Man-hour investment plan generation system and man-hour investment plan generation method

Abstract

To provide a man-hour investment plan generation system and a man-hour investment plan generation method which predict total man-hours by using time-series amounts of man-hour investment in which amounts of man-hour investment and their investment periods are associated with each other.SOLUTION: The man-hour investment plan generation system comprises an arithmetic unit for executing prescribed arithmetic processing (project total man-hours and quality prediction simulation system 10) and a storage device storing therein a program and connected to the arithmetic unit. The arithmetic unit includes a man-hour investment plan input unit which receives input of a man-hour investment plan for a project, an analysis unit which analyzes the man-hour investment plan inputted to the arithmetic unit, and an output unit which outputs an analysis result of the analysis unit. The man-hour investment plan input unit receives input of time-series data in which amounts of man-hour investment and their investment periods are associated with each other as the man-hour investment plan.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a technique for monitoring a man-hour input plan generation system, particularly for predicting the total man-hours and quality of a project.

Background Art

[0002] In a software development project, a project planner plans in advance the man-hours to be invested in the project. Ideally, by investing sufficient man-hours, the quality of the deliverable is improved, and the total man-hours can be reduced by reducing the rework man-hours. However, in reality, the human resources of the organization are limited, and there may be cases where sufficient man-hours cannot be invested. In that case, the project planner needs to set a man-hour input plan that meets the compromised quality and total man-hours within the scope of the organization's human resources. Therefore, the project planner predicts the quality and total man-hours from the man-hour input plan set within the scope of the organization's human resources, and determines whether the predicted quality and total man-hours are acceptable. If this is not acceptable, the man-hour input plan is set again within the scope of the organization's human resources, the quality and total man-hours are predicted, and it is determined whether the predicted quality and total man-hours are acceptable. This operation is repeated until the prediction results of the quality and total man-hours become acceptable.

[0003] As background art in this technical field, there is the following prior art. In a plant construction work support device for assisting in creating a plan for construction work or renewal work of a plant composed of a plurality of components constructed in a construction area, Patent Document 1 (Japanese Patent Laid-Open No. 2011-170496) discloses a database means for accumulating a plurality of performance data of past works, an input means for inputting the type and construction site of the construction target that is the same as or similar to the planned work, a performance data extraction means for extracting from the database means the performance data of past works corresponding to the type and construction site of the construction target input by the input means, and a plan creation means for creating a process plan table in which the work content of the planned work and its chronological order are set using the performance data extracted by the performance data extraction means.

Prior Art Documents

[0004] [Patent Document 1] Japanese Patent Publication No. 2011-170496 [Overview of the project] [Problems that the invention aims to solve]

[0005] Patent Document 1 predicts the total man-hours for a new construction project based on performance data from past construction projects and creates a project schedule. However, when applying this to a software development project, there is room to improve the accuracy of the total man-hour prediction. This is because, in a software development project, spending a lot of man-hours in upstream processes such as requirements analysis and basic design can suppress the amount of bugs introduced later and reduce the number of rework cycles in later processes, thereby reducing the total man-hours. In other words, the time-series man-hour input, which correlates the amount of man-hours invested with the timing of that input, affects the total man-hours. However, Patent Document 1 does not use the time-series man-hour input to predict the total man-hours, so there is room for improvement in accuracy in this respect.

[0006] Therefore, in this invention, a time-series data of man-hour input, which associates the amount of man-hour input with the timing of its input, is accepted as input, and at least one of the total man-hours and quality is predicted using the time-series data of man-hour input. [Means for solving the problem]

[0007] A typical example of the invention disclosed in this application is as follows: a man-hour input plan generation system comprising a computing device that performs predetermined calculation processing, and a storage device that stores the program and is connected to the computing device, wherein the computing device has an input unit that receives input of a man-hour input plan for a project, an analysis unit that analyzes the input man-hour input plan, and an output unit that outputs the analysis results from the analysis unit, and the input unit is characterized in that it receives input of time-series data relating the amount of man-hours to be used and the timing of its use as the man-hour input plan. [Effects of the Invention]

[0008] According to one aspect of the present invention, the accuracy of predicting total man-hours and quality can be improved. Problems, configurations, and effects other than those described above will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows an example of the processing flow and display of the project total man-hour and quality prediction simulation system of Example 1. [Figure 2] This figure shows an example of the man-hour input data for Example 1. [Figure 3] This figure shows an example of characteristic data for Example 1. [Figure 4] This figure shows an example of milestone data for Example 1. [Figure 5] This figure shows an example of past data for Example 1. [Figure 6] This figure shows an example of past data for Example 1. [Figure 7] This figure shows an example of past data for Example 1. [Figure 8] This figure shows an example of past data for Example 1. [Figure 9] This figure shows an example of the data held by the analysis unit of Example 1. [Figure 10] This figure shows an example of the data held by the analysis unit of Example 1. [Figure 11] This figure shows an example of the data displayed by the output unit of Example 1. [Figure 12] This is a flowchart of the process for calculating the ideal effort input plan for the target project in Example 1. [Figure 13] This is a flowchart of the process for calculating the future total man-hours, number of bugs, productivity, bug density, and development period for the original man-hour input plan in Example 1. [Figure 14]It is a flowchart of a process for calculating the future total man-hours, number of bugs, productivity, bug density, and development period in the ideal man-hour input plan of Example 1. [Figure 15] It is a flowchart of a process for calculating the future total man-hours, number of bugs, productivity, bug density, and development period in the man-hour input plan of the simulation of the project of Example 1. [Figure 16] It is a diagram showing an example of the "man-hour input pattern" of Example 1.

Mode for Carrying Out the Invention

[0010] Hereinafter, examples will be described with reference to the drawings. Note that the examples described below do not limit the invention according to the claims. Also, not all of the elements and their combinations described in the examples are necessarily essential as the solution means of the invention.

[0011] <Example 1> FIG. 1 is a diagram showing an example of the processing flow and display of the project total man-hours / quality prediction simulation system 10.

[0012] The project total man-hours / quality prediction simulation system 10 includes a man-hour input plan input unit 101, an analysis unit 102, and an output unit 103.

[0013] When the man-hour input plan input unit 101 reads the man-hour input data 104, the characteristic data 105, and the milestone data 106, it displays the man-hour input data 104 as the original man-hour input plan in the man-hour input graph 110, displays the characteristic data 105 as the characteristic data 111, and displays the milestone data 106 as the milestone data 112. The man-hour input data 104, which is the man-hour input plan, is represented by time-series data associating the man-hour input amount and its input time. Next, the analysis unit 102 reads the man-hour input data 104, the characteristic data 105, the milestone data 106, and the past data 107, calculates an ideal man-hour input plan, and displays it as the ideal man-hour input plan in the man-hour input graph 110. Next, the user edits the original man-hour input plan in the man-hour input graph 110 on the GUI of the man-hour input plan input unit 101. The edited man-hour input amount is displayed as the man-hour input plan for simulation separately from the original man-hour input plan. The man-hour input plan input unit 101 provides a GUI for the user to edit. According to this GUI, the user can edit the man-hour input amount by operating the editing point indicating the man-hour input per period up and down.

[0014] After editing the man-hour input amount, the analysis unit 102 additionally reads the original man-hour input plan, the ideal man-hour input plan, and the man-hour input plan for simulation displayed in the man-hour input graph 110, calculates the future total man-hours, the number of bugs, the productivity, the bug density, and the development period, and displays them on the output unit 103. The display format may be any of a text format, a table format, a graph format, a chart format, etc. Thereafter, triggered by the user's editing of the man-hour input plan for simulation in the man-hour input graph 110 on the GUI of the man-hour input plan input unit 101, the analysis unit 102 executes the analysis and updates the display content of the output unit 103.

[0015] The project total effort and quality prediction simulation system 10 is implemented on a computer having a processor, memory, auxiliary storage device, and communication interface. The processor is an arithmetic unit that executes programs stored in memory. The functions provided by the project total effort and quality prediction simulation system 10 are realized by the processor executing various programs. Note that some of the processing performed by the processor when executing programs may be executed by other arithmetic units (e.g., hardware such as ASICs and FPGAs). The memory includes ROM, which is a non-volatile memory element, and RAM, which is a volatile memory element. ROM stores immutable programs (e.g., BIOS). RAM is a high-speed, volatile memory element such as DRAM (Dynamic Random Access Memory) and temporarily stores programs executed by the processor and data used when executing programs. The auxiliary storage device is a large-capacity, non-volatile storage device such as a magnetic storage device and stores programs executed by the processor and data used by the processor when executing programs. The communication interface is a network interface device that controls communication with other devices according to a predetermined protocol.

[0016] Figure 2 shows an example of the effort input data 104. The effort input data 104 records the current effort input plan for a project for which the user wants to create an effort input plan, for example in a tabular format. The "Month" column records future months, and the "Effort" column records the effort input currently planned for the project in that month.

[0017] Figure 3 shows an example of characteristic data 105. Characteristic data 105 records quantitative and qualitative data that indicate the characteristics of the project for which the user wants to create an effort input plan. For example, it is good to record the target domain, target language, number of development sites, number of functional and non-functional requirements, FP value, etc.

[0018] Figure 4 shows an example of milestone data 106. Milestone data 106 records milestones for a project for which the user wants to create an effort input plan, for example in a tabular format. The "Month" column records the months in which milestones are set for the project, and the "Milestone" column records the milestones set for that month in the project.

[0019] Figures 5 to 8 show an example of historical data 107. For example, historical data 107 may consist of multiple tables as shown in Figures 5 to 8.

[0020] The historical data 107 shown in Figure 5 represents data on the man-hours invested in past projects. The "Project" column contains the project name, the "Month" column contains the month in which the man-hours were incurred, and the "Man-hours" column records the man-hours invested in that project during that month.

[0021] The historical data 107 shown in Figure 6 represents some of the characteristic data from past projects. The "Project" column contains the project name, while the other columns record the characteristic values ​​of that project. For example, the target domain, target language, number of development sites, number of functional and non-functional requirements, and FP value may be recorded.

[0022] The historical data 107 shown in Figure 7 is a portion of the milestone data for past projects. The "Project" column records the project name, the "Month" column records the month in which a milestone was set for that project, and the "Milestone" column records the milestone set for that month in that project.

[0023] The historical data 107 shown in Figure 8 represents some of the data from past projects, including the final total man-hours and quality. The "Project" column records the project name, the "Total Man-Hours" column records the final total man-hours for the project, the "Number of Bugs" column records the final number of bugs for the project, and the "Development Period" column records the final development period for the project.

[0024] Figures 9 and 10 show examples of data held by the analysis unit 102.

[0025] The data shown in Figure 9 represents the ideal effort input for each domain during the early, middle, and late stages of the project. The "Target Domain" column lists the domain, while the other columns record the ideal effort input for each stage of the project, with the FP value set to 100.

[0026] The data shown in Figure 10 represents the pattern classification results of past projects. The "Project" column contains the project name, and the "Pattern" column records the pattern classification result for that project.

[0027] Figure 11 shows an example of the data displayed by the output unit 103.

[0028] The output unit 103 displays the calculated results for the future total man-hours, number of bugs, productivity, bug density, and development period for the man-hour input plan for the original, ideal, and simulation projects entered by the user. The leftmost column shows the classification as original, ideal, or simulation; the "Total Man-Hours" column shows the total man-hours for that category of the project; the "Number of Bugs" column shows the number of bugs for that category of the project; the "Productivity" column shows the productivity for that category of the project; the "Bug Density" column shows the bug density for that category of the project; and the "Development Period" column shows the development period for that category of the project. These column items are examples, and columns for other items may also be displayed.

[0029] Figure 12 is a flowchart showing the process by which the analysis unit 102 calculates the ideal man-hour allocation plan for the input target project.

[0030] The analysis unit 102 obtains a pre-defined definition of the ideal way to allocate effort for a project in a given domain, based on the domain value set as the characteristic data 105 of the project (1201). The ideal way to allocate effort is preferably expressed as a ratio.

[0031] The analysis unit 102 calculates the ideal man-hour allocation plan for the project based on the definition of the ideal man-hour allocation method and the FP value set as the characteristic data 105 of the project. One method for calculating the ideal man-hour allocation plan is to estimate that the relationship between the FP value and total man-hours is linear, calculate a coefficient of how many times the FP value of the project is compared to the standard, multiply that coefficient by the definition of the ideal man-hour allocation method to obtain the ideal man-hours for the initial, middle, and late stages of the project, and further calculate by interpolating the man-hours between the obtained values. Alternatively, a method may be used in which the relationship between the FP value and man-hours is estimated to be nonlinear (1202).

[0032] The calculated ideal man-hour input plan for the project is then displayed as the ideal man-hour input plan by the man-hour input plan input unit 101 (1203).

[0033] Figure 13 is a flowchart showing the process by which the analysis unit 102 calculates the future total man-hours, number of bugs, productivity, bug density, and development period for the original man-hour input plan displayed in the man-hour input graph 110.

[0034] The analysis unit 102 compares the man-hours of the original man-hours plan for the project, displayed by the man-hours input plan input unit 101, with multiple man-hours patterns that have been stored in advance, and classifies the man-hours into man-hours patterns (1301). The method of classifying into patterns can be a method using an ideal form formula calculated from the average shape of the man-hours plan, a method based on the definition of a flag, pattern matching of the shape of the man-hours plan, or other methods.

[0035] The analysis unit 102 uses a predictive model created in advance from characteristic data and actual data of past data 107 projects that have the same effort input pattern and are classified in the same domain as the project in question to predict the future total effort and number of bugs corresponding to the original effort input of the project. In this case, it is preferable to use a machine learning model or a rule-based model as the predictive model. For example, it is preferable to use an estimation model based on a neural network to predict the future total effort and number of bugs. This estimation model uses actual data such as the number of requirements, requirement complexity, number of tests, and development difficulty as explanatory variables, and actual data of total effort and number of bugs as dependent variables, and performs machine learning. By inputting the number of requirements, requirement complexity, number of tests, development difficulty, etc., the total effort and number of bugs can be obtained. Furthermore, in order to improve the prediction accuracy, milestone data 106 of the project in question and milestone data of past data 107 projects may be used (1302).

[0036] The analysis unit 102 calculates the future productivity, bug density, and development period corresponding to the original man-hour input for the project, based on the values ​​of the project's future total man-hours, future bug count, and characteristic data 105 (1303). A machine learning model or a rule-based model may be used as the prediction model. For example, a neural network estimation model may be used to predict the future total man-hours and bug count. This estimation model uses actual data such as the number of requirements, requirement complexity, number of tests, and development difficulty as explanatory variables, and actual data of total man-hours and bug count as dependent variables, and performs machine learning. By inputting the number of requirements, requirement complexity, number of tests, development difficulty, etc., the total man-hours and bug count can be obtained.

[0037] Then, the output unit 103 displays the future total man-hours, number of bugs, productivity, bug density, and development period corresponding to the original man-hour input for the project (1304).

[0038] Figure 14 is a flowchart showing the process by which the analysis unit 102 calculates the future total man-hours, number of bugs, productivity, bug density, and development period in an ideal man-hour input plan.

[0039] The analysis unit 102 compares the ideal effort input plan for the project displayed on the effort input graph 110 by the effort input plan input unit 101 with a number of pre-stored effort input patterns and classifies the effort input into effort input patterns (1401). The classification method into patterns can be a method using an ideal formula calculated from the average shape of the effort input plan, a method based on the definition of a flag, pattern matching of the shape of the effort input plan, or other methods.

[0040] The analysis unit 102 uses a pre-created prediction model to predict the future total man-hours and number of bugs corresponding to the ideal man-hour input for the project, based on characteristic data and actual data from a group of past data 107 projects that have the same man-hour input pattern and are classified in the same domain as the project in question. In this case, a machine learning model or a rule-based model may be used as the prediction model. For example, a neural network estimation model may be used to predict the future total man-hours and number of bugs. This estimation model uses actual data such as the number of requirements, requirement complexity, number of tests, and development difficulty as explanatory variables, and actual data on total man-hours and number of bugs as dependent variables, and performs machine learning. By inputting the number of requirements, requirement complexity, number of tests, development difficulty, etc., the total man-hours and number of bugs can be obtained. Furthermore, to improve prediction accuracy, milestone data 106 for the project in question and milestone data from the project group of past data 107 may also be used (1402).

[0041] The analysis unit 102 calculates the future productivity, bug density, and development period corresponding to the ideal man-hour input for the project, based on the values ​​of the project's future total man-hours, future bug count, and characteristic data 105 (1403). A machine learning model or a rule-based model may be used as the prediction model. For example, a neural network estimation model may be used to predict the future total man-hours and bug count. This estimation model uses actual data such as the number of requirements, requirement complexity, number of tests, and development difficulty as explanatory variables, and actual data of total man-hours and bug count as dependent variables, and performs machine learning. By inputting the number of requirements, requirement complexity, number of tests, development difficulty, etc., the total man-hours and bug count can be obtained.

[0042] The output unit 103 then displays the future total man-hours, number of bugs, productivity, bug density, and development period corresponding to the ideal man-hour input for the project (1404).

[0043] Figure 15 is a flowchart showing the process by which the analysis unit 102 calculates the future total man-hours, number of bugs, productivity, bug density, and development period in the man-hour input plan for the simulation of the project.

[0044] The analysis unit 102 classifies the man-hours to be allocated into man-hour allocation patterns based on the man-hour allocation plan of the simulation of the project displayed on the man-hour allocation graph 110 by the man-hour allocation plan input unit 101 (1501). The classification method into patterns can be a method using an ideal form formula calculated from the average shape of the man-hour allocation plan, a method based on the definition of a flag, pattern matching of the shape of the man-hour allocation plan, or other methods.

[0045] The analysis unit 102 uses a pre-created prediction model to predict the future total man-hours and number of bugs corresponding to the man-hour input for the simulation of the project in question, based on characteristic data and actual data from a group of past data 107 projects that have the same man-hour input pattern and are classified in the same domain as the project in question. In this case, a machine learning model or a rule-based model may be used as the prediction model. Furthermore, to improve the prediction accuracy, milestone data 106 for the project in question and milestone data from the project group of past data 107 may also be used (1502).

[0046] The analysis unit 102 calculates the future productivity, bug density, and development period corresponding to the man-hour input for the simulation of the project, based on the total future man-hours for the project, the value of the future number of bugs, and the value of the characteristic data 105 (1503).

[0047] The output unit 103 then displays the future total man-hours, number of bugs, productivity, bug density, and development period corresponding to the man-hour input for the simulation of the project (1504).

[0048] Figure 16 shows an example of a "man-hour input pattern" in the processes shown in Figures 13, 14, and 15.

[0049] There are several patterns for manpower input, including the initial investment type (1601), which involves a large initial investment; the constant investment type (1602), which involves little change in investment during the period; and the short-term concentrated investment type (1603), which involves concentrated investment during a specific period. The initial investment type (1601) is suitable for projects to create large software with a large team. The constant investment type (1602) is suitable for projects to modify existing software. The short-term concentrated investment type (1603) is suitable for projects to create small software. These are just examples, and other patterns may be defined.

[0050] <Example 2> In Example 2, in addition to Example 1, the effort input graph 110 displayed by the effort input plan input unit 101 also uses skill information of the personnel to be assigned to the project. In other words, in Example 2, the effort input plan is information on "which personnel with which skills should be assigned to which amount of effort in which future month of the project." This allows for weighting of effort according to the skills of the personnel in Example 2.

[0051] <Example 3> In Example 3, in addition to Example 1, when calculating the effort input graph 110 displayed by the effort input plan input unit 101, two types of information are used: internal effort, which covers development within the organization, and outsourced effort, which covers development outsourced to external parties. That is, in Example 3, the effort input plan consists of two effort input graphs: one representing the original, ideal, and simulated effort input plans for internal effort, and the other representing the original, ideal, and simulated effort input plans for outsourced effort.

[0052] <Example 4> In Example 4, in addition to Example 1, when calculating the effort input graph 110 displayed by the effort input planning input unit 101, an alert is displayed if there is a large discrepancy between the ideal and simulated effort input plan. The alert can be displayed using text, a pop-up window, a dialog box, or coloring the effort input graph 110.

[0053] <Example 5> In Example 5, in addition to Example 1, the output unit 103 displays an alert if there is a large discrepancy between the ideal and the simulation total man-hours, number of bugs, productivity, bug density, and development period. The alert can be displayed using text, a popup window, a dialog box, or coloring on the output screen.

[0054] As described above, the project total man-hour and quality prediction simulation system 10 of this embodiment allows project planners to predict the quality and total man-hours of the man-hour input plan with high accuracy.

[0055] It should be noted that the present invention is not limited to the embodiments described above, but includes various modifications and equivalent configurations within the spirit of the attached claims. For example, the embodiments described above are described in detail for the purpose of clearly illustrating the present invention, and the present invention is not necessarily limited to having all the described configurations. Furthermore, some of the configurations of one embodiment may be replaced with those of another embodiment. Furthermore, configurations of other embodiments may be added to the configuration of one embodiment. Furthermore, some of the configurations of each embodiment may be added, deleted, or replaced with those of other embodiments.

[0056] Furthermore, each of the aforementioned configurations, functions, processing units, and processing means may be implemented in hardware, for example, by designing them as integrated circuits, or they may be implemented in software by having a processor interpret and execute programs that realize each function.

[0057] Information such as programs, tables, and files that implement each function can be stored in memory, hard disks, SSDs (Solid State Drives), or other storage media such as IC cards, SD cards, and DVDs.

[0058] Furthermore, the control lines and information lines shown are those deemed necessary for explanation purposes and do not necessarily represent all control lines and information lines required for implementation. In reality, it can be assumed that almost all components are interconnected. [Explanation of Symbols]

[0059] 10. Quality Prediction Simulation System 101 Man-hour input planning section 102 Analysis Department 103 Output section 104 Man-hour input data 105 Characteristic Data 106 Milestone Data 107 Past Data 110 Man-hour input graph 111 Characteristic Data 112 Milestone Data

Claims

1. A man-hour input plan generation system, A computing device that executes a predetermined computation process and a storage device that is connected to the computing device, an input unit for receiving an input of a man-hour input plan for a project; an analysis unit that analyzes the man-hour input plan input by the arithmetic device; The arithmetic device has an output unit that outputs an analysis result by the analysis unit, The input unit receives input of time-series data associating man-hour input amounts and input times as the man-hour input plan, The analysis unit is The input time series data is collated with a plurality of man-hour input patterns stored in advance to identify a corresponding man-hour input pattern; extracting past projects having characteristics common to the identified man-hour input pattern from among pre-stored past projects; A man-hour input plan generating system characterized by predicting at least one of the total man-hours and quality of the man-hour input plan based on the performance of the extracted past projects, and outputting at least one of the total man-hours and the quality.

2. A labor-hour input plan generation system as described in claim 1, The input unit receives characteristic data relating to a project as an input; The man-hour input plan generating system is characterized in that the analysis unit generates ideal time-series data based on the input characteristic data.

3. A labor-hour input plan generation system according to claim 2, The input unit further receives milestone data relating to a project as an input; The man-hour input plan generating system is characterized in that the analysis unit generates the ideal time series data based on milestone data in past projects.

4. A labor-hour input plan generation system as described in claim 1, The man-hour input plan generating system is characterized in that, when a user edits the man-hour input plan, the analysis unit outputs a total man-hours or quality as an analysis result of the input man-hour input plan, and the output unit outputs the analysis result by the analysis unit.

5. A labor-hour input plan generation system as described in claim 1, The input unit further receives milestone data relating to a project as an input; The man-hour input plan generating system is characterized in that the analysis unit predicts at least one of the total man-hours or the quality based on milestone data of past projects.

6. A labor-hour input plan generation system according to claim 1, The man-hour input plan generating system is characterized in that the input unit provides an interface that enables a user to generate a man-hour input plan by making changes to ideal time-series data using a GUI.

7. A labor-hour input plan generating method executed by a labor-hour input plan generating system, comprising: The man-hour input plan generation system includes a computing device that executes a predetermined calculation process, and a storage device that is connected to the computing device, The method for generating a man-hour input plan includes the steps of: an input step in which the arithmetic device receives an input of a man-hour input plan for a project; an analysis step in which the arithmetic device analyzes the input man-hour input plan; an output step in which the arithmetic device outputs an analysis result obtained by the analysis step, In the input step, the arithmetic device receives input of time-series data associating man-hour input amounts and input times as the man-hour input plan, In the analysis step, the calculation device compares the input time-series data with a plurality of man-hour input patterns stored in advance to identify a corresponding man-hour input pattern, extracts past projects having characteristics in common with the identified man-hour input pattern from the pre-stored past projects, and predicts at least one of a total man-hour input and quality based on the track record of the extracted past projects.

8. A method for generating a labor input plan as described in claim 7, comprising: In the input step, the computing device receives characteristic data related to a project as an input; The method for generating a man-hour input plan, wherein in the analyzing step, the arithmetic device generates ideal time-series data based on the input characteristic data.

9. A method for generating a labor input plan as described in claim 8, comprising: In the input step, the arithmetic device further receives milestone data related to a project as an input; The method for generating a man-hour input plan, wherein in the analysis step, the computing device generates ideal time-series data based on milestone data from past projects.

10. A method for generating a labor input plan as described in claim 7, comprising: A method for generating a labor-hour input plan, characterized in that, in the analysis step, in response to a user editing the labor-hour input plan, the arithmetic device outputs a total amount of labor or quality as an analysis result of the input labor-hour input plan, and in the output step, the arithmetic device outputs the analysis result of the analysis step.

11. A method for generating a labor input plan as described in claim 7, comprising: In the input step, the arithmetic device further receives milestone data related to a project as an input; A method for generating a man-hour input plan, wherein in the analysis step, the calculation device predicts at least one of the total man-hours or the quality based on milestone data from past projects.

12. A method for generating a labor input plan as described in claim 7, comprising: The man-hour input plan generating method is characterized in that, in the input step, the arithmetic device provides an interface that enables a user to make changes via a GUI from ideal time-series data to generate a man-hour input plan.

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