CN112053049A - Neural network model-based software project required resource balancing method - Google Patents
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Abstract
The invention discloses a method for balancing resources required by a software project based on a neural network model, which relates to the field of software engineering.
Description
Technical Field
The invention relates to the field of software engineering, in particular to a method for balancing resources required by a software project based on a neural network model.
Background
With the development of electronic information technology, large software projects become more complex, the management difficulty of the software projects is increased, and problems such as project delay, cost rise and the like are easy to occur. Therefore, the management effect of the software project is improved, the internal resources of an enterprise are effectively utilized, and the efficiency of the software project is an important way for improving the competitiveness of the enterprise. Therefore, to develop such a software project, in many cases, users do not give explicit ideas from the beginning and do not put exact requirements. For such intangible software projects with definite practical purposes, the quantity of resources and time cost required to be invested in the software projects are difficult to calculate by definite formula algorithms, most of the software projects can be judged only by experience, and whether the judgment is accurate or not is completely dependent on people, so that the inaccurate prediction result caused by incomplete artificial consideration can be caused, the resources required by the software projects can not be effectively balanced, and enterprises can not effectively utilize the internal resources.
Disclosure of Invention
In order to solve the defects of the prior art, the embodiment of the invention provides a method for balancing resources required by a software project based on a neural network model, which comprises the following steps:
inputting expected effect data of a software project into a trained neural network model to obtain the quantity of resources and time cost required by the software project;
analyzing the resources required by the software project according to the quantity of the resources required by the software project and the time cost and combining the existing conditions, and judging whether resource balance is required or not;
and if so, balancing the resources required by the software project by using a time optimal balancing method or a resource optimal balancing method.
Preferably, analyzing the resources required by the software project according to the amount of the resources required by the software project and the time cost, and determining whether resource balancing is required includes:
and judging whether the time cost and the resource quantity which can be invested into the software project are both larger than or equal to the time cost and the resource quantity required by the software project, if so, determining that the software project does not need to be subjected to resource balance, and if not, determining that the software project needs to be subjected to resource balance.
Preferably, the creation process of the trained neural network model includes:
acquiring historical effect data and historical demand data of a plurality of software projects, and generating an experience data set;
and inputting the empirical data set serving as training data into a neural network model to obtain the trained neural network model.
Preferably, the method further comprises:
after the software project is completed, calculating an actual value of resource data required by the software project;
and comparing the actual values of the resource data required by the software project and the resource data required by the software project, which are obtained by calculating the neural network model, one by one, respectively judging whether the error between each piece of resource data required by the software project and each piece of actual resource data required by the software project is smaller than a preset numerical value, if not, taking each piece of actual resource data required by the software project as training data, and training the model neural network model again.
Preferably, the effect data of the item of software comprises:
the software project cost is calculated according to the software project cost, the software project difficulty requirement quantity, the software project general requirement quantity, the requirement fuzzy degree, the software project predicted user quantity, whether the software project is a substitute of an existing product or a new product, the software project predicted service life, the software project confidentiality grade requirement, the integrated system quantity and the software product predicted single-day average data volume.
Preferably, the requirement data of the software project comprises:
the resource data required by the software project comprises the predicted profit of the software project, the predicted technical expert investment of the software project, the predicted high-level development engineer investment of the software project, the predicted intermediate-level development engineer investment of the software project, the predicted planner investment of the software project, the predicted implementation engineer investment of the software project, the predicted test engineer investment of the software project and the predicted other personnel investment of the software project.
Preferably, the neural network model is a BP neural network model.
The method for balancing resources required by the software project based on the neural network model, provided by the embodiment of the invention, has the following beneficial effects:
the trained neural network model is used for predicting the quantity of resources and time cost required by the software project, and the resources required by the software project are effectively balanced according to the prediction result, so that the prediction accuracy is improved, and enterprises can effectively utilize the internal resources.
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FIG. 1 is a schematic flow chart of a method for balancing resources required by a software project based on a neural network model according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an internal structure of a neural network model according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the embodiments.
As shown in fig. 1, the method for balancing resources required by a software project based on a neural network model provided by the embodiment of the present invention includes the following steps:
and S101, inputting expected effect data of the software project into the trained neural network model to obtain the quantity of resources required by the software project and the time cost.
S102, analyzing the resources required by the software project according to the quantity of the resources required by the software project and the time cost by combining the existing conditions, and judging whether resource balance is required or not;
and S103, if so, balancing the resources required by the software project by using a time optimal balancing method or a resource optimal balancing method.
Optionally, analyzing the resources required by the software project according to the amount of the resources required by the software project and the time cost, and determining whether resource balancing is required includes:
and judging whether the time cost and the resource quantity which can be input into the software project are both larger than or equal to the time cost and the resource quantity required by the software project, if so, determining that the software project does not need to be subjected to resource balance, and if not, determining that the software project needs to be subjected to resource balance.
Optionally, the creating process of the trained neural network model includes:
acquiring historical effect data and historical demand data of a plurality of software projects, and generating an experience data set;
and inputting the empirical data set serving as training data into the neural network model to obtain the trained neural network model.
As a specific example, as shown in fig. 2, the BP neural network model is a supervised machine learning model, and its learning rule is an error correction algorithm, i.e. it is modified according to the neural network output error and the neuron connection strength, and its weight adjustment uses a back propagation algorithm. Wherein, the BP neural network model comprises three parts of an input layer, a hidden layer and an output layer, x1、x2…xmFor software project historical effects data, y1、y2…ynAnd historical demand data corresponding to the software project. The total price of the software project, the quantity of the hard points of the software project, the quantity of the general requirements of the software project and the softwareThe fuzzy degree of project requirements, the predicted number of users of the software project, whether the software project is a substitute of an existing product or a new product, the predicted service life of the software project, the product confidentiality level requirement, the number of integrated systems and the predicted single-day average data volume of the software project are used as input layers; and taking the actual net profit of the software project, the actual technical expert investment time of the software project, the actual high-level development engineer investment time of the software project, the actual medium-level development engineer investment time of the software project, the actual planner investment time of the software project, the actual implementation engineer investment time of the software project, the actual test engineer investment time of the software project and the actual other personnel investment time of the software project as output layers. All input layers are set to be forward-related, an empirical data set is used, and the BP neural network model processing process after input signals are given is as follows: the input signal enters from the input layer and is transmitted to the hidden layer, after the input signal is processed by the hidden layer unit, the signal obtains an output layer signal through an activation function, if the output layer node cannot obtain expected output, namely the error is greater than a set error, the error is propagated reversely, the output error is returned to the hidden layer and the input layer in sequence by the output layer according to a certain form, and the weight is modified according to the error signal. And continuously carrying out the forward propagation process and the reverse propagation process of the error of the signal until the error of the neural network model is reduced to be within a set error, and obtaining the trained neural network model, wherein the weight is a value on two node paths of the BP neural network model, and the value represents the cost, such as the length of a path from one node to the other node, the time spent, the cost paid by the node and the like.
Optionally, the method further comprises:
after the software project is finished, calculating an actual value of resource data required by the software project;
and comparing the actual values of the resource data required by the software project and the resource data required by the software project, which are obtained by calculating the neural network model, one by one, respectively judging whether the error between each piece of resource data required by the software project and each piece of actual resource data required by the software project is smaller than a preset numerical value, if not, taking each piece of actual resource data required by the software project as training data, and training the model neural network model again.
Optionally, the effect data of the software project comprises:
the software project cost is calculated according to the software project cost, the software project difficulty requirement quantity, the software project general requirement quantity, the requirement fuzzy degree, the software project predicted user quantity, whether the software project is a substitute of an existing product or a new product, the software project predicted service life, the software project confidentiality grade requirement, the integrated system quantity and the software product predicted single-day average data volume.
The required fuzzy degrees are divided into four types, namely definite, relatively definite, fuzzy and very fuzzy. The purpose and the implementation mode are clearly explained, namely the requirement of the user is clearly stated; the use is clearly explained according to the requirements provided by the user, and the implementation mode is visual; the application is clearly explained by the requirements of the user, the implementation mode is not intuitive, and repeated modification is possible; the fuzzy meaning that the requirement of the user only explains the general purpose, the realization mode is not clear, and the repeated modification is possible.
Optionally, the requirement data of the software project includes:
the resource data required by the software project comprises the predicted profit of the software project, the predicted technical expert investment of the software project, the predicted high-level development engineer investment of the software project, the predicted intermediate-level development engineer investment of the software project, the predicted planner investment of the software project, the predicted implementation engineer investment of the software project, the predicted test engineer investment of the software project and the predicted other personnel investment of the software project.
Optionally, the neural network model is a BP neural network model.
According to the method for balancing the resources required by the software project based on the neural network model, the quantity of the resources and the time cost required by the software project are obtained by inputting the expected effect data of the software project into the trained neural network model, the resources required by the software project are analyzed according to the quantity of the resources and the time cost required by the software project and by combining the existing conditions, whether resource balancing is required is judged, if yes, the resources required by the software project are balanced by using a time optimal balancing method or a resource optimal balancing method, the resources required by the software project are effectively balanced, the prediction accuracy is improved, and enterprises can effectively utilize the internal resources.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It will be appreciated that the relevant features of the method and apparatus described above are referred to one another.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In addition, the memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (7)
1. A method for balancing resources required by a software project based on a neural network model is characterized by comprising the following steps:
inputting expected effect data of a software project into a trained neural network model to obtain the quantity of resources and time cost required by the software project;
analyzing the resources required by the software project according to the quantity of the resources required by the software project and the time cost and combining the existing conditions, and judging whether resource balance is required or not;
and if so, balancing the resources required by the software project by using a time optimal balancing method or a resource optimal balancing method.
2. The method of claim 1, wherein analyzing the resources required by the software project according to the amount of resources required by the software project and the time cost, in combination with existing conditions, and determining whether resource balancing is required comprises:
and judging whether the time cost and the resource quantity which can be invested into the software project are both larger than or equal to the time cost and the resource quantity required by the software project, if so, determining that the software project does not need to be subjected to resource balance, and if not, determining that the software project needs to be subjected to resource balance.
3. The method of claim 1, wherein the creation of the trained neural network model comprises:
acquiring historical effect data and historical demand data of a plurality of software projects, and generating an experience data set;
and inputting the empirical data set serving as training data into a neural network model to obtain the trained neural network model.
4. The method of claim 1, wherein the method further comprises:
after the software project is completed, calculating an actual value of resource data required by the software project;
and comparing the actual values of the resource data required by the software project and the resource data required by the software project, which are obtained by calculating the neural network model, one by one, respectively judging whether the error between each piece of resource data required by the software project and each piece of actual resource data required by the software project is smaller than a preset numerical value, if not, taking each piece of actual resource data required by the software project as training data, and training the model neural network model again.
5. The method of claim 1, wherein the effect data of the software project comprises:
the software project cost is calculated according to the software project cost, the software project difficulty requirement quantity, the software project general requirement quantity, the requirement fuzzy degree, the software project predicted user quantity, whether the software project is a substitute of an existing product or a new product, the software project predicted service life, the software project confidentiality grade requirement, the integrated system quantity and the software product predicted single-day average data volume.
6. The method of claim 1, wherein the demand data of the software project comprises:
the resource data required by the software project comprises the predicted profit of the software project, the predicted technical expert investment of the software project, the predicted high-level development engineer investment of the software project, the predicted intermediate-level development engineer investment of the software project, the predicted planner investment of the software project, the predicted implementation engineer investment of the software project, the predicted test engineer investment of the software project and the predicted other personnel investment of the software project.
7. The method of claim 1, wherein the neural network model is a BP neural network model.
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| CN101561904A (en) * | 2009-05-12 | 2009-10-21 | 中国科学院软件研究所 | Process data-based method and system for determining cost of software project |
| CN111191871A (en) * | 2019-11-21 | 2020-05-22 | 深圳壹账通智能科技有限公司 | Project baseline data generation method and device, computer equipment and storage medium |
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| CN101561904A (en) * | 2009-05-12 | 2009-10-21 | 中国科学院软件研究所 | Process data-based method and system for determining cost of software project |
| CN111191871A (en) * | 2019-11-21 | 2020-05-22 | 深圳壹账通智能科技有限公司 | Project baseline data generation method and device, computer equipment and storage medium |
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Application publication date: 20201208 |