# Software Development, Configuration, Monitoring, and Management of Artificial Neural Networks

Authors: Yongbin Tang; Xi Chen
Journal: Security and Communication Networks
 (2022)

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## Abstract

With the increasing demand for software systems, the software development industry is also developing rapidly. With the development of information technology, the more functions of the software, the more valuable it is, so the function design of the software becomes more complicated and difficult. The design of software system functions is increasingly large and complex. Scientific and effective use of software configuration management can well deal with collaborative work problems such as version management and change control in the software development process. In the process of software development and configuration, there will always be many problems that are difficult to detect. For example, when inputting the program code, there are not always some letter or space errors, and these errors are difficult to detect in time. For this reason, we need to establish a monitoring and management system for software development. As a computing model of human brain neural network, the artificial neural network can play the role of monitoring and management when it is applied to software development and configuration, which provides support for the security and scientificity of software development and configuration systems. This study studies the role and effectiveness of an artificial neural network in the monitoring and management of software development and configuration and validates it through experiments. The experimental results show that the artificial neural network has a strong ability to identify the problems in the software development configuration, which can improve the software development efficiency by at least 20%. It can improve the quality of software development and then improve the life cycle of software.


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## Body

## 1. Introduction

At present, the demand for software is increasing day by day, and the competition among software enterprises is becoming increasingly fierce. The continuous pursuit of high quality and low cost in software development is the key factor for enterprises to survive in the large competitive pressure. If it is necessary to develop low-cost and high-quality software systems, the configuration management process of software development is the top priority. Because configuration management can coordinate the work of team members and promote team coordination and unity [1]; however, the current software development lacks unified management for its configuration management process, and each configuration process cannot be seamlessly connected, which will lead to problems. Due to the lack of development time and development funds, the current software has left a lot of problems in the process of software development. These problems can only be continuously updated and upgraded in the later stage, which greatly increases the cost and time of software development. Moreover, the software production cannot reach the scale and cannot produce the software standard component warehouse within the software enterprise, so that the application software products are always in a state of low level and repeated development. Not only the time cannot be guaranteed but also the cost cannot be reduced, so that the product has no market competitiveness. This study studies the application of artificial neural networks to software development, configuration, monitoring, and management, which can promote the competitiveness of software development enterprises, promote the systematization and unity of software systems in the management process, and improve software quality. This enables the development team to improve the collaborative workability of the development team, to share information in a timely and effective manner, to reduce the blind and ineffective modification of software configuration items in the project, and to reduce the occurrence of some unnecessary problems. It monitors and manages the process of software development and configuration, which can effectively find the problems existing in the software development process, obtain effective solutions, solve all problems at one time, and correct it at any time during the development process to reduce the accumulation of a large number of problems later, reduce the subsequent updates and upgrades in use, and improve the use cycle of the software [2]. It can also standardize the work of developers and improve software reusability and collaboration between groups. It can effectively manage the software development project, to complete the software project plan according to the process and budget and realize the expected economic and social benefits.To improve the management process of software development configuration, a large number of researchers have done systematic research in this area. Among them, Stefanic researched methodologies and software workbenches to provide software engineers with complete application lifecycle support. Additionally, he presents a new high-level concept for exchanging complex dynamic data using the OASIS TOSCA standard (mainly for static contexts). Experiments show that it supports the concept of dynamic TOSCA, which directly supports dynamic reconfiguration of the application runtime [3]. Soujanya believes that some software systems need automatic and dynamic reconfiguration and proposes that SPL should have a processing mechanism and feature model is an important part of SPL. Empirical studies on improved prototypes show significant performance advantages of using ontology for SPL configuration management [4]. To improve the software development life cycle (SDLC), Hiererra adopts the analysis method based on CMMI development version 1.3. He proposed corresponding solutions for the deficiencies in specific practice and raised the score of specific practices to [5]. Zaman proposes a recommender system for development teams and clients in response to the rapid growth in software demand. Experimental results show that the proposed recommender system is feasible and effective and can provide better recommendations for developers and customers [6]. One method proposed by Fahmy to manage software changes is software configuration management (SCM). His research aims to explore how to assess the competencies of supply chain management practitioners by defining competency standards and developing competency assessment frameworks [7]. Their research has a good inspiration for software development, but they all ignore the process of software configuration, fail to fully consider the collaborative ability of the software development team and the development methods of different types of software, fail to fully consider the factors affecting software development, and lack the support of theory and data.The research of this study has the following innovations: (1) the artificial neural network is used to monitor the process of software development, so that the program code can be corrected in time and effectively in the process of inputting; (2) artificial neural network is used to connect the development team of the headquarters with the teams distributed in various places, promote the work communication between the local team and the headquarters team, and improve the efficiency of software development and the collaboration ability of the company team; and (3) in the configuration management of the software, the artificial neural network is used to change the common code, and the shared code cannot be notified to all team members in time, to promote the efficiency of work communication between teams.


## 2. The Method of Software Development Configuration Monitoring and Management

### 2.1. Artificial Neural Network

The artificial neural network abstracts and simplifies the neurons of the human brain from the perspective of information processing in the human brain, so that it can establish a simple model similar to the processing of the human brain. It builds a type of simple model and forms different networks according to different connection methods. The model has the ability of self-adaptation, self-organization, and self-learning similar to the human brain and has practical effects in the fields of pattern recognition, combinatorial optimization, prediction, and prediction [8].The structure of a single neuron in the artificial neural network is shown in Figure1, and if the artificial neural network is applied to software development, then the artificial neural network can follow up the work of software development in real time and find errors in time for correction. Software development requires writing code, and there will be some errors in the process of inputting the code, which the developer cannot detect during the development process. Therefore, we need to add an artificial neural network model to the configuration to identify errors in the code input process. We need to understand the artificial neural network, and a series of algorithms need to be designed in the artificial neural network model [9]. In Figure 1, when the amount of information input to a single neuron is f, the information will be divided and processed for discrimination. Each information stream will have an input signal k, and the calculation form of the amount of information t that needs to be processed in each information stream is as follows:(1)t1=f∗k1∗s1,t2=12f∗k2∗s2−t1.Figure 1
Model structure of a single neuron.Among them,s is the processing time of each information flow. Because the amount of information that each information stream needs to process is not necessarily the same, the information processing time may also be different. Then, when there are n information streams, the amount of information that needs to be processed by the nth information stream is as follows:(2)tn=1nf∗kn+sn−tn−1.When the artificial neural network monitors the code developed by the software during the configuration, it will mark the code with an error during the input process in red according to the internal settings of the system and return to the original program, and a back check will appear. The number of code input errors isd, and then, the number of codes u for the entire return check is as follows:(3)u=1ntn∗su∗ku−tn−1∗k.Until all input software development codes are corrected, the next monitoring and management can be passed. For this reason, the final quantity input into the total node system needs to be calculated according to the specific amount of data output by a single node, that is, a single information flow to the total node as follows:(4)W1=∑n=1k∗t1∗i1f,W2=∑n=2k∗t2∗i2f−W1.Then, the number of thenth data stream input to the summary point is as follows:(5)Wn=∑nk∗tn∗inf−W1.Since the amount of information processed by each information flow has been obtained, the formula for calculating the amount of informationW input into the summary point is as follows:(6)W=W1+W2+⋯+Wnn∗∑nk∗tn∗inf∗ℓ.Among them,ℓ represents a parallel matrix, and its form is as follows:(7)ℓ=tnknksnini.In the summary point, the input programming code is uniformly monitored and managed to monitor whether the software developed has originally set functions. If the originally set function is missing, the artificial neural network will automatically respond, and then, a prompt will appear for the developer to make an effective reference; if the originally set software function is not omitted, the code in the program will continue to enter the node of the activation function. The form of the activation function is as follows:(8)Ge=11+e−k∗s.When the programming code developed by the software is imported into this node, the programming code needs to be activated and a thresholdh will appear on this node. Under the action of this activation function, the activation procedure of this code is as follows:(9)Gt1=11+t1−k1∗s∗h,Gtn=11+tn−kn∗s∗h.Because the number of program codes is large, it needs to be activated one by one and then activated as a whole. Its activation formula is expressed as follows:(10)GW1=11+w1−k∗i∗h,GWn=11+wn−K∗i∗h.Then, the formula for activation of all programming codes is expressed as follows:(11)GW=11+w1−k∗i+w2−k∗i+⋯+wn−k∗i.The information flow processed by a single neuron is as mentioned, but software development is difficult to complete by individual ability, so general software development is completed by teamwork. It uses artificial neural networks to combine individual software functions developed by all individuals on the team. It reasonably finds the inconsistency among the individuals of the team, thereby improving the efficiency of the entire R&D team. Its simple schematic diagram is shown in Figure2.Figure 2
Simple schematic diagram.As shown in Figure2, the members of each team are responsible for the software functions, and all program codes need to be monitored by the artificial neural network; that is, each team member needs to share information through the artificial neural network. Then, the formula for calculating the final code quantity Q through the artificial neural network is as follows:(12)Q=Q1+Q2+⋯+Qn∗k∗s∗ih.The powerful computing ability and problem-solving ability of artificial neural networks, as well as the strong sample identification and classification ability, make the process of software development and configuration more efficient. It can be found at any time during the development process whether there are software functions that are not set, so that they can be added in time to ensure that the originally set software functions are complete.


### 2.2. Software Development and Configuration Management

Software is the interdependent part of the computer system and hardware, which includes a complete set of programs, data, and related documents. Software development uses programming code to design software programs, data, and documents [10]. The program refers to the instruction program that needs to be executed for the functions and performance designed by the software before development; data are a data structure that allows programs in the software to use information normally; documents are data related to program development, maintenance, and software use [11]. The software development phase has three steps, namely design, coding, and testing. The development of software requires a lot of time, manpower, and material resources, because software development is generally jointly developed by multiple teams, that is, distributed collaborative development, and there are many types of arrangements between teams, one of which is distributed collaborative development. Its distributed team organizational structure is shown in Figure 3.Figure 3
Distributed team organizational structure.Software development team members generally include project managers, project leaders, software requirement analysts, software developers, and software testers. Software development companies distribute their teams in various places and conduct project development in several places at the same time, and each team conducts development in a targeted manner according to business needs [12]. Software development is a highly specialized project, and there are still a lot of problems in the current software development process. At the beginning of software development, requirement analysis is difficult due to unclear expressions by customers, frequent changes in the requirements themselves, or wrong understanding by analysts. This, in turn, may lead to poor analysis of the customer’s needs, resulting in the need for replanning and design in the later stage, wasting a lot of time and cost. Moreover in the development process, since there are multiple software development engineers, if they cannot get in touch and coordinate in real time, the team will waste manpower in the process of developing software and at the same time prolong the development cycle of the software. There are also testers who mainly test whether the interface of the module is correct and whether the output of the software is correct. However, problems will occur during the user’s trial.Software configuration items are generally the product of activities at various stages of the software life cycle. Software configuration management is to track the defects and changes that jeopardize software quality and affect the development cycle during the development process by managing and tracking the continuous changes and updates of software in the development and survival process, which enables the software to ensure the user’s experience in the process of being formally put into use [13]. The purpose of software configuration management is to ensure that various information materials, including code, data, and documents, are developed during the software’s entire life cycle, so that future software changes can be directly referenced [14]. The software configuration is shown in Figure 4.Figure 4
Software configuration.Software configuration management involves identifying the configuration of software in a timely manner at a given point in time, systematically controlling changes to the configuration, and maintaining configuration integrity and traceability throughout the software life cycle [15]. Work products placed under software configuration management include software products delivered to customers and product items identified with those software products or required to produce those software products. Software configuration management makes software maintenance and upgrades reliable and helps protect valuable code resources, accumulate software wealth, and improve software reuse. Just like WeChat and QQ in our mobile phones, they need to be updated every once in a while to improve the functions of the software [16].


### 2.3. Monitoring and Management of Software Development and Configuration by Artificial Neural Network

There are many problems in the development configuration of the software that are not easy to detect; for example, in the development of software programs, the development of programs involves complex and cumbersome codes, and there will be many difficult to detect errors in the middle, as shown in Figure5.Figure 5
Code.See Figure5 for a piece of code. There will definitely be some tiny errors in the code input process, such as letters or spaces, which will affect the final program execution. These tiny errors are sometimes difficult to detect, so we need to use an artificial neural networks to monitor the software development process, including the input of program code and the process of later data and documents, to ensure the accuracy of the data that the software needs to save in the end. The application of artificial neural networks in software development configuration can better identify errors in the design process, thereby improving the efficiency of software development and reducing unnecessary time costs [17]. In addition, software development is carried out by multiple people, so software has multiple functions. Just like our WeChat, in addition to being able to communicate in words, it can also send a series of functions such as a circle of friends and small videos, all of which need to be developed and designed by multiple Chen Xu personnel. In the process of software development, some software functions will inevitably be left without being noticed, so artificial neural networks are needed for effective identification, and this problem can be found at a faster speed. It needs timely feedback to avoid problems when using it. In software development, it is also necessary to consider the problem of customer information protection, because there are a large number of software packages on the market, and some of them are too eager for quick success, which causes customers to leak information in the process of using [18]. Therefore, it is necessary to use the artificial neural network to detect whether there is a protection program designed in the software development process to ensure the safety and quality of the software and provide users with a better experience. Only in this way we can occupy the market. Of course, the artificial neural network can connect the operations of various members of the software development in real time, and once one of the programmers discriminates with the code input of other programmers, it will give feedback in time. This requires the entire team to negotiate and adjust in a timely manner to ensure the unity of the development team, avoid later rework, and reduce unnecessary consumption of manpower and material resources, as shown in Figure 6.Figure 6
Monitoring recognition by artificial neural network.Software configuration is the core of software development environment management. If the configuration work is not done well, it will cause a series of problems, such as the general code cannot notify everyone in the team in time and the sharing of resources between teams is hindered. The use of artificial neural network for detection can just solve the problem of simultaneous update [19]. Because artificial neural networks can correlate with the work of all team developers, changes in one person’s software development process can damage the work of other team members. In a large-scale software system, large-scale software is developed in an incremental release mode, and multiple versions may be released. Therefore, there are many people who add error modifications and software upgrades, and it is very easy to cause version conflicts that cannot support multiple checkouts according to the situation of the code manager. Therefore, the application of artificial neural network to large-scale software systems for detection can well change this situation and avoid wasting a lot of manpower, material resources, and time [20].


## 2.1. Artificial Neural Network

The artificial neural network abstracts and simplifies the neurons of the human brain from the perspective of information processing in the human brain, so that it can establish a simple model similar to the processing of the human brain. It builds a type of simple model and forms different networks according to different connection methods. The model has the ability of self-adaptation, self-organization, and self-learning similar to the human brain and has practical effects in the fields of pattern recognition, combinatorial optimization, prediction, and prediction [8].The structure of a single neuron in the artificial neural network is shown in Figure1, and if the artificial neural network is applied to software development, then the artificial neural network can follow up the work of software development in real time and find errors in time for correction. Software development requires writing code, and there will be some errors in the process of inputting the code, which the developer cannot detect during the development process. Therefore, we need to add an artificial neural network model to the configuration to identify errors in the code input process. We need to understand the artificial neural network, and a series of algorithms need to be designed in the artificial neural network model [9]. In Figure 1, when the amount of information input to a single neuron is f, the information will be divided and processed for discrimination. Each information stream will have an input signal k, and the calculation form of the amount of information t that needs to be processed in each information stream is as follows:(1)t1=f∗k1∗s1,t2=12f∗k2∗s2−t1.Figure 1
Model structure of a single neuron.Among them,s is the processing time of each information flow. Because the amount of information that each information stream needs to process is not necessarily the same, the information processing time may also be different. Then, when there are n information streams, the amount of information that needs to be processed by the nth information stream is as follows:(2)tn=1nf∗kn+sn−tn−1.When the artificial neural network monitors the code developed by the software during the configuration, it will mark the code with an error during the input process in red according to the internal settings of the system and return to the original program, and a back check will appear. The number of code input errors isd, and then, the number of codes u for the entire return check is as follows:(3)u=1ntn∗su∗ku−tn−1∗k.Until all input software development codes are corrected, the next monitoring and management can be passed. For this reason, the final quantity input into the total node system needs to be calculated according to the specific amount of data output by a single node, that is, a single information flow to the total node as follows:(4)W1=∑n=1k∗t1∗i1f,W2=∑n=2k∗t2∗i2f−W1.Then, the number of thenth data stream input to the summary point is as follows:(5)Wn=∑nk∗tn∗inf−W1.Since the amount of information processed by each information flow has been obtained, the formula for calculating the amount of informationW input into the summary point is as follows:(6)W=W1+W2+⋯+Wnn∗∑nk∗tn∗inf∗ℓ.Among them,ℓ represents a parallel matrix, and its form is as follows:(7)ℓ=tnknksnini.In the summary point, the input programming code is uniformly monitored and managed to monitor whether the software developed has originally set functions. If the originally set function is missing, the artificial neural network will automatically respond, and then, a prompt will appear for the developer to make an effective reference; if the originally set software function is not omitted, the code in the program will continue to enter the node of the activation function. The form of the activation function is as follows:(8)Ge=11+e−k∗s.When the programming code developed by the software is imported into this node, the programming code needs to be activated and a thresholdh will appear on this node. Under the action of this activation function, the activation procedure of this code is as follows:(9)Gt1=11+t1−k1∗s∗h,Gtn=11+tn−kn∗s∗h.Because the number of program codes is large, it needs to be activated one by one and then activated as a whole. Its activation formula is expressed as follows:(10)GW1=11+w1−k∗i∗h,GWn=11+wn−K∗i∗h.Then, the formula for activation of all programming codes is expressed as follows:(11)GW=11+w1−k∗i+w2−k∗i+⋯+wn−k∗i.The information flow processed by a single neuron is as mentioned, but software development is difficult to complete by individual ability, so general software development is completed by teamwork. It uses artificial neural networks to combine individual software functions developed by all individuals on the team. It reasonably finds the inconsistency among the individuals of the team, thereby improving the efficiency of the entire R&D team. Its simple schematic diagram is shown in Figure2.Figure 2
Simple schematic diagram.As shown in Figure2, the members of each team are responsible for the software functions, and all program codes need to be monitored by the artificial neural network; that is, each team member needs to share information through the artificial neural network. Then, the formula for calculating the final code quantity Q through the artificial neural network is as follows:(12)Q=Q1+Q2+⋯+Qn∗k∗s∗ih.The powerful computing ability and problem-solving ability of artificial neural networks, as well as the strong sample identification and classification ability, make the process of software development and configuration more efficient. It can be found at any time during the development process whether there are software functions that are not set, so that they can be added in time to ensure that the originally set software functions are complete.


## 2.2. Software Development and Configuration Management

Software is the interdependent part of the computer system and hardware, which includes a complete set of programs, data, and related documents. Software development uses programming code to design software programs, data, and documents [10]. The program refers to the instruction program that needs to be executed for the functions and performance designed by the software before development; data are a data structure that allows programs in the software to use information normally; documents are data related to program development, maintenance, and software use [11]. The software development phase has three steps, namely design, coding, and testing. The development of software requires a lot of time, manpower, and material resources, because software development is generally jointly developed by multiple teams, that is, distributed collaborative development, and there are many types of arrangements between teams, one of which is distributed collaborative development. Its distributed team organizational structure is shown in Figure 3.Figure 3
Distributed team organizational structure.Software development team members generally include project managers, project leaders, software requirement analysts, software developers, and software testers. Software development companies distribute their teams in various places and conduct project development in several places at the same time, and each team conducts development in a targeted manner according to business needs [12]. Software development is a highly specialized project, and there are still a lot of problems in the current software development process. At the beginning of software development, requirement analysis is difficult due to unclear expressions by customers, frequent changes in the requirements themselves, or wrong understanding by analysts. This, in turn, may lead to poor analysis of the customer’s needs, resulting in the need for replanning and design in the later stage, wasting a lot of time and cost. Moreover in the development process, since there are multiple software development engineers, if they cannot get in touch and coordinate in real time, the team will waste manpower in the process of developing software and at the same time prolong the development cycle of the software. There are also testers who mainly test whether the interface of the module is correct and whether the output of the software is correct. However, problems will occur during the user’s trial.Software configuration items are generally the product of activities at various stages of the software life cycle. Software configuration management is to track the defects and changes that jeopardize software quality and affect the development cycle during the development process by managing and tracking the continuous changes and updates of software in the development and survival process, which enables the software to ensure the user’s experience in the process of being formally put into use [13]. The purpose of software configuration management is to ensure that various information materials, including code, data, and documents, are developed during the software’s entire life cycle, so that future software changes can be directly referenced [14]. The software configuration is shown in Figure 4.Figure 4
Software configuration.Software configuration management involves identifying the configuration of software in a timely manner at a given point in time, systematically controlling changes to the configuration, and maintaining configuration integrity and traceability throughout the software life cycle [15]. Work products placed under software configuration management include software products delivered to customers and product items identified with those software products or required to produce those software products. Software configuration management makes software maintenance and upgrades reliable and helps protect valuable code resources, accumulate software wealth, and improve software reuse. Just like WeChat and QQ in our mobile phones, they need to be updated every once in a while to improve the functions of the software [16].


## 2.3. Monitoring and Management of Software Development and Configuration by Artificial Neural Network

There are many problems in the development configuration of the software that are not easy to detect; for example, in the development of software programs, the development of programs involves complex and cumbersome codes, and there will be many difficult to detect errors in the middle, as shown in Figure5.Figure 5
Code.See Figure5 for a piece of code. There will definitely be some tiny errors in the code input process, such as letters or spaces, which will affect the final program execution. These tiny errors are sometimes difficult to detect, so we need to use an artificial neural networks to monitor the software development process, including the input of program code and the process of later data and documents, to ensure the accuracy of the data that the software needs to save in the end. The application of artificial neural networks in software development configuration can better identify errors in the design process, thereby improving the efficiency of software development and reducing unnecessary time costs [17]. In addition, software development is carried out by multiple people, so software has multiple functions. Just like our WeChat, in addition to being able to communicate in words, it can also send a series of functions such as a circle of friends and small videos, all of which need to be developed and designed by multiple Chen Xu personnel. In the process of software development, some software functions will inevitably be left without being noticed, so artificial neural networks are needed for effective identification, and this problem can be found at a faster speed. It needs timely feedback to avoid problems when using it. In software development, it is also necessary to consider the problem of customer information protection, because there are a large number of software packages on the market, and some of them are too eager for quick success, which causes customers to leak information in the process of using [18]. Therefore, it is necessary to use the artificial neural network to detect whether there is a protection program designed in the software development process to ensure the safety and quality of the software and provide users with a better experience. Only in this way we can occupy the market. Of course, the artificial neural network can connect the operations of various members of the software development in real time, and once one of the programmers discriminates with the code input of other programmers, it will give feedback in time. This requires the entire team to negotiate and adjust in a timely manner to ensure the unity of the development team, avoid later rework, and reduce unnecessary consumption of manpower and material resources, as shown in Figure 6.Figure 6
Monitoring recognition by artificial neural network.Software configuration is the core of software development environment management. If the configuration work is not done well, it will cause a series of problems, such as the general code cannot notify everyone in the team in time and the sharing of resources between teams is hindered. The use of artificial neural network for detection can just solve the problem of simultaneous update [19]. Because artificial neural networks can correlate with the work of all team developers, changes in one person’s software development process can damage the work of other team members. In a large-scale software system, large-scale software is developed in an incremental release mode, and multiple versions may be released. Therefore, there are many people who add error modifications and software upgrades, and it is very easy to cause version conflicts that cannot support multiple checkouts according to the situation of the code manager. Therefore, the application of artificial neural network to large-scale software systems for detection can well change this situation and avoid wasting a lot of manpower, material resources, and time [20].


## 3. Experiment and Analysis of Monitoring Management of Software Development Configuration

### 3.1. The Experiment of Artificial Neural Network to Improve the Efficiency of Software Development

This experiment will invite a software development team from a company to help us complete the experiment. The software to be developed is an English-related software. There are four versions, namely scallop listening, scallop words, scallop speaking, and scallop reading. The software functions of the scallop series are shown in Table1.Table 1
Scallop series software features.
VersionListeningWordReadingOralUnique functionMainly to play English listeningRecite words online and look them upEnglish novel reading, geographic journal, newsRead after English and practice oral EnglishCommon functionsCan look up new words, the system has the same English dictionary insideOther settingsHelp and customer service, security and privacy, course recommendedIn Table1, it can be seen that each version has its own specific function, which means that each version has its own program code, and each version also has common functions and software settings, which can be done by general program code. To this end, we also investigated and recorded how much code input the team members could complete on a daily and weekly basis. The results are shown in Table 2.Table 2
Member code input amount.
MembersDaily input quantityWeekly input quantityA54189B46167C57232D56211Average53.25199.75The weekly input volume for the codes in Table2 is based on the team’s working day, which is five days of work. It is obvious that the weekly input is not calculated by multiplying the daily input by the working days of the week. Because in the process of code input, due to the different input speeds among team members, there will be inconsistencies in the development process of the team. Therefore, the team needs to communicate and correct all program codes to ensure that the functions of the software will not conflict or intersect. At the same time, due to the lack of effective code management, general code cannot be found and alerted to every programmer. Therefore, we refer the artificial neural network to the software development team, so that the work of each programmer is unified with other programmers, and the work of each programmer is as effective as possible. This avoids the work of the team being disconnected due to the fact that some members work slower and some members work more efficiently and ensures that the work of team members is effective. The code input of team members after the introduction of artificial neural network is shown in Figure 7.Figure 7
Code input comparison chart. (a) Daily input. (b) Weekly input.
(a)(b)From Figure7, after the introduction of the artificial neural network in (a), the daily code input of the four programmers has increased, and the speed is about 20% faster than before. Without the introduction of artificial neural network, the code input of these four programmers is basically about 50 lines, but after the introduction of artificial neural network, their code input is basically more than 60 lines. The weekly input volume of the graph (b) is also greatly improved, which shows that the artificial neural network can coordinate the work among team members, strengthen the collaboration ability between members, greatly improve work efficiency, and save time costs.


### 3.2. Verification of Software Problem Correction Speed

Software problems mainly refer to software problems found in module testing, integration testing, and system testing. This experiment is to measure the speed of software problem repair after the introduction of artificial neural network. To this end, we invited another software development team from this company to participate in the development of the Scallop series software. These two teams are named Team 1 and Team 2. Team 2 will introduce artificial neural networks into the software development process, while Team 2 will employ traditional software development procedures. As the two teams develop the Scallop software system, we keep track of how quickly the teams are fixing bugs in the software. The team’s usual change in the defect rate of the development software is shown in Table3.Table 3
Changes in Team 1 DI values.
DaysBefore introducing configuration managementAfter introducing configuration management05674341043436720374.5289.530267.4234.540194.61785094.5836046.736.7Note. DI = the number of fatal-level problems  ∗ 10 + the number of serious-level problems  ∗ 3 + the number of general-level problems  ∗ 1 + the number of prompt-level problems ∗ 0.1 [21].In Table3, the smaller the DI value, the higher the software quality, so DI is one of the indicators to measure the software quality [22]. Among them, Table 3 records the change in the defect rate before and after the introduction of configuration management by Team 1. It can be seen that after the introduction of configuration management, the rate of defect rate decline is significantly faster than before the introduction, so software configuration management is essential. What Team 1 needed to document was the speed of scallop software revisions after the introduction of configuration management. To this end, we recorded the data of the two teams for comparison, as shown in Figure 8.Figure 8
Comparison of ID changes. (a) Scallop listening software and (b) Scallop word software.
(a)(b)From Figure8, it can be seen that the defect rate of Team 1 is 140% at the beginning of testing software defects, while the defect rate of Team 2 is 120%. From the point of view of software development, under the monitoring and management of the artificial neural network, the quality of the software developed by the second team is slightly better than that of the software developed by the first team. On the whole, the software defect rate of Team 2 dropped significantly faster than that of Team 1. We also recorded the development time of the scallop series software by the two teams, and the comparison chart is shown in Figure 9.Figure 9
Comparison of software development time.From Figure9, the software development speed of Team 2 is clearly faster than that of Team 1. In terms of the development time of the hearing software, the second team only needs about 50% of the time of the first team, so the artificial neural network can improve the efficiency of software development.


### 3.3. Experimental Summary

Through the comparison of the daily and weekly input of code, the artificial neural network can promote the efficiency and accuracy of code input of programmers in the software development team and can also help save time and cost and improve the speed of software development. With the help of artificial neural networks, the speed of software development has been increased by at least about 20%. From the perspective of the development speed of the system software, its speed is at least 50% higher than that in the past. Judging from the defect rate and the rate of decline of the software developed by Team 1 and Team 2, it is obvious that the software developed under the monitoring and management of artificial neural networks is of higher quality.


## 3.1. The Experiment of Artificial Neural Network to Improve the Efficiency of Software Development

This experiment will invite a software development team from a company to help us complete the experiment. The software to be developed is an English-related software. There are four versions, namely scallop listening, scallop words, scallop speaking, and scallop reading. The software functions of the scallop series are shown in Table1.Table 1
Scallop series software features.
VersionListeningWordReadingOralUnique functionMainly to play English listeningRecite words online and look them upEnglish novel reading, geographic journal, newsRead after English and practice oral EnglishCommon functionsCan look up new words, the system has the same English dictionary insideOther settingsHelp and customer service, security and privacy, course recommendedIn Table1, it can be seen that each version has its own specific function, which means that each version has its own program code, and each version also has common functions and software settings, which can be done by general program code. To this end, we also investigated and recorded how much code input the team members could complete on a daily and weekly basis. The results are shown in Table 2.Table 2
Member code input amount.
MembersDaily input quantityWeekly input quantityA54189B46167C57232D56211Average53.25199.75The weekly input volume for the codes in Table2 is based on the team’s working day, which is five days of work. It is obvious that the weekly input is not calculated by multiplying the daily input by the working days of the week. Because in the process of code input, due to the different input speeds among team members, there will be inconsistencies in the development process of the team. Therefore, the team needs to communicate and correct all program codes to ensure that the functions of the software will not conflict or intersect. At the same time, due to the lack of effective code management, general code cannot be found and alerted to every programmer. Therefore, we refer the artificial neural network to the software development team, so that the work of each programmer is unified with other programmers, and the work of each programmer is as effective as possible. This avoids the work of the team being disconnected due to the fact that some members work slower and some members work more efficiently and ensures that the work of team members is effective. The code input of team members after the introduction of artificial neural network is shown in Figure 7.Figure 7
Code input comparison chart. (a) Daily input. (b) Weekly input.
(a)(b)From Figure7, after the introduction of the artificial neural network in (a), the daily code input of the four programmers has increased, and the speed is about 20% faster than before. Without the introduction of artificial neural network, the code input of these four programmers is basically about 50 lines, but after the introduction of artificial neural network, their code input is basically more than 60 lines. The weekly input volume of the graph (b) is also greatly improved, which shows that the artificial neural network can coordinate the work among team members, strengthen the collaboration ability between members, greatly improve work efficiency, and save time costs.


## 3.2. Verification of Software Problem Correction Speed

Software problems mainly refer to software problems found in module testing, integration testing, and system testing. This experiment is to measure the speed of software problem repair after the introduction of artificial neural network. To this end, we invited another software development team from this company to participate in the development of the Scallop series software. These two teams are named Team 1 and Team 2. Team 2 will introduce artificial neural networks into the software development process, while Team 2 will employ traditional software development procedures. As the two teams develop the Scallop software system, we keep track of how quickly the teams are fixing bugs in the software. The team’s usual change in the defect rate of the development software is shown in Table3.Table 3
Changes in Team 1 DI values.
DaysBefore introducing configuration managementAfter introducing configuration management05674341043436720374.5289.530267.4234.540194.61785094.5836046.736.7Note. DI = the number of fatal-level problems  ∗ 10 + the number of serious-level problems  ∗ 3 + the number of general-level problems  ∗ 1 + the number of prompt-level problems ∗ 0.1 [21].In Table3, the smaller the DI value, the higher the software quality, so DI is one of the indicators to measure the software quality [22]. Among them, Table 3 records the change in the defect rate before and after the introduction of configuration management by Team 1. It can be seen that after the introduction of configuration management, the rate of defect rate decline is significantly faster than before the introduction, so software configuration management is essential. What Team 1 needed to document was the speed of scallop software revisions after the introduction of configuration management. To this end, we recorded the data of the two teams for comparison, as shown in Figure 8.Figure 8
Comparison of ID changes. (a) Scallop listening software and (b) Scallop word software.
(a)(b)From Figure8, it can be seen that the defect rate of Team 1 is 140% at the beginning of testing software defects, while the defect rate of Team 2 is 120%. From the point of view of software development, under the monitoring and management of the artificial neural network, the quality of the software developed by the second team is slightly better than that of the software developed by the first team. On the whole, the software defect rate of Team 2 dropped significantly faster than that of Team 1. We also recorded the development time of the scallop series software by the two teams, and the comparison chart is shown in Figure 9.Figure 9
Comparison of software development time.From Figure9, the software development speed of Team 2 is clearly faster than that of Team 1. In terms of the development time of the hearing software, the second team only needs about 50% of the time of the first team, so the artificial neural network can improve the efficiency of software development.


## 3.3. Experimental Summary

Through the comparison of the daily and weekly input of code, the artificial neural network can promote the efficiency and accuracy of code input of programmers in the software development team and can also help save time and cost and improve the speed of software development. With the help of artificial neural networks, the speed of software development has been increased by at least about 20%. From the perspective of the development speed of the system software, its speed is at least 50% higher than that in the past. Judging from the defect rate and the rate of decline of the software developed by Team 1 and Team 2, it is obvious that the software developed under the monitoring and management of artificial neural networks is of higher quality.


## 4. Discussion

The artificial neural network discussed in this study is an emerging field of artificial intelligence. It is an abstract and simple model of human brain neurons, which can be used in various fields to help identify errors, just like the human brain to identify errors in the program, but the recognition rate is higher than the human brain [23]. In some meticulous fields, it has powerful computing power and intelligent features, such as input errors of program code in software development. Introducing artificial neural networks into software development configuration is a new reference method, which can provide a better environment for software development configuration to a certain extent. At present, there are a lot of problems in software development configuration, and due to the different tasks assigned by the software development team members, errors in the programming code in the development work may occur. This requires the artificial neural network to instantly share the work progress and work process among team members, to improve the working environment of the entire team. This study understands the software development and configuration process, especially for the problems of time, manpower, and capital costs caused by the inability of traditional monitoring and management systems to unify the work among team members in development and configuration. These problems are the key to the survival of software companies and how to develop high-quality software at low cost, and it is necessary to solve the problem of inadequate monitoring and management. Artificial neural network has a good effect on solving this problem. The artificial neural network is composed of multiple neurons, which can monitor and manage the work of all team members at the same time, promote the coordination of the entire teamwork, enable the teamwork to be managed uniformly, and improve the inconsistency of each member in software development.The monitoring and management ability of artificial neural network in software development configuration is also verified by experiments. It is found that artificial neural networks can not only promote the efficiency of software development but also promote the coordination of teamwork and save a lot of development costs. Artificial neural networks can be applied not only to software development but also to other similar fields, especially projects that require a lot of code. Applying it to code inspection can improve efficiency and save a lot of time.


## 5. Conclusions

In this study, the artificial neural network has been deeply discussed, and its powerful computing power and problem processing ability can be used in many fields, such as medical treatment and artificial intelligence. This study mainly studies its problems in solving software development and configuration. The existence of such a large number of problems in the software development configuration can lead to increased costs. If the software development configuration is not in place, the poor user experience will eventually affect the company’s interests, but with the help of artificial neural networks, the problems in software development configuration can be effectively solved and the quality of the software can be improved. The experiments in this study show that the artificial neural network can not only improve the efficiency of software development but also effectively improve the quality of the developed software, which fundamentally reduces the cost. The research in this study has great practical value for the software development industry. Of course, the research in this study is only applicable to the development of large-scale software systems and has certain limitations for small-scale software development.



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*Source: 9122908-2022-04-14.xml*