Software development method based on cloud computing

Abstract

The invention discloses a software development method based on cloud computing, relates to the technical field of software development, and solves the technical problem that the use experience of a user is reduced due to the fact that feedback is often difficult to timely and specifically update in a software development method. The method comprises the following steps: S1, acquiring a software demand, and selecting a proper cloud computing server for software development according to the software demand; s2, acquiring user feedback data, optimizing the software according to the user feedback data, and generating a software update package and update content; s3, generating an update reminding signal according to the update content; s4, sending the update reminding signal to a corresponding user terminal; and the software is pertinently updated in time according to the feedback data of the user, so that the use experience of the user is improved.

Description

Technical Field

[0001] The present application belongs to the field of software development technology, and relates to software development technology, and specifically to a software development method based on cloud computing. Background Art

[0002] With the rapid development of computing technology, cloud computing, as a computing resource model that provides on-demand allocation, network access, and rapid expansion, has been widely used. Cloud computing platforms such as Amazon AWS, Google Cloud, and Microsoft Azure provide users with a large number of services such as computing, storage, databases, AI, and machine learning.

[0003] The prior art (the invention patent application with publication number CN117453190A) discloses a software development method based on cloud computing, which relates to the field of software development technology. The present invention parses software development requests through cloud computing software development services, obtains accurate software development requirements, and generates software code that accurately corresponds to the software development requests, with high accuracy; selects a cloud service provider according to software development requirements, thereby improving the efficiency of software development; uses the development tools and services provided by the cloud service provider to perform automated testing and deployment of the code, and generates software code, thereby saving a lot of time. The scheme is: receiving a software development request, parsing the software development request through cloud computing software development services, and obtaining software development requirements; selecting a cloud service provider according to software development requirements; using the development tools and services provided by the cloud service provider to perform automated testing and deployment of the code, and generating software code that corresponds to the software development request, so as to complete software development.

[0004] The above-mentioned cloud computing software development method uses a cloud computing platform instead of traditional offline development during software development, which greatly improves the efficiency of software development. After the software development is completed and successfully launched, users will download and use the software. During the use process, users will find some software defects and generate a series of feedback. Existing software development methods often find it difficult to make timely and targeted updates to feedback, resulting in a decrease in user experience. Therefore, a software development method based on cloud computing is needed. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a cloud computing-based software development method to solve the technical problem that software development methods often find it difficult to make timely and targeted updates to feedback, resulting in a decline in user experience.

[0006] To achieve the above objectives, the first aspect of the present application provides a software development method based on cloud computing, comprising the following steps:

[0007] S1: Obtain software requirements and select appropriate cloud computing servers for software development based on the software requirements;

[0008] S2: Obtain user feedback data, optimize the software according to the user feedback data, and generate software update packages and update content;

[0009] S3: Generate an update reminder signal according to the update content;

[0010] S4: Sending an update reminder signal to a corresponding user terminal.

[0011] The present application selects the corresponding cloud computing server to develop the software according to the software requirements; during the use of the software, the feedback data of the users is obtained, the feedback content in the feedback data is analyzed, and the software is updated and developed; and the appropriate update range is selected according to the update content, and an update reminder signal is generated; the software is updated in a targeted manner according to the user feedback data in a timely manner, thereby improving the user experience.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11: Obtaining software requirements; the software requirements include functional requirements, performance requirements and data requirements;

[0014] S12: Inputting the functional requirements, performance requirements and data requirements into a server matching model to obtain a matching degree with each cloud computing server; the server matching model is obtained by training an artificial intelligence model;

[0015] S13: Select the cloud computing server with the highest matching degree for software development.

[0016] Preferably, the server matching model is obtained by training an artificial intelligence model, including:

[0017] Obtain the functional characteristics of several cloud computing servers and the functional requirements, performance requirements and data requirements in several software requirements, as well as the matching degree between the several software requirements and the functional characteristics of the cloud computing servers; the matching degree is the matching situation between the software requirements and the cloud computing servers obtained by comprehensive evaluation of the software requirements and the functional characteristics of the cloud computing servers by experts. The greater the value of the matching degree, the more suitable the corresponding cloud computing server is for the corresponding software development; the functional characteristics of the cloud computing server are the functions it can provide,

[0018] Integrate several functional requirements, performance requirements, data requirements, and the functional characteristics and matching degree of each cloud computing server into training data, and integrate the functional requirements, performance requirements, data requirements, and the matching degree of each cloud computing server into test data; use the training data to train the artificial intelligence model, and use the test data to test the trained artificial intelligence model; specifically, after training the artificial intelligence model with the training data, extract the functional requirements, performance requirements, and data requirements in the test data, input them into the trained artificial intelligence model, output the matching degree with each cloud computing server, and judge whether the difference between the matching degree of each output and the matching degree recorded in the corresponding test data is within an acceptable range; if yes, proceed to the next set of test data for testing; if not, adjust the relevant parameters of the artificial intelligence and continue to test the current set of test data; until a set proportion of the test data passes the test; finally, a server matching model is obtained, whose input is the functional requirements, performance requirements and data requirements in the software requirements; and whose output is the matching degree between the software requirements and each cloud computing server; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0019] Preferably, S2 comprises the following steps:

[0020] S21: Obtain user feedback data; the user feedback data includes a number of feedback contents, as well as the feedback time KTi and the feedback quantity KS corresponding to the feedback contents; i is the number of the feedback user, and the feedback time is the time from the corresponding user's feedback to the present;

[0021] S22: Calculate the reference coefficient CK of each feedback content by formula CK=α1×KS / YS+α2×∑(KTi / PTi); wherein α1 and α2 are proportional coefficients, and the specific values ​​are set according to experience; YS is the total number of users; PTi is the usage time of the feedback user numbered i, i=1, 2, ..., I; I is the total number of feedback users; the usage time is the time from the user downloading the software to the present;

[0022] S23: Determine whether the reference coefficient corresponding to each feedback content is greater than the set emergency reference coefficient threshold; if yes, mark the corresponding feedback content as emergency update content and proceed to S24; if no, mark the corresponding feedback content as ordinary update content and proceed to S26;

[0023] S24: Determine whether there is an update task currently; if yes, proceed to S25; if no, update the urgent update content, software update package and update content;

[0024] S25: Determine whether the update task corresponds to an emergency update task; if yes, wait for the emergency update task to be updated, then update the emergency update content to obtain the software update package and update content; if no, stop the current update task, update the emergency update content to obtain the software update package and update content; the emergency update task is a task when the emergency update content is updated;

[0025] S26: Determine whether there is an update task currently; if yes, wait for the update task to be completed, and then update the normal update content to obtain the software update package and update content; if no, update the normal update content to obtain the software update package and update content; the update content includes emergency update content or normal update content.

[0026] Preferably, the software update package and update content are obtained by the following methods, including:

[0027] Obtain feedback content corresponding to urgent update content or ordinary update content; match the feedback content with functional requirements, performance requirements and data requirements to obtain corresponding update requirements; select a cloud computing server to update and develop the software according to the update requirements to obtain an update software package; and integrate the corresponding feedback content and update requirements into update content.

[0028] Preferably, matching the feedback content with the functional requirements, performance requirements and data requirements to obtain corresponding update requirements includes:

[0029] Extract several keywords from the feedback content, match the keywords to their corresponding functional requirements, performance requirements, and data requirements, and integrate the matched functional requirements, performance requirements, and data requirements into update requirements.

[0030] Preferably, S3 comprises the following steps, including:

[0031] S31: Obtain update content and determine whether the update content is an urgent update content; if yes, proceed to S32; if no, proceed to S33;

[0032] S32: Generate a comprehensive update reminder signal;

[0033] S33: Obtain the user ID in the user feedback data to which the feedback content corresponding to the updated content belongs; generate a partial update reminder signal according to a number of user IDs; the corresponding update reminder signal includes a full update reminder signal and a partial update reminder signal.

[0034] Compared with the prior art, the beneficial effects of this application are:

[0035] 1. This application selects the corresponding cloud computing server to develop the software according to the software requirements; during the use of the software, the feedback data of the users is obtained, the feedback content in the feedback data is analyzed, and the software is updated and developed; and the appropriate update range is selected according to the update content, and an update reminder signal is generated; the software is updated in a targeted manner according to the user feedback data in a timely manner, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A schematic diagram of the steps of the software development method in this application;

[0038] Figure 2 Schematic diagram of the software update steps in this application. DETAILED DESCRIPTION

[0039] The technical solution of the present application will be described clearly and completely below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0040] See also Figure 1 The first aspect of the present application provides a software development method based on cloud computing, comprising the following steps:

[0041] S1: Obtain software requirements, which include functional requirements, performance requirements and data requirements; functional requirements include the functions that the software needs to implement, performance requirements include the performance of the software when it is working, and data requirements include the type and format of the data used during software development and subsequent use; select a suitable cloud computing server for software development based on software requirements; cloud computing servers are used to provide basic services and development platforms for software development, such as basic computing resources, servers, networks and storage, as well as operating systems, databases and development tools, for users to develop and run applications.

[0042] S2: Obtain user feedback data, optimize the software according to the user feedback data, and generate software update packages and update content; user feedback data is the feedback generated by users on various functions and performance of the software during the use of the corresponding software, and the feedback is transmitted to the software developer through the software itself or other channels;

[0043] S3: Generate an update reminder signal according to the update content; the update reminder signal is a signal to remind the software user of the update;

[0044] S4: Sending an update reminder signal to a corresponding user terminal.

[0045] This embodiment selects the corresponding cloud computing server to develop the software according to the software requirements; during the use of the software, the feedback data of the users is obtained, the feedback content in the feedback data is analyzed, and the software is updated and developed; and a suitable update range is selected according to the update content, and an update reminder signal is generated; the software is updated in a targeted manner according to the user feedback data in a timely manner, thereby improving the user experience.

[0046] S1 includes the following steps:

[0047] S11: Obtaining software requirements; the software requirements include functional requirements, performance requirements and data requirements;

[0048] S12: Inputting the functional requirements, performance requirements and data requirements into a server matching model to obtain a matching degree with each cloud computing server; the server matching model is obtained by training an artificial intelligence model;

[0049] S13: Select the cloud computing server with the highest matching degree for software development.

[0050] The server matching model is obtained through artificial intelligence model training, including:

[0051] Obtain the functional characteristics of several cloud computing servers and the functional requirements, performance requirements and data requirements in several software requirements, as well as the matching degree between the several software requirements and the functional characteristics of the cloud computing servers; the matching degree is the matching situation between the software requirements and the cloud computing servers obtained by comprehensive evaluation of the software requirements and the functional characteristics of the cloud computing servers by experts. The greater the value of the matching degree, the more suitable the corresponding cloud computing server is for the corresponding software development; the functional characteristics of the cloud computing server are the functions it can provide, such as

[0052] Integrate several functional requirements, performance requirements, data requirements, and the functional characteristics and matching degree of each cloud computing server into training data, and integrate the functional requirements, performance requirements, data requirements, and the matching degree of each cloud computing server into test data; use the training data to train the artificial intelligence model, and use the test data to test the trained artificial intelligence model; specifically, after training the artificial intelligence model with the training data, extract the functional requirements, performance requirements, and data requirements in the test data, input them into the trained artificial intelligence model, output the matching degree with each cloud computing server, and judge whether the difference between the matching degree of each output and the matching degree recorded in the corresponding test data is within an acceptable range; if yes, proceed to the next set of test data for testing; if not, adjust the relevant parameters of the artificial intelligence and continue to test the current set of test data; until a set proportion of the test data passes the test; finally, a server matching model is obtained, whose input is the functional requirements, performance requirements and data requirements in the software requirements; and whose output is the matching degree between the software requirements and each cloud computing server; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0053] See also Figure 2 , S2 includes the following steps:

[0054] S21: Obtain user feedback data; it can be understood that the user feedback data here refers to the feedback data of all users; and it is processed, and the content of each user feedback is classified and processed. When the feedback content is similar, the corresponding feedback content will be classified into the same type of feedback content; the user feedback data includes a number of feedback contents, and the feedback time KTi and feedback quantity KS of the corresponding feedback content; i is the number of the feedback user, and the feedback time is the time interval from receiving the corresponding user feedback data to the present;

[0055] S22: The reference coefficient CK of each feedback content is calculated by the formula CK=α1×KS / YS+α2×∑(KTi / PTi); wherein α1 and α2 are proportional coefficients, and the specific values ​​are set according to experience; YS is the total number of users; PTi is the usage time of the feedback user numbered i, i=1, 2, ..., I; I is the total number of feedback users; the usage time is the time interval from the user downloading the software to the current time; this embodiment generates the reference coefficient of each feedback content by the above formula; when the feedback time of each user is closer to the usage time, it means that the user has discovered the corresponding problem when he just started using the software, which means that the problem is obvious and has a great impact. The problem needs to be solved urgently, so the reference coefficient is set to be larger; when the number of people who give feedback on the corresponding problem is more, the universality of the corresponding problem is stronger, the number of people affected is more, and the problem needs to be solved urgently, so the reference coefficient is set to be larger;

[0056] S23: Determine whether the reference coefficient corresponding to each feedback content is greater than a set emergency reference coefficient threshold; the emergency reference coefficient threshold is set based on experience; if yes, mark the corresponding feedback content as emergency update content and enter S24; if no, mark the corresponding feedback content as ordinary update content and enter S26;

[0057] S24: Determine whether there is an update task currently; if yes, proceed to S25; if no, update the urgent update content, software update package and update content;

[0058] S25: Determine whether the update task corresponds to an emergency update task; if yes, wait for the emergency update task to be updated, then update the emergency update content to obtain the software update package and update content; if no, stop the current update task, update the emergency update content to obtain the software update package and update content; the emergency update task is a task when the emergency update content is updated;

[0059] S26: Determine whether there is an update task currently; if yes, wait for the update task to be completed, and then update the normal update content to obtain the software update package and update content; if no, update the normal update content to obtain the software update package and update content; the update content includes emergency update content or normal update content.

[0060] This embodiment determines whether the corresponding feedback content needs to be updated urgently based on the number of feedback and feedback time of each user feedback data, and develops the update content of the software in a targeted manner according to the judgment result, so as to enhance the user experience.

[0061] Software update packages and update content are obtained through the following methods, including:

[0062] Obtain feedback content corresponding to urgent update content or ordinary update content; match the feedback content with functional requirements, performance requirements and data requirements to obtain corresponding update requirements; select a cloud computing server to update and develop the software according to the update requirements to obtain an update software package; and integrate the corresponding feedback content and update requirements into update content.

[0063] Match the feedback content with the functional requirements, performance requirements and data requirements to obtain the corresponding update requirements, including: extracting several keywords in the feedback content, and matching the keywords to their corresponding functional requirements, performance requirements and data requirements. For example, if the keyword in the feedback content is "file input in image format is not supported", then this keyword is the data format requirement and will be matched to the data requirement; if the keyword is "file upload speed is slow", then this keyword is the software performance requirement and will be matched to the performance requirement; matching can be done through expert experience or by training relevant artificial intelligence models for matching; and integrating the functional requirements, performance requirements and data requirements after matching into update requirements.

[0064] S3 includes the following steps, including:

[0065] S31: Obtain update content and determine whether the update content is an urgent update content; if yes, proceed to S32; if no, proceed to S33;

[0066] S32: Generate a comprehensive update reminder signal;

[0067] S33: Obtain the user ID in the user feedback data to which the feedback content corresponding to the updated content belongs; generate a partial update reminder signal according to a number of user IDs; the corresponding update reminder signal includes a full update reminder signal and a partial update reminder signal.

[0068] It is worth noting that after the partial update signal is generated in the present embodiment, some users perform software update; user feedback data of some users after the software update is obtained, and it is determined whether the correlation between the feedback content in the user feedback data and the feedback content before the update is less than the set correlation threshold; if so, a comprehensive update reminder signal is generated to remind other users to update; if not, the software is updated according to the corresponding feedback content, and a partial update reminder signal is generated; wherein the correlation degree indicates the correlation between the two feedback contents; the more similar contents there are between the two feedback contents, the greater the corresponding correlation degree value; it can be obtained by training a natural speech model, or by the cosine similarity of the word vectors between the two feedback data, or by expert evaluation.

[0069] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0070] How this application works:

[0071] The present application selects the corresponding cloud computing server to develop the software according to the software requirements; during the use of the software, the feedback data of the users is obtained, the feedback content in the feedback data is analyzed, and the software is updated and developed; and the appropriate update range is selected according to the update content, and an update reminder signal is generated; the software is updated in a targeted manner according to the user feedback data in a timely manner, thereby improving the user experience.

[0072] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. A software development method based on cloud computing, characterized in that: The following steps are involved: S1: Obtain software requirements and select appropriate cloud computing servers for software development based on the software requirements; S2: Obtain user feedback data, optimize the software according to the user feedback data, and generate software update packages and update content; S3: Generate an update reminder signal according to the update content; S4: Sending an update reminder signal to a corresponding user terminal.

2. The cloud computing-based software development method according to claim 1, characterized in that: The S1 comprises the following steps: S11: Obtaining software requirements; the software requirements include functional requirements, performance requirements and data requirements; S12: Inputting the functional requirements, performance requirements and data requirements into a server matching model to obtain a matching degree with each cloud computing server; the server matching model is obtained by training an artificial intelligence model; S13: Select the cloud computing server with the highest matching degree for software development.

3. The cloud computing-based software development method according to claim 2, characterized in that: The server matching model is obtained through artificial intelligence model training, including: Obtaining functional characteristics of several cloud computing servers and functional requirements, performance requirements and data requirements in several software requirements, as well as the degree of matching between the several software requirements and the functional characteristics of the cloud computing servers; Integrate several functional requirements, performance requirements, data requirements, as well as the functional characteristics and matching degree of each cloud computing server into training data, and integrate the functional requirements, performance requirements, data requirements, as well as the matching degree of each cloud computing server into test data; use the training data to train the artificial intelligence model, and use the test data to test the trained artificial intelligence model; finally, the functional requirements, performance requirements and data requirements in the software requirements are input; the server matching model with the matching degree of the software requirements and each cloud computing server as output is obtained; the artificial intelligence model includes the BP neural network model and the RBF neural network model.

4. The cloud computing-based software development method according to claim 1, characterized in that: The S2 comprises the following steps: S21: Obtain user feedback data; the user feedback data includes a number of feedback contents, as well as the feedback time KTi and feedback quantity KS corresponding to the feedback contents; i is the number of the feedback user, S22: Calculate the reference coefficient CK of each feedback content by the formula CK=α1×KS / YS+α2×∑(KTi / PTi); wherein α1 and α2 are proportional coefficients; YS is the total number of users; PTi is the usage time of the feedback user numbered i, i=1, 2, ..., I; I is the total number of feedback users; S23: Determine whether the reference coefficient corresponding to each feedback content is greater than the set emergency reference coefficient threshold; if yes, mark the corresponding feedback content as emergency update content and proceed to S24; if no, mark the corresponding feedback content as ordinary update content and proceed to S26; S24: Determine whether there is an update task currently; if yes, proceed to S25; if no, update the urgent update content, software update package and update content; S25: Determine whether the update task corresponds to an emergency update task; if yes, wait for the emergency update task to be updated, then update the emergency update content to obtain the software update package and update content; if no, stop the current update task, update the emergency update content to obtain the software update package and update content; the emergency update task is a task when the emergency update content is updated; S26: Determine whether there is an update task currently; if yes, wait for the update task to be completed, and then update the normal update content to obtain the software update package and update content; if no, update the normal update content to obtain the software update package and update content; the update content includes emergency update content or normal update content.

5. The cloud computing-based software development method according to claim 4, characterized in that: The software update package and update content are obtained by the following methods, including: Obtain feedback content corresponding to urgent update content or ordinary update content; match the feedback content with functional requirements, performance requirements and data requirements to obtain corresponding update requirements; select a cloud computing server to update and develop the software according to the update requirements to obtain an update software package; and integrate the corresponding feedback content and update requirements into update content.

6. The cloud computing-based software development method according to claim 5, characterized in that: The matching of the feedback content with the functional requirements, performance requirements and data requirements to obtain the corresponding update requirements includes: Extract several keywords from the feedback content, match the keywords to their corresponding functional requirements, performance requirements, and data requirements, and integrate the matched functional requirements, performance requirements, and data requirements into update requirements.

7. The cloud computing-based software development method according to claim 1, characterized in that: The S3 The following steps are involved, including: S31: Obtain update content and determine whether the update content is an urgent update content; if yes, proceed to S32; if no, proceed to S33; S32: Generate a comprehensive update reminder signal; S33: Obtain the user ID in the user feedback data to which the feedback content corresponding to the updated content belongs; generate a partial update reminder signal according to a number of user IDs; the corresponding update reminder signal includes a full update reminder signal and a partial update reminder signal.

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