CN117033145A - Software development method based on cloud computing - Google Patents
Software development method based on cloud computing Download PDFInfo
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- CN117033145A CN117033145A CN202311296873.7A CN202311296873A CN117033145A CN 117033145 A CN117033145 A CN 117033145A CN 202311296873 A CN202311296873 A CN 202311296873A CN 117033145 A CN117033145 A CN 117033145A
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- 238000012545 processing Methods 0.000 claims abstract description 10
- 230000018109 developmental process Effects 0.000 claims description 39
- 238000012544 monitoring process Methods 0.000 claims description 18
- 230000032683 aging Effects 0.000 claims description 11
- 238000009434 installation Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 abstract description 9
- 238000012360 testing method Methods 0.000 description 8
- 238000013139 quantization Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
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- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/302—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a software system
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Abstract
The invention discloses a software development method based on cloud computing, which comprises the following steps: acquiring update data of application software stored on a cloud server side, and sending the update data of the application software to a data processor; acquiring update data of application software, analyzing and processing based on the update data of the application software to obtain a software update coefficient, and sending the software update coefficient to a software development platform; acquiring a software update coefficient of a data processor, and comparing and judging the software update coefficient to obtain a software update quality degree; when the software update quality difference value is obtained, obtaining software installation data of a client; calculating to obtain a software installation coefficient; the method and the device can effectively improve the quality of software development through the cross analysis of data such as updating, installing and the like of the software development.
Description
Technical Field
The invention relates to the technical field of software development, in particular to a software development method based on cloud computing.
Background
Chinese patent CN112463595A discloses a mobile terminal software development processing method based on cloud computing and a cloud computing software platform, wherein test node analysis is carried out on an object to be tested to obtain multiple information such as test node entity items of a software image file, test node expansion items, list items of a software image file list and the like, then item matching is carried out by taking the test node entity items as main information to obtain corresponding test node entities, and then screening is carried out on the test node entities by combining the test node expansion items and the list items to obtain more accurate target test node entities;
in the prior art, in the software development process, the problem of mutual influence exists between the software update frequency and the software installation frequency, namely, the larger the software update frequency is, the terminal installation frequency is possibly influenced, and in the development process, the terminal installation frequency cannot be effectively monitored, so that the problem of reduced software development quality is caused.
Disclosure of Invention
The invention aims to provide a software development method based on cloud computing, which solves the following technical problems: in the software development process, the problem that the software update frequency and the software installation frequency are mutually influenced exists.
The aim of the invention can be achieved by the following technical scheme:
a software development method based on cloud computing comprises the following steps:
step 1: acquiring update data of application software stored on a cloud server side, and sending the update data of the application software to a data processor; the update data of the application software comprises an update time value ZGs, an update time value ZGx and an update size value ZGd;
step 2: acquiring update data of the application software, analyzing and processing the update data based on the application software to obtain a software update coefficient XRg, and sending the software update coefficient to a software development platform;
step 3: acquiring a software update coefficient of a data processor, and comparing and judging the software update coefficient to obtain a software update quality degree;
step 4: when the software update quality difference value is obtained, obtaining software installation data of a client; the software installation data comprises a software installation effective value and a software installation aging value; calculating to obtain a software installation coefficient; obtaining a software update coefficient and a software installation coefficient, calculating a difference value, and taking an absolute value to obtain a software influence coefficient difference value;
comparing the software influence coefficient difference CRy with the software influence coefficient difference CRy1 and the software influence coefficient difference CRy2; obtaining a monitoring period T; and when the monitoring period is reached each time, the software supervision platform sends the consultation report to the enterprise for software development and makes a corresponding reply.
As a further scheme of the invention: in step 1, the update time value is obtained by:
acquiring the specific time of the current software update, and comparing the specific time of the software update with a corresponding time interval to obtain a time added value;
acquiring the specific time of the current software update and the specific time of the last software update, and calculating to obtain the difference value of the two adjacent software update times to obtain an update interval value;
and calculating to obtain an updated time value through the time added value and the updated interval value.
As a further scheme of the invention: the time interval dividing rule is as follows: the day 24h is divided into two segmented sections, wherein the two segmented sections are (8, 20) and (20, 8), the time added value corresponding to (8, 20) is ZFs1, and the time added value corresponding to (20, 8) is ZFs2.
As a further scheme of the invention: the updated sub-value is obtained in the following manner;
the total number of updates of the current software in the historical time is obtained and the update times value ZGx is marked.
As a further scheme of the invention: the updated size value is obtained by:
obtaining the byte size of the current software update and the byte size of the last software update, calculating to obtain the difference value of the byte sizes of the adjacent software two times, obtaining the updated size value, and marking the updated size value as ZFs.
As a further scheme of the invention: the software update coefficients XRg are obtained and compared with the software update coefficient threshold XRg1 and the software update coefficient threshold XRg;
if the software update coefficient XRg is less than the software update coefficient threshold XRg, a software update quality difference signal is generated.
As a further scheme of the invention: if the software update coefficient threshold XRg1 is less than or equal to the software update coefficient XRg and less than or equal to the software update coefficient threshold XRg, generating a software update quality intermediate signal;
if the software update coefficient threshold XRg2 is less than the software update coefficient XRg, a software update quality priority signal is generated.
As a further scheme of the invention: the software installation effective value is obtained by the following method;
acquiring the number value of client software installation in the historical time when each software update is performed, and marking as ZA i Wherein i represents the number of software updates;
using the formulaThe software installation effectiveness value ZAy is calculated.
As a further scheme of the invention: the software installation aging value is obtained in the following way;
acquiring a time average value of software installation completion of a client when the current software is updated, and marking the time average value as TJd; and the time average total value of the software installation completed by the client during the software update in the historical time is marked as TJl; using the formulaThe software installation age ZAx is calculated.
As a further scheme of the invention: the monitoring period T is calculated by updating the interval value ZJs and the software installation aging value ZAx.
The invention has the beneficial effects that:
according to the cloud machine, when software development is obtained, related data parameters including an update time value, an update time value and an update size value in an update process are obtained, and specific judgment and analysis are carried out according to the update time value, the update time value and the update size value, so that the quality condition of the current software is updated;
the data processor calculates an update time value ZGs, an update time value ZGx and an update size value ZGd to obtain a software update coefficient reflecting the quality condition of software update, so that the software update quantization processing is facilitated;
the software development platform is used for acquiring the software update coefficient of the data processor and comparing and judging the software update coefficient;
and when the monitoring period is reached each time, the software monitoring platform sends a consultation report to an enterprise for software development and replies correspondingly, so that the quality of the software development can be effectively improved through the cross analysis of the data such as updating and installing of the software development.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention is a software development system based on cloud computing, including:
the cloud machine is used for acquiring the update data of the application software stored on the cloud server side and sending the update data of the application software to the data processor;
the data processor is used for acquiring the update data of the application software of the cloud machine, analyzing and processing the update data based on the application software to obtain a software update coefficient, and sending the software update coefficient to the software development platform;
the software development platform is used for acquiring the software update coefficient of the data processor, comparing and judging the software update coefficient to obtain the software update quality degree, and transmitting the software update quality degree to the supervision platform;
the software supervision platform is used for acquiring the software update quality degree of the software development platform, monitoring the application software and distributing the corresponding monitoring degree.
Example 1: the cloud machine is used for acquiring the update data of the application software stored on the cloud server, wherein the update data of the application software comprises an update time value, an update time value and an update size value and is respectively marked as ZGs, ZGx, ZGd;
wherein the update time value is obtained by:
acquiring the specific time of the current software update, comparing the specific time of the software update with a corresponding time interval to obtain a time added value, and marking the time added value as ZFs;
the time interval dividing rule is as follows: dividing a day 24h into two segmentation sections, wherein the two segmentation sections are (8, 20) and (20, 8), (8, 20) correspond to ZFs1, and (20, 8) correspond to ZFs2, the relationship between ZFs1 and ZFs2 satisfies the following formula that zfs1=k is ZFs2,0.21< k <0.42, and ZFs1 and ZFs2 are positive numbers;
acquiring the specific time of the current software update and the specific time of the last software update, calculating to obtain the difference value of the two adjacent software update times, obtaining an update interval value, and marking the update interval value as ZJs;
substituting the obtained time added value ZFs and the update interval value ZJs into a formulaCalculating an update time value ZGs; wherein, a1 and a2 are proportionality coefficients, the value of a1 is 1.06, and the value of a2 is 1.62; the update time value and the time added value are in positive correlation with the update interval value, the larger the numerical value of the time added value and the update interval value is, the larger the update time value is, the better the selection of the specific time and the more time period of the software update is, the more stable the software is, and the smaller the influence is during the update;
the updated sub-value is obtained in the following manner;
acquiring the total number of times of updating the current software in the historical time, and marking an updating time value ZGx;
the updated size value is obtained by:
obtaining the byte size of the current software update and the byte size of the last software update, calculating to obtain the difference value of the byte sizes of the adjacent software two times, obtaining an updated size value, and marking the updated size value as ZFs;
according to the cloud machine, when software development is obtained, related data parameters including an update time value, an update time value and an update size value in the update process are obtained, and specific judgment and analysis are carried out according to the update time value, the update time value and the update size value, so that the quality condition of the current software update is improved.
Example 2: the data processor is used for acquiring the update data of the application software of the cloud machine, and analyzing and processing the update data based on the application software to obtain a software update coefficient;
the analysis processing process based on the update data of the application software is as follows:
substituting the obtained update time value ZGs, update time value ZGx, update size value ZGd into the formulaThe software update coefficients XRg are obtained through calculation, wherein b1, b2 and b3 are proportionality coefficients, the value of b1 is 1.12, the value of b2 is 1.19, and the value of b3 is 1.14;
the data processor calculates the update time value ZGs, the update time value ZGx and the update size value ZGd to obtain the software update coefficient reflecting the software update quality condition, thereby facilitating the quantization processing of the software update.
Example 3: the software development platform is used for acquiring the software update coefficient of the data processor and comparing and judging the software update coefficient;
the specific working process of the software development platform is as follows:
the software update coefficients XRg are obtained and compared with the software update coefficient threshold XRg1 and the software update coefficient threshold XRg; wherein the software update coefficient threshold XRg1< the software update coefficient threshold XRg2;
if the software update coefficient XRg is less than the software update coefficient threshold XRg, generating a software update quality difference signal;
if the software update coefficient threshold XRg1 is less than or equal to the software update coefficient XRg and less than or equal to the software update coefficient threshold XRg, generating a software update quality intermediate signal;
if the software update coefficient threshold XRg2 is less than the software update coefficient XRg, generating a software update quality priority signal;
the software update quality grade signal reflects the condition of the software update quality grade, and the better the grade is, the better the software update times, update influence time and update data size are, so that the better the software quality is.
Example 4: the software supervision platform acquires the software update quality degree of the software development platform, monitors application software and distributes corresponding monitoring degrees;
the working process of the software supervision platform is as follows:
step 1: when the software update quality difference value is obtained, obtaining software installation data of a client; the software installation data comprises a software installation effective value and a software installation aging value, and ZAy and ZAx are marked respectively;
the software installation effective value is obtained by the following method;
acquiring the number value of client software installation in the historical time when each software update is performed, and marking as ZA i Wherein i represents the number of software updates;
using the formulaCalculating to obtain a software installation effective value ZAy;
the software installation aging value is obtained in the following way;
acquiring a time average value of software installation completion of a client when the current software is updated, and marking the time average value as TJd; and the time average total value of the software installation completed by the client during the software update in the historical time is marked as TJl; using the formulaCalculating to obtain a software installation aging value ZAx;
step 2: substituting the obtained software installation effective value ZAy and software installation effective value ZAx into a formulaCalculating to obtain a software installation coefficient XRa; wherein, c1 and c2 are proportionality coefficients, the value of c1 is 1.95, and the value of c2 is 1.40;
step 3: obtaining a software update coefficient XRg and a software installation coefficient XRa, calculating a difference value, and taking an absolute value to obtain a software influence coefficient difference value CRy;
comparing the software influence coefficient difference CRy with the software influence coefficient difference CRy1 and the software influence coefficient difference CRy2; wherein, the software influence coefficient difference value CRy1< the software influence coefficient difference value CRy2;
if the software influence coefficient difference value CRy is smaller than the software influence coefficient difference value CRy1, a first monitoring period T is generated;
if the software influence coefficient difference value CRy1 is less than or equal to the software influence coefficient difference value CRy2 and less than or equal to the software influence coefficient difference value CRy2, a first monitoring period T/2 is generated;
if the software influence coefficient difference value CRy2 is smaller than the software influence coefficient difference value CRy, a first monitoring period T/4 is generated;
wherein, the monitoring period T is obtained by the following way:
the update interval value ZJs and the software installation aging value ZAx are obtained and calculated by the following formula;
the formula isWherein e is a mathematical constant, alpha is an error correction coefficient, and the value is 20.284;
when the quality difference of the software update is obtained, the supervision platform analyzes and judges according to the software installation data of the client, combines the update interval value ZJs and the software installation time effect value ZAx, gives a corresponding monitoring period, and when the monitoring period is reached each time, the software supervision platform sends a consultation report to an enterprise for software development and replies correspondingly, so that the quality of the software development can be effectively improved through the cross analysis of the data such as the update, the installation and the like of the software development.
Example 5: based on the above embodiments 1-4, the present invention is a software development method based on cloud computing, comprising the following steps:
step 1: acquiring update data of application software stored on a cloud server side, and sending the update data of the application software to a data processor;
step 2: acquiring update data of application software of the cloud machine, analyzing and processing the update data based on the application software to obtain a software update coefficient, and sending the software update coefficient to a software development platform;
step 3: acquiring a software update coefficient of a data processor, comparing and judging the software update coefficient to obtain a software update quality degree, and transmitting the software update quality degree to a supervision platform;
step 4: the quality degree of the software update of the software development platform is obtained, the application software is monitored, and the corresponding monitoring degree is distributed.
The working principle of the invention is as follows: according to the cloud machine, when software development is obtained, related data parameters including an update time value, an update time value and an update size value in an update process are obtained, and specific judgment and analysis are carried out according to the update time value, the update time value and the update size value, so that the quality condition of the current software is updated;
the data processor calculates an update time value ZGs, an update time value ZGx and an update size value ZGd to obtain a software update coefficient reflecting the quality condition of software update, so that the software update quantization processing is facilitated;
the software development platform is used for acquiring the software update coefficient of the data processor and comparing and judging the software update coefficient;
and when the monitoring period is reached each time, the software monitoring platform sends a consultation report to an enterprise for software development and replies correspondingly, so that the quality of the software development can be effectively improved through the cross analysis of the data such as updating and installing of the software development.
The formulas are all formulas with dimensions removed and numerical calculation, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by a person skilled in the art according to the actual situation;
the foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (10)
1. The software development method based on cloud computing is characterized by comprising the following steps of:
step 1: acquiring update data of application software stored on a cloud server side; the update data of the application software comprises an update time value ZGs, an update time value ZGx and an update size value ZGd;
step 2: acquiring update data of the application software, and analyzing and processing based on the update data of the application software to obtain a software update coefficient XRg;
step 3: acquiring a software update coefficient, and comparing and judging the software update coefficient to obtain the software update quality degree;
step 4: when the software update quality difference value is obtained, obtaining software installation data of a client; calculating to obtain a software installation coefficient; obtaining a software update coefficient and a software installation coefficient, calculating a difference value, and taking an absolute value to obtain a software influence coefficient difference value; the software installation data comprises a software installation effective value and a software installation aging value;
comparing the software influence coefficient difference CRy with the software influence coefficient difference CRy1 and the software influence coefficient difference CRy2; obtaining a monitoring period T; and when the monitoring period is reached each time, the software supervision platform sends the consultation report to the enterprise for software development and makes a corresponding reply.
2. The method of claim 1, wherein in step 1, the update time value is obtained by:
acquiring the specific time of the current software update, and comparing the specific time of the software update with a corresponding time interval to obtain a time added value;
acquiring the specific time of the current software update and the specific time of the last software update, and calculating to obtain the difference value of the two adjacent software update times to obtain an update interval value;
and calculating to obtain an updated time value through the time added value and the updated interval value.
3. The cloud computing-based software development method of claim 2, wherein the time interval division rule is: the day 24h is divided into two segmented sections, wherein the two segmented sections are (8, 20) and (20, 8), the time added value corresponding to (8, 20) is ZFs1, and the time added value corresponding to (20, 8) is ZFs2.
4. The method for developing software based on cloud computing as recited in claim 1, wherein in step 1, the updated sub-value is obtained by;
the total number of updates of the current software in the historical time is obtained and the update times value ZGx is marked.
5. The method for developing software based on cloud computing as recited in claim 1, wherein in step 1, the updated size value is obtained by:
obtaining the byte size of the current software update and the byte size of the last software update, calculating to obtain the difference value of the byte sizes of the adjacent software two times, obtaining the updated size value, and marking the updated size value as ZFs.
6. The method of claim 1, wherein in step 3, if the software update coefficient XRg is less than the software update coefficient threshold XRg, a software update quality difference signal is generated.
7. The method according to claim 1, wherein in step 3, if the software update coefficient threshold XRg1 is less than or equal to the software update coefficient XRg and less than or equal to the software update coefficient threshold XRg2, a software update quality intermediate signal is generated;
if the software update coefficient threshold XRg2 is less than the software update coefficient XRg, a software update quality priority signal is generated.
8. The method for developing software based on cloud computing as recited in claim 1, wherein in step 4, the software installation effective value is obtained by;
acquiring the number value of client software installation in the historical time when each software update is performed, and marking as ZA i Wherein i represents the number of software updates;
using the formulaThe software installation effectiveness value ZAy is calculated.
9. The method for developing software based on cloud computing as recited in claim 1, wherein in step 4, the software installation aging value is obtained by;
acquiring a time average value TJd of software installation completion of a client when current software is updated; and a time average total value TJl for the client to complete the software installation at the time of the software update in the history time; using the formulaCalculated to obtainSoftware installation aging value ZAx.
10. The method according to claim 1, wherein in step 4, the monitoring period T is calculated by updating the interval value ZJs and the software installation aging value ZAx.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003162529A (en) * | 2001-11-26 | 2003-06-06 | Mitsubishi Electric Corp | Quality management database for software component |
| US8954951B1 (en) * | 2013-04-09 | 2015-02-10 | Google Inc. | Stop distribution of application updates |
| CN107077362A (en) * | 2014-10-27 | 2017-08-18 | 微软技术许可有限责任公司 | Install and more new software system |
| CN114546864A (en) * | 2022-02-24 | 2022-05-27 | 深圳创维－Rgb电子有限公司 | Software quality evaluation method, device, equipment and storage medium |
| CN115080412A (en) * | 2022-06-25 | 2022-09-20 | 平安银行股份有限公司 | Software update quality evaluation method, device, equipment and computer storage medium |
| CN115840697A (en) * | 2022-12-02 | 2023-03-24 | 北京百度网讯科技有限公司 | Software testing method and device |
-
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- 2023-10-09 CN CN202311296873.7A patent/CN117033145B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003162529A (en) * | 2001-11-26 | 2003-06-06 | Mitsubishi Electric Corp | Quality management database for software component |
| US8954951B1 (en) * | 2013-04-09 | 2015-02-10 | Google Inc. | Stop distribution of application updates |
| CN107077362A (en) * | 2014-10-27 | 2017-08-18 | 微软技术许可有限责任公司 | Install and more new software system |
| CN114546864A (en) * | 2022-02-24 | 2022-05-27 | 深圳创维－Rgb电子有限公司 | Software quality evaluation method, device, equipment and storage medium |
| CN115080412A (en) * | 2022-06-25 | 2022-09-20 | 平安银行股份有限公司 | Software update quality evaluation method, device, equipment and computer storage medium |
| CN115840697A (en) * | 2022-12-02 | 2023-03-24 | 北京百度网讯科技有限公司 | Software testing method and device |
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