code is obtained by screening the error codes generated by the software program in the front-end operation process based on the occurrence frequency of the error codes;
receiving a second error code; the second error code is generated when the software program generates a log in the running process, and is obtained by screening the error codes contained in the log based on the occurrence frequency of the error codes;
and screening out target error codes from the first error codes and the second error codes based on the frequency of the first error codes and the frequency of the second error codes.
Optionally, analyzing the historical test case of the functional module to be optimized to determine the problem of the module to be optimized includes:
testing the software program based on the historical test case and obtaining a test result;
if the success rate of the test result is greater than a preset test threshold, the coverage rate of the historical test case of the functional module to be optimized is indicated to be problematic;
and if the success rate of the test result is less than a preset test threshold, indicating that the function of the functional module to be optimized has a problem.
Optionally, the generating a new test case based on the problem of the module to be optimized includes:
if the coverage rate of the historical test case of the functional module to be optimized has a problem, adding a scene case, and generating a new test case based on the added scene case;
and if the function of the functional module to be optimized has a problem, setting a new test case based on the functional part with the problem.
Optionally, the determining a functional module to be optimized in the software program based on the target error code includes:
acquiring a mapping relation between each function module in the preset software program and an error code;
and positioning the functional module to be optimized corresponding to the target error code based on the preset mapping relation between the functional module and the error code in the software program.
The embodiment of the invention discloses a data analysis device, which comprises:
a receiving unit for receiving a target error code; the target error code is obtained by screening error codes generated in the running process of the software program based on the frequency of the error codes;
a function module to be optimized determining unit, configured to determine a function module to be optimized in the software program based on the target error code; the functional module is a module for forming the software program;
the acquisition unit is used for acquiring the historical test case of the functional module to be optimized;
the analysis unit is used for analyzing the historical test case of the functional module to be optimized and determining the problems of the module to be optimized;
and the test case generation unit is used for generating a new test case based on the problems of the module to be optimized.
Optionally, the analysis unit is configured to:
the first obtaining subunit is used for testing the software program based on the historical test case and obtaining a test result;
the first problem determination subunit is used for indicating that the coverage rate of the historical test case of the functional module to be optimized has a problem if the success rate of the test result is greater than a preset test threshold;
and the second problem determination subunit is used for indicating that the function of the functional module to be optimized has a problem if the success rate of the test result is less than a preset test threshold.
Optionally, the test case generating unit includes:
the first test case generation subunit is used for adding a scene case if the coverage rate of the historical test case of the functional module to be optimized has a problem, and generating a new test case based on the added scene case;
and the second test case generation subunit is used for setting a new test case based on the functional part with the problem if the function of the functional module to be optimized has the problem.
The embodiment of the invention also discloses a data analysis system, which comprises:
the error code acquisition end is used for acquiring logs respectively generated in the front end and the logs in the running process of the software program;
and the data processing end is used for executing the data analysis method.
The embodiment of the invention discloses a data analysis method and a data analysis device, wherein the method comprises the following steps: receiving a target error code; the target sequence error code is obtained by screening error codes generated in the running process of the software program based on the frequency of the error codes; determining a functional module to be optimized in the software program based on the target error code, wherein the functional module is a module forming the program; acquiring a historical test case of a functional module to be optimized; analyzing the historical test case of the functional module to be optimized to determine the problems of the module to be optimized; and generating a new test case based on the problems of the module to be optimized. Therefore, functions to be optimized in the software program can be automatically identified, and the optimization method can be automatically generated, so that possible problems of the software program can be found in time, the software program can be timely adjusted, and the satisfaction degree of a user on the use of the program at the software position is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating a data analysis method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a process of obtaining a target error code;
fig. 3 is a schematic flowchart illustrating a method for analyzing a problem existing in a module to be optimized according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a data analysis apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram illustrating a data analysis system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a schematic flow chart of a data analysis method provided in an embodiment of the present invention is shown, where the method includes:
s101: receiving a target error code;
the target error codes are generated in the running process of the software program and are obtained by screening the error codes generated in the running process of the software program based on the occurrence frequency of the error codes;
in this embodiment, a fault may occur in the running process of the software program, or a logic error may occur, in this case, the software program may report an error through an error code, that is, the error code may reflect a problem occurring in the running process of the software program.
The error code may be embodied in two ways:
the method comprises the steps of firstly, generating at the front end of program operation;
for example, when a mobile banking is in use, if an error occurs, an error is reported at a mobile terminal, which is a front end for program operation.
The method II is characterized in that errors are reflected in a log generated in the program running process;
for example, when a mobile phone bank is in operation, an operation log is generated in the server, and the log can represent problems generated by the mobile phone bank in the operation process.
In order to find the problem that needs to be solved urgently, in this embodiment, the error codes are screened based on the frequency generated by the error codes, so as to obtain the target error codes with a high occurrence frequency.
Specifically, referring to fig. 2, the method further includes:
s201: receiving a first error code; the first error code is obtained by screening the error codes generated in the front-end operation process of the software program based on the occurrence frequency of the error codes;
the method for screening the error codes generated by the software program in the front-end operation process based on the error code frequency may include the following two ways:
the method comprises the steps that firstly, from error codes generated in the front-end operation process, the error codes with the error code occurrence frequency before the preset rank are screened out, and first error codes are obtained;
for example, error codes with the frequency of occurrence in the top ten can be screened from error codes generated during the front-end operation of the software program.
And in the second mode, the error codes with the occurrence frequency greater than the preset frequency threshold value are screened out from the error codes generated in the front-end operation process, so that the first error codes are obtained.
S202: receiving a second error code; the second error code is generated when the software program generates a log in the running process, and is obtained by screening the error codes contained in the log based on the occurrence frequency of the error codes;
in this embodiment, the method for screening error codes included in a log generated during the operation of a software program based on the frequency of the error codes may include the following two methods:
in the first mode, from error codes contained in a log obtained when a software program runs, screening out the error codes with the error code occurrence frequency before a preset rank to obtain second error codes;
for example, error codes with the frequency of occurrence in the top ten may be screened out from error codes included in a log obtained by the software program at runtime.
And secondly, screening error codes with the occurrence frequency larger than a preset frequency threshold value from the error codes contained in the log obtained when the software program runs to obtain second error codes.
S203: screening out target error codes from the first error codes and the second error codes based on the frequency of the first error codes and the frequency of the second error codes;
in this embodiment, in order to further screen out the problems that need to be solved, the error codes with higher occurrence frequency are screened out from the first error codes and the second error codes.
For example, the frequencies of the first error codes and the second error codes may be sorted, and the error codes whose frequency of occurrence is before the preset rank may be screened, for example, the error codes whose frequency of occurrence is before the top ten may be screened. Alternatively, error codes with an occurrence frequency greater than a preset second frequency threshold may be screened out.
In this embodiment, by screening the error code, the problem to be solved is urgently screened out.
S102: determining a functional module to be optimized in the software program based on the target error code; the functional module is a module for forming the software program;
in this embodiment, the corresponding relationship between each function in the software program and the error code is pre-stored, and when the error code is determined, the function module that generates the error code may be located, specifically, S102 includes:
acquiring a mapping relation between each function module in the preset software program and an error code;
and positioning the functional module to be optimized corresponding to the target error code based on the preset mapping relation between the functional module and the error code in the software program.
S103: acquiring a historical test case of the functional module to be optimized;
in this embodiment, the software program is generally tested before being online, each functional module of the software program is tested during testing, and each functional module corresponds to a test case for testing, which is called a historical test case for the functional module.
S104: analyzing the historical test case of the functional module to be optimized to determine the problems of the module to be optimized;
in this embodiment, the applicant finds that the possible problems of the module to be optimized include: the functions of the module to be optimized have problems, or the coverage rate of the test case of the module to be optimized has problems in the process of historical test.
Based on this, in this embodiment, which kind of problem the test module to be optimized belongs to may be determined based on analysis of the historical test case, where a specific implementation method will be described in detail below, and details are not described in this embodiment again.
S105: and generating a new test case based on the problems of the module to be optimized.
In this embodiment, in order to optimize the module to be optimized, a new test case may be generated based on the problems of the module to be optimized.
As can be seen from the above description, the problems of the module to be optimized include: the functions of the module to be optimized have problems, or the coverage rate of the test case of the module to be optimized has problems in the process of historical test.
In view of the above-mentioned two problems, the method for generating the test case comprises:
if the coverage rate of the historical test case of the functional module to be optimized has a problem, adding a scene case, and generating a new test case based on the added scene case;
and if the function of the functional module to be optimized has a problem, setting a new test case based on the functional part with the problem.
In this embodiment, a target error code is received; the target sequence error code is obtained by screening error codes generated in the running process of a software program based on the frequency of the error codes; determining a functional module to be optimized in the software program based on the target error code, wherein the functional module is a module forming the program; acquiring a historical test case of a functional module to be optimized; analyzing the historical test case of the functional module to be optimized to determine the problems of the module to be optimized; and generating a new test case based on the problems of the module to be optimized. Therefore, the functions to be optimized in the software program can be automatically identified, and the optimization method can be automatically generated, so that possible problems of the software program can be timely found, the software program can be timely adjusted, and the satisfaction degree of a user on the use of the program at the software position is improved.
Referring to fig. 3, a schematic flowchart of a method for analyzing a problem existing in a module to be optimized according to an embodiment of the present invention is shown, where in this embodiment, the method includes:
s301: testing the software program based on the historical test case and obtaining a test result;
s302: if the success rate of the test result is greater than the preset test threshold, the coverage rate of the historical test case of the functional module to be optimized is indicated to be problematic;
s303: and if the success rate of the test result is less than a preset test threshold, indicating that the function of the functional module to be optimized has a problem.
In this embodiment, generally, the software program is tested by a historical test case before being online, and in order to optimize the software program, the software program is tested based on a test result before the software program is online.
To confirm whether a problem exists with the test case, the test case may be reused in the software program that has been brought online. If the success rate of the test result is greater than the preset test threshold, it indicates that the coverage rate of the historical test case of the functional module to be optimized is problematic, that is, the coverage rate of the historical test case is not comprehensive, and a test scene needs to be added. If the success rate of the test result is smaller than or equal to the preset test threshold, it indicates that the coverage rate of the test case is not a problem, and it indicates that the functional module to be optimized has a problem.
For example, the following steps are carried out: if the success rate of the test result is greater than 90%, it indicates that the coverage rate of the historical test case is in problem, and if the success rate of the test result is less than or equal to 90%, it indicates that the functional module to be optimized is in problem.
By the method, the possible problems of the software program can be found in time, and the software program can be adjusted in time, so that the satisfaction degree of a user on the use of the program at the software position is improved.
Referring to fig. 4, a schematic structural diagram of a data analysis apparatus according to an embodiment of the present invention is shown, in this embodiment, the apparatus includes:
a receiving unit 401, configured to receive a target error code; the target error code is obtained by screening the error code generated during the software operation based on the frequency of the error code;
a to-be-optimized functional module determining unit 402, configured to determine a functional module to be optimized in the software program based on the target error code; the functional module is a module for forming the software program;
an obtaining unit 403, configured to obtain a historical test case of the functional module to be optimized;
an analysis unit 404, configured to analyze a historical test case of the functional module to be optimized, and determine a problem existing in the module to be optimized;
and the test case generating unit 405 is configured to generate a new test case based on a problem existing in the module to be optimized.
Optionally, the error codes include error codes generated by the program in the front end during the running process and error codes generated in the log.
Optionally, the method further includes:
a target error code screening unit for
Receiving a first error code; the first error code is obtained by screening the error codes generated by the software program in the front-end operation process based on the occurrence frequency of the error codes;
receiving a second error code; the second error code is generated when the software program generates a log in the running process, and is obtained by screening the error codes contained in the log based on the occurrence frequency of the error codes;
and screening out target error codes from the first error codes and the second error codes based on the frequency of the first error codes and the frequency of the second error codes.
Optionally, the analysis unit is configured to:
the first obtaining subunit is used for testing the software program based on the historical test case and obtaining a test result;
the first problem determination subunit is used for indicating that the coverage rate of the historical test case of the functional module to be optimized has a problem if the success rate of the test result is greater than a preset test threshold;
and the second problem determination subunit is used for indicating that the function of the functional module to be optimized has a problem if the success rate of the test result is less than a preset test threshold.
Optionally, the test case generating unit includes:
the first test case generation subunit is used for adding a scene case if the coverage rate of the historical test case of the functional module to be optimized has a problem, and generating a new test case based on the added scene case;
and the second test case generation subunit is used for setting a new test case based on the functional part with the problem if the function of the functional module to be optimized has the problem.
Optionally, the function module determining unit to be optimized includes:
the acquiring subunit is used for acquiring a mapping relation between each function module in the preset software program and an error code;
and the positioning subunit is used for positioning the functional module to be optimized corresponding to the target error code based on the preset mapping relation between the functional module and the error code in the software program.
Receiving a target error code by the apparatus of the embodiment; the target error code is obtained by screening error codes generated in the running process of the software program based on the frequency of the error codes; determining a functional module to be optimized in the software program based on the target error code, wherein the functional module is a module forming the program; acquiring a historical test case of a functional module to be optimized; analyzing the historical test case of the functional module to be optimized to determine the problems of the module to be optimized; and generating a new test case based on the problems of the module to be optimized. Therefore, the functions to be optimized in the software program can be automatically identified, and the optimization method can be automatically generated, so that possible problems of the software program can be timely found, the software program can be timely adjusted, and the satisfaction degree of a user on the use of the program at the software position is improved.
Referring to fig. 5, a schematic structural diagram of a data analysis system according to an embodiment of the present invention is shown, where in this embodiment, the system includes:
an error code acquisition terminal 501, configured to acquire logs respectively generated in a front end and a log during running of a software program;
wherein, error code acquisition end includes: a front-end error code acquisition end and a log error code acquisition end; the front-end error code acquisition end can be arranged in the front-end equipment, and the log error code acquisition end can be arranged in a server for storing logs.
The data processing terminal 502 is configured to execute the following data analysis method:
receiving a target error code; the target error codes are generated in the running process of the software program, and the target error codes are obtained by screening the error codes based on the frequency of the error codes generated in the running process of the software program;
determining a functional module to be optimized in the software program based on the target error code; the functional module is a module for forming the software program;
acquiring a historical test case of the functional module to be optimized;
analyzing the historical test case of the functional module to be optimized to determine the problems of the module to be optimized;
and generating a new test case based on the problems of the module to be optimized.
Optionally, the error codes include error codes generated by the program in the front end during the running process and error codes generated in the log.
Optionally, the method further includes:
receiving a first error code; the first error code is obtained by screening the error codes generated by the software program in the front-end operation process based on the occurrence frequency of the error codes;
receiving a second error code; the second error code is generated when the software program generates a log in the running process, and is obtained by screening the error codes contained in the log based on the occurrence frequency of the error codes;
and screening out target error codes from the first error codes and the second error codes based on the frequency of the first error codes and the frequency of the second error codes.
Optionally, analyzing the historical test case of the functional module to be optimized to determine the problem of the module to be optimized includes:
testing the software program based on the historical test case and obtaining a test result;
if the success rate of the test result is greater than a preset test threshold, the coverage rate of the historical test case of the functional module to be optimized is indicated to be problematic;
and if the success rate of the test result is less than a preset test threshold, indicating that the function of the functional module to be optimized has a problem.
Optionally, the generating a new test case based on the problem of the module to be optimized includes:
if the coverage rate of the historical test case of the functional module to be optimized has a problem, adding a scene case, and generating a new test case based on the added scene case;
and if the function of the functional module to be optimized has a problem, setting a new test case based on the functional part with the problem.
Optionally, the determining a functional module to be optimized in the software program based on the target error code includes:
acquiring a mapping relation between each function module in the preset software program and an error code;
and positioning the functional module to be optimized corresponding to the target error code based on the preset mapping relation between the functional module and the error code in the software program.
Based on the system, the problems in the software program can be automatically collected and screened, so that the functions to be optimized in the software program can be automatically identified, the optimization method can be automatically generated, the possible problems of the software program can be timely found, the software program can be timely adjusted, and the satisfaction degree of a user on the use of the software program is improved.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of data analysis, comprising:
receiving a target error code; the target error code is obtained by screening error codes generated in the running process of the software program based on the frequency of the error codes;
determining a functional module to be optimized in the software program based on the target error code; the functional module is a module for forming the software program;
acquiring a historical test case of the functional module to be optimized;
analyzing the historical test case of the functional module to be optimized to determine the problems of the module to be optimized;
and generating a new test case based on the problems of the module to be optimized.
2. The method of claim 1, wherein the error codes comprise error codes generated by a program during execution in a front end and error codes generated in a log.
3. The method of claim 2, further comprising:
receiving a first error code; the first error code is obtained by screening the error codes generated by the software program in the front-end operation process based on the occurrence frequency of the error codes;
receiving a second error code; the second error code is generated when the software program generates a log in the running process, and is obtained by screening the error codes contained in the log based on the occurrence frequency of the error codes;
and screening out target error codes from the first error codes and the second error codes based on the frequency of the first error codes and the frequency of the second error codes.
4. The method of claim 1, wherein analyzing the historical test cases of the functional module to be optimized to determine the problem of the module to be optimized comprises:
testing the software program based on the historical test case and obtaining a test result;
if the success rate of the test result is greater than a preset test threshold, the coverage rate of the historical test case of the functional module to be optimized is indicated to be problematic;
and if the success rate of the test result is less than a preset test threshold, indicating that the function of the functional module to be optimized has a problem.
5. The method of claim 1, wherein generating a new test case based on the problem with the module to be optimized comprises:
if the coverage rate of the historical test case of the functional module to be optimized has a problem, adding a scene case, and generating a new test case based on the added scene case;
and if the function of the functional module to be optimized has a problem, setting a new test case based on the functional part with the problem.
6. The method of claim 1, wherein determining the functional module to be optimized in the software program based on the target error code comprises:
acquiring a mapping relation between each function module in the preset software program and an error code;
and positioning the functional module to be optimized corresponding to the target error code based on the preset mapping relation between the functional module and the error code in the software program.
7. A data analysis apparatus, comprising:
a receiving unit for receiving a target error code; the target error code is obtained by screening error codes generated in the running process of the software program based on the frequency of the error codes;
a function module to be optimized determining unit, configured to determine a function module to be optimized in the software program based on the target error code; the functional module is a module for forming the software program;
the acquisition unit is used for acquiring the historical test case of the functional module to be optimized;
the analysis unit is used for analyzing the historical test case of the functional module to be optimized and determining the problems of the module to be optimized;
and the test case generation unit is used for generating a new test case based on the problems of the module to be optimized.
8. The apparatus of claim 7, wherein the analysis unit is configured to:
the first obtaining subunit is used for testing the software program based on the historical test case and obtaining a test result;
the first problem determination subunit is used for indicating that the coverage rate of the historical test case of the functional module to be optimized has a problem if the success rate of the test result is greater than a preset test threshold;
and the second problem determination subunit is used for indicating that the function of the functional module to be optimized has a problem if the success rate of the test result is less than a preset test threshold.
9. The apparatus of claim 7, wherein the test case generation unit comprises:
the first test case generation subunit is used for adding a scene case if the coverage rate of the historical test case of the functional module to be optimized has a problem, and generating a new test case based on the added scene case;
and the second test case generation subunit is used for setting a new test case based on the functional part with the problem if the function of the functional module to be optimized has the problem.
10. A data analysis system, comprising:
the error code acquisition end is used for acquiring logs respectively generated in the front end and the logs in the running process of the software program;
data processing terminal for executing the data analysis method of claims 1-6 above.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010294643.7A CN111444110B (en) | 2020-04-15 | 2020-04-15 | Data analysis method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010294643.7A CN111444110B (en) | 2020-04-15 | 2020-04-15 | Data analysis method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111444110A true CN111444110A (en) | 2020-07-24 |
| CN111444110B CN111444110B (en) | 2024-01-23 |
Family
ID=71650159
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010294643.7A Active CN111444110B (en) | 2020-04-15 | 2020-04-15 | Data analysis method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111444110B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130151906A1 (en) * | 2011-12-08 | 2013-06-13 | International Business Machines Corporation | Analysis of Tests of Software Programs Based on Classification of Failed Test Cases |
| CN108629865A (en) * | 2018-04-28 | 2018-10-09 | 百度在线网络技术（北京）有限公司 | Generation method, device, equipment and the storage medium of fault log |
| CN110532122A (en) * | 2019-08-26 | 2019-12-03 | 东软医疗系统股份有限公司 | Failure analysis methods and system, electronic equipment, storage medium |
| CN110597718A (en) * | 2019-08-30 | 2019-12-20 | 苏州浪潮智能科技有限公司 | Automatic test implementation method and system based on AI |
-
2020
- 2020-04-15 CN CN202010294643.7A patent/CN111444110B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130151906A1 (en) * | 2011-12-08 | 2013-06-13 | International Business Machines Corporation | Analysis of Tests of Software Programs Based on Classification of Failed Test Cases |
| CN108629865A (en) * | 2018-04-28 | 2018-10-09 | 百度在线网络技术（北京）有限公司 | Generation method, device, equipment and the storage medium of fault log |
| CN110532122A (en) * | 2019-08-26 | 2019-12-03 | 东软医疗系统股份有限公司 | Failure analysis methods and system, electronic equipment, storage medium |
| CN110597718A (en) * | 2019-08-30 | 2019-12-20 | 苏州浪潮智能科技有限公司 | Automatic test implementation method and system based on AI |
Non-Patent Citations (1)
| Title |
|---|
| 赵则章;江建慧;: "操作系统健壮性测试方法研究" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111444110B (en) | 2024-01-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8689187B2 (en) | Predictive run testing | |
| US20090204946A1 (en) | Intelligent software code updater | |
| CN109828859B (en) | Mobile terminal memory analysis method and device, storage medium and electronic equipment | |
| CN110766167B (en) | Interactive feature selection method, device and readable storage medium | |
| CN110750433A (en) | Interface test method and device | |
| CN111176985B (en) | Software interface performance testing method and device, computer equipment and storage medium | |
| CN114168429A (en) | Error reporting analysis method and device, computer equipment and storage medium | |
| CN111444110A (en) | Data analysis method and device | |
| CN112084108A (en) | Test script generation method and device and related components | |
| CN111506455A (en) | Checking method and device for service release result | |
| CN111143644B (en) | Identification method and device of Internet of things equipment | |
| CN112148621A (en) | Test method and device and electronic equipment | |
| CN113806231A (en) | Code coverage rate analysis method, device, equipment and medium | |
| CN110362464B (en) | Software analysis method and equipment | |
| CN115437903A (en) | Interface test method, device, apparatus, storage medium, and program | |
| CN111225114A (en) | Dial testing method and device, computer equipment and storage medium | |
| CN114253867B (en) | Automatic testing method, device and system based on neural network model | |
| CN113485906B (en) | Method for testing statistical data in financial cloud platform | |
| CN113791818B (en) | Code improvement method, device, medium and equipment based on measurement platform | |
| CN113836012B (en) | Algorithm testing method and device, electronic equipment and storage medium | |
| CN113434364B (en) | Method and device for detecting memory of screen terminal equipment, storage medium and electronic device | |
| CN111638875A (en) | Opposite terminal generation method and system | |
| US20230269142A1 (en) | Network data analysis method, network data analysis functional network element and communication system | |
| CN116991724A (en) | Interface testing method and device based on monitoring log, electronic equipment and storage medium | |
| CN109194459B (en) | Data extraction method and device of transmission signal and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |