Project analysis device, project analysis method, and program

Abstract

To quantify the impact of management items on a development project.SOLUTION: A device for analyzing a development project based on project management data includes: a data complementation unit configured to identify past projects that are similar in project characteristics to the development project as similar projects and add management items contained in project data of the similar projects to development project's project data; a data extraction unit configured to extract multiple data representing the impact on the development project for management items included in the development project's project data that meet extraction conditions set for object management items to be extracted, and generate the extracted data; and an impact degree calculation unit configured to calculate a degree of impact on the development project for each object management item based on the extracted data and generate impact degree data in tabular format that shows the object management items and their impact degrees.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a project analysis device, a project analysis method, and a program. [Background technology]

[0002] In recent years, in order to achieve efficient development in various fields, project development has been carried out using application lifecycle management tools (hereinafter referred to as "ALM tools"). Furthermore, development efficiency can be improved by analyzing the data accumulated in ALM tools, extracting problems, and predicting future behavior.

[0003] Background art in this technical field is provided by Patent Document 1. This publication proposes a system that automatically performs highly accurate progress predictions based on data trends in, for example, ongoing projects, taking into account the project's characteristics and progress trends (see abstract). Another background art is provided by Patent Document 2. This publication proposes a project management system that derives the impact of, for example, specification changes (see abstract). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-164392 [Patent Document 2] Japanese Patent Application Laid-Open No. 2010-287026 Summary of the Invention [Problem to be solved by the invention]

[0005] In Patent Document 1, data on completed projects is used to predict future data trends in data on currently ongoing projects. However, there is no disclosure of a technique for quantitatively expressing the extent to which the predicted results will affect the ongoing projects.

[0006] Furthermore, Patent Document 2 predicts the impact of specifications that will be affected by specification changes, etc. However, because this is a prediction method that uses data that already exists, it does not describe a technology that complements future information that does not currently exist.

[0007] Therefore, an object of the present invention is to provide a project analysis device, a project analysis method, and a program that quantify the impact on a project under development (hereinafter abbreviated as "development project") of management matters that are currently occurring in the project and management matters that will occur in the future. [Means for solving the problem]

[0008] The project analysis device of the present invention is, for example, a project analysis device that analyzes a development project based on project management data of a development project and past projects managed by a project management tool, and includes: a memory unit that stores project characteristics for each project managed by the project management tool and project data that represents management items for each development process in the project, generated for each project based on the project management data; a data complementation unit that defines past projects whose project characteristics are similar to those of the development project as similar projects, compares the project data of the similar projects with the project data of the development project, and adds to the project data of the development project management items that are not included in the project data of the development project but are included in the project data of the similar projects as management items that may arise in the future in the development project; a data extraction unit that extracts, from the management items included in the project data of the development project, multiple data representing the impact on the development project for target management items that satisfy extraction conditions set for target management items to be extracted, to generate extracted data; and an impact calculation unit that calculates the degree of impact on the development project for each target management item based on the extracted data, and generates impact data in which the target management items and the impact are displayed in tabular format.

[0009] Furthermore, the project analysis method of the present invention is, for example, a project analysis method using a project analysis device that analyzes a development project based on project management data of a development project and a past project managed by a project management tool, the project analysis device comprising a storage unit, a data complement unit, a data extraction unit, and an impact calculation unit, the storage unit stores project characteristics for each project managed by the project management tool, and project data representing management matters for each development process in the project, generated for each project based on the project management data, the data complement unit determines the past project whose project characteristics are similar to the development project as a similar project, and calculates the impact of the similar project. and compares the project data of the development project with the project data of the development project, and adds the management items that are not included in the project data of the development project but are included in the project data of the similar project to the project data of the development project as management items that may arise in the future in the development project, the data extraction unit extracts, from the management items included in the project data of the development project, multiple data representing the impact on the development project for target management items that satisfy extraction conditions set for the target management items to be extracted, to generate extracted data, and the impact calculation unit calculates the impact on the development project for each target management item based on the extracted data, and generates impact data in which the target management items and the impact are expressed in tabular form.

[0010] The program of the present invention causes a computer to function as the project analysis device. [Effects of the Invention]

[0011] According to the present invention, a project analysis device, a project analysis method, and a program are provided that quantify the impact on a development project of management issues that are currently occurring in the development project and management issues that will occur in the future. Problems, configurations, and effects other than those described above will become clear from the description of the following embodiments. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram illustrating an overview of a project analysis device according to a first embodiment. [Figure 2] FIG. 1 illustrates an example of an overall configuration of a project analysis apparatus. [Figure 3] FIG. 10 is a diagram illustrating an example of project characteristic data. [Figure 4] FIG. 10 is a diagram illustrating an example of project data. [Figure 5] FIG. 10 is a diagram illustrating an example of a flowchart of a process performed by a data complementing unit. [Figure 6] An example of development project supplemental data will be described. [Figure 7] FIG. 10 is a diagram illustrating an example of a flowchart of a process performed by a data extraction unit. [Figure 8] FIG. 10 is a diagram illustrating an example of extraction condition data in which a plurality of types of extraction conditions are compiled as a table. [Figure 9] FIG. 10 is a diagram illustrating an example of extracted data. [Figure 10] FIG. 10 is a diagram illustrating an example of impact data output by an impact calculation unit. [Figure 11] FIG. 10 is a diagram for explaining normalization of the severity Sn in the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, the embodiments will be described with reference to the drawings. [Example]

[0014] FIG. 1 is a diagram illustrating an overview of a project analysis device according to a first embodiment. The project analysis device 100 analyzes a development project based on project management data of a development project and past projects managed by an ALM tool 110, which is a project management tool. The analysis results are displayed on the project analysis device 100 or a display device. Furthermore, the analysis results are stored in the ALM tool 110.

[0015] The ALM tool 110 stores project management data for each project. The project management data includes work management ticket data 122, defect management data 123, traceability data 124, deliverable data 125, and configuration management data 126. These data are created and updated by project members according to the progress of the project. For example, data related to project A is stored by project A member 121, and data related to project B is stored by project B member 131.

[0016] Work management ticket data 122 is data for managing the progress and results of work by personnel or teams, projects, etc., and is managed with a ticket ID assigned to each work management ticket. Defect management data 123 is data for recording defects that occur and managing the process up to the defect resolution, and is managed with a defect ID assigned to each defect. Deliverable data 125 is data for storing deliverables such as plans and design documents required for development and created source code, and is managed with a deliverable ID assigned to each deliverable. Traceability data 124 is data for managing the connections (traceability) between data such as work management ticket data 122, defect management data 123, and deliverable data 125, and is managed with a traceability ID assigned to each traceability. Configuration management data 126 is data for managing the change history of all data, such as the update date, update version, and update content.

[0017] 2 is a diagram showing an example of the overall configuration of the project analysis apparatus 100. The project analysis apparatus 100 includes an input interface 210, a processor 220, a storage unit 230, and an output interface 240.

[0018] The input interface 210 is a port connected to an input device such as a mouse or keyboard, and transmits data input by the input device to the processor 220.

[0019] The storage unit 230 stores data used in processing by the processor 220 and data generated as a result of processing by the processor 220. Of the data stored in the storage unit 230, development project data 231, which is project data of a development project, past project data 232, which is project data of past projects, and project characteristics 233 of each of the development project and past projects are assumed to be stored in advance in the storage unit 230. The project data is generated based on the ALM tool 110.

[0020] Here, project characteristics and project data will be specifically described using FIGS. 3 and 4. FIG. 3 is a diagram showing an example of project characteristics. Project characteristics are information that represents the characteristics of a project, including information such as the project ID and target product, and are used to identify the project. FIG. 4 is a diagram showing an example of project data. Project data is data that represents management items for each development process in a project. Management items include the work, deliverables, defects, and traceability in the project. Project data includes the development process included in the project, the ticket ID of the work management ticket data corresponding to the development process, the deliverable, the deliverable version, the related ticket ID, the related defect ID, and the defect code. The development process and ticket ID are part of the data in the work management ticket data 122. In addition, the deliverable is part of the deliverable data 125, the deliverable version is part of the configuration management data 126, the related ticket ID and related defect ID are part of the traceability data 124, and the defect code is part of the defect management data 123. Note that the related ticket ID is the ticket ID of the work management ticket data related to the development process listed in the leftmost column of the same row. The related defect ID is the ID of a defect that occurred in the development process described in the leftmost column of the same row.

[0021] The processor 220 in FIG. 2 has functional units such as a similar project selection unit 221, a data complementation unit 222, a data extraction unit 223, and an influence calculation unit 224.

[0022] The similar project selection unit 221 receives information for identifying projects to be analyzed, such as a project ID, from the input interface 210, identifies projects with project characteristics that include information that matches the received information, and acquires project data for the identified projects from the storage unit 230. In the following, development projects are the targets of analysis, and the similar project selection unit 221 acquires development project data 231 from the storage unit 230.

[0023] Furthermore, the similar project selection unit 221 compares project characteristics, selects past projects similar to the development project, and outputs past project data 232 of the selected past projects as similar project data 234. Possible methods for selecting similar projects include determining the degree of match between the project characteristics of the development project and the past projects, determining the degree of match taking weighting into account by increasing the weighting of project characteristics with high importance, multivariate analysis, machine learning, etc. Also, the similar project data 234 is not limited to data on a single project, and multiple projects may be selected.

[0024] The data complementing unit 222 complements the development project data 231 by adding management items that may occur in the future in the development project using the similar project data 234 or the data complementing model 235, and outputs the complemented development project data 231 as development project complementing data 236. Because a development project is an ongoing project, there may be work or problems that have not yet occurred but may occur in the future, and information about this may be omitted from the development project data 231. Even in such cases, the data complementing unit 222 adds data on management items such as work or problems that may occur in the future to the development project data 231 to create the development project complementing data 236, thereby preventing omissions in understanding management items required for the development project. The data complementing model 235 is a model that has previously learned management items that need to be complemented in the development project from project data of past projects and the development project.

[0025] FIG. 5 is an example of a flowchart of processing by the data complementing unit 222. In step S300, the data complementing unit 222 acquires similar project data 234 from the data complementing unit 222 and acquires development project data 231 from the storage unit 230. In step S301, it is determined whether sufficient data is stored in the similar project data 234. The sufficiency of the data in the similar project data 234 can be determined, for example, using the data input rate or the data non-input rate. In step S302, it is determined whether sufficient data has been stored for all similar projects. If all similar projects have been determined, the process proceeds to step S303; if there are similar projects that have not been determined, the process returns to step S301 and performs processing. In step S303, it is determined whether a similar project with sufficient data stored exists. If a similar project with sufficient data stored exists, the process proceeds to step S304; if not, the process proceeds to step S305. In step S304, similar project data 234, which has sufficient data stored, is used to complement data relating to work, defects, etc. that do not exist in development project data 231 and that may be created in the future, and the process ends by outputting the complemented data as development project data 236. In step S305, data complement model 235 is used to complement data relating to work, defects, etc. that do not exist in development project data 231 and that may be created in the future, and the process ends by outputting the complemented data as development project data 236.

[0026] FIG. 6 illustrates an example of development project supplemental data 236. Development project supplemental data is data obtained by adding a column for "Actual Results / Supplementary Data Classification" to the rightmost column of development project data and further adding data on management items such as tasks and defects. In this example, supplementation 409 and 410 have been performed. The actual results / supplementary data classification is used to distinguish whether data has been supplemented by the data supplementation unit 222. Data that was included in the development project data is marked as "Actual Results," while data supplemented by the data supplementation unit 222 is marked as "Supplementary." Examples of methods for supplementing data include supplementation 409, which adds part of the data registered in a ticket, and supplementation 410, which adds a new ticket itself. Supplementation 409 is an example in which a related ticket ID, related defect ID, and defect code have been added. Supplementation 410 is an example in which a development process, deliverables related to the development process, related tickets, related defect IDs, and defect codes have been added. Furthermore, examples of methods by which the data complementing unit 222 complements data include adding data that exists in similar project data to the development project data, as well as highly accurate complementing methods using multivariate analysis and machine learning.

[0027] 7 is an example of a flowchart of processing by the data extraction unit 223. The data extraction unit 223 extracts management items contained in the development project data in accordance with preset extraction conditions and additional extraction conditions, and generates extracted data that summarizes the extracted management items. Each step will be explained below.

[0028] In step S600, the data extraction unit 223 acquires development project data from the storage unit 230. In step S601, some of the management items included in the development project data are extracted according to the extraction conditions. The extraction conditions are conditions set for the target management items to be extracted from the management items included in the development project data. FIG. 8 shows an example of extraction condition data in which multiple types of extraction conditions are compiled as a table. Extraction conditions are set for each item. The data extraction unit 223 extracts target management items that satisfy at least one extraction condition included in the extraction condition data. For example, if the extraction condition 502 shown in FIG. 8 specifies "response cost is 0.4 [M yen] or more," the target management items will be defects. The data extraction unit 223 refers to the defect management data of the ALM tool, checks the response cost for each defect indicated by the defect ID included in the development project supplemental data 236 shown in FIG. 6, and extracts defects with a response cost of "0.4 [M yen] or more."

[0029] In step S602, the data extraction unit 223 extracts some of the management items included in the development project data in accordance with the additional extraction conditions. The additional extraction conditions are conditions for specifying additional management items that you want to extract from among the target management items that do not satisfy the extraction conditions. For example, if the additional extraction condition is set to "probability of defect reproduction is 50% or more," defects that have a high probability of recurrence can be extracted regardless of the extraction conditions and added to the extracted data. Note that data that satisfies both the extraction conditions and the additional extraction conditions is extracted in step S601, and is therefore not extracted in step S602.

[0030] In step S603, the data extraction unit 223 generates extracted data 239 for the management items extracted based on the extraction conditions and the additional extraction conditions. In this case, the extracted data 239 is for defects. FIG. 9 shows an example of the extracted data 239. All defects except for defect ID 101 have a response cost of 0.4 million yen or more, and therefore are defects extracted based on the extraction conditions. In contrast, defect ID 101 has a response cost of 0.3 million yen, and therefore is not extracted based on the extraction conditions. However, because the defect reproducibility is 50%, it is a defect extracted based on the additional extraction conditions. In this way, by using the additional extraction conditions, it is possible to specify management items to be extracted as extracted data in addition to those that satisfy the extraction conditions. The additional extraction conditions used here are stored in the storage unit as additional specified condition data. For extracted data related to defects, each column stores, for example, data for the defect ID, defect discovery process, defect implementation process, defect code, defect response cost, defect occurrence rate, and defect reproducibility, from left to right. Each row stores information for each extracted defect. The information contained in the extracted data is included in the ALM tool or development project data. For example, information about the defect discovery process and defect mitigation process is obtained by referencing the "cause" in the defect management data in the ALM tool shown in Figure 1. Information about defect response costs, defect occurrence rates, and defect reproducibility can also be obtained by referencing the defect management data in the ALM tool.

[0031] Here, data relating to defects is extracted from defect management data 123, but this is not limiting. For example, data relating to work may be extracted from work management ticket data 122, data relating to traceability may be extracted from traceability data 124, or data relating to deliverables may be extracted from deliverable data 125.

[0032] 2 receives extracted data 239 as input, calculates the degree of impact on the development project for each extracted target management item, generates impact data that shows the target management items and the impact in a tabular format, and outputs this to the output interface 240. The impact calculation unit 224 calculates the impact using, for example, equation (1).

[0033]

number

[0034] Here, n represents the number of the target management item. Hereinafter, the management item with number n will be called management item n. n is the impact of management item n, S n is the severity of control item n, F n is the occurrence frequency of control item n, R n is the reproducibility of control item n.

[0035] For example, when the data extraction unit 223 generates extracted data (see FIG. 9) related to defects from the defect management data and the impact calculation unit 224 calculates the impact of the defect based on the extracted data, the impact calculation unit 224 calculates the impact of each defect by setting the defect ID to n. n In this case, the severity S n is the defect response cost 703, and the occurrence frequency F n is the defect occurrence rate 704, and the reproducibility R n corresponds to the defect reproducibility 705. Note that defect response cost 703 indicates the cost actually spent on defect response in similar projects. Defect occurrence rate 704 indicates the defect occurrence rate calculated for each defect code across all defects that occurred in all projects. Defect reproducibility 705 indicates the degree of possibility that a defect will recur, and the higher the possibility that a defect will recur, the larger the value. For example, if a system that can automatically detect defects is built, the defect is less likely to recur, so the value of defect reproducibility 705 will be small. For example, when determining defect reproducibility 705, an automatic calculation method using the input rate of items that serve as countermeasures for defect occurrence and the data storage rate is expected.

[0036] Note that the defect response cost 703 has different units from the defect occurrence rate 704 and defect reproducibility 705, so it needs to be normalized to be expressed as a percentage. One example of this normalization method is a method of normalizing using equation (2).

[0037]

number

[0038] S n is the normalized severity of a certain management issue n, S n_real is the severity of control item n, S max is the maximum value of the severity included in the extracted data, and β is a correction parameter that determines the range of the severity S. For example, based on the extracted data in Figure 9, n When calculating the severity S n Since this corresponds to the defect response cost, the maximum severity S max The defect response cost for defect ID 100 is 30.5 [M yen]. Also, β is the severity S n The numerical range of the occurrence frequency F n and reproducibility R n is a parameter used to adjust the numerical range so that it is the same as the frequency of occurrence F and the reproducibility R. If the occurrence frequency F and the reproducibility R are expressed as a percentage, for example, from 0 to 100%, then β will be 100.

[0039] 10 is a diagram showing an example of impact data output by the impact calculation unit 224. The impact data is in a table format, and each column stores, for example, data such as a rank 901, a defect impact 902, a defect ID 700, a defect content 904, and a defect development process 702, from left to right. The rank indicates the ranking of the defects contained in the extracted data. In FIG. 10, the impact I calculated by the impact calculation unit 224 is n The defect information is stored in descending order.

[0040] The above explanation was given using an example of the impact of a defect based on defect management data of the ALM tool 110. However, when calculating the impact based on work management ticket data 122, traceability data 124, deliverable data 125, etc., the impact can also be calculated by replacing the severity, occurrence frequency, and reproducibility with information appropriate for each data.

[0041] For example, when the impact calculation unit 224 calculates the impact of a deliverable based on the deliverable data 125, the impact calculation unit 224 calculates the impact of each deliverable by setting the deliverable ID to n. n In this case, the severity S n is the expected reworking man-hours when creating the deliverable, and the occurrence frequency F n is the expected rework rate when creating the deliverable, and R is the reproducibility n corresponds to the possibility of rework and reproducibility. In addition, rework reproducibility means that if measures are taken to prevent rework from occurring, This is an index that takes a value close to 0% depending on the level of measures taken, and takes a value of 100% if no measures are taken.

[0042] Furthermore, for example, when the impact calculation unit 224 calculates the impact of an operation based on the operation management ticket data 122, the impact calculation unit 224 calculates the impact I for each operation, where n is the ticket ID. n In this case, the severity S n is the work man-hours, and the occurrence frequency F n is the occurrence rate of delays to the schedule of the work in question in past projects, and the reproducibility R n The reproducibility of work delays corresponds to the possibility of work delays. Note that the reproducibility of work delays is an index that takes a value close to 0% depending on the level of measures taken to prevent work delays, and takes a value of 100% if no measures are taken.

[0043] In addition, for example, when the influence calculation unit 224 calculates the influence based on the traceability data 124, the influence calculation unit 224 calculates the influence I for each traceability by setting the traceability ID to n. n In this case, the severity Sn is the man-hours required to achieve traceability, and occurrence frequency F n is the occurrence rate of traceability setting omissions in past projects, and the reproducibility R n The reproducibility of traceability omissions corresponds to the possibility of reproducibility of traceability omissions. Note that the reproducibility of traceability omissions is an index that takes a low value if measures are taken to prevent omissions in traceability settings, and takes a value of 100% if no measures are taken.

[0044] In addition, when the impact calculation unit 224 generates extracted data from the work management ticket data 122, the traceability data 124, and the deliverable data 125, the data extraction unit 223 extracts data corresponding to at least the severity, occurrence frequency, and reproducibility, as in the case of defect management data.

[0045] In this way, by quantifying and outputting the impact of management items on a development project, the degree of impact that management items have on the development project can be visualized and understood, allowing the person in charge to address the items with the highest impact first. As a result, it is possible to prevent delays and oversights in starting development work, failure to create deliverables, the occurrence of defects, and failure to set up traceability.

[0046] In the above, impact level I n Although the method of calculating the severity S as a product of three indices according to formula (1) has been described, the number of indices is not limited to this. n , frequency of occurrence F n and reproducibility R n In addition to the above, a fourth indicator, α, may be used. α is an indicator that is related to the calculation of impact and can be expressed quantitatively. For example, when calculating the impact of a deliverable, data such as the percentage of issues pointed out in a review of the deliverable can be used as α. The impact may also be calculated as the product of two indicators.

[0047] The storage unit 230 may store the information received from the input interface 210. Alternatively, the development project data 231, the past project data 232, and the project characteristics 233 may be stored in a server other than the project analysis device 100 itself, or in a server of the project analysis device 100 synchronized with server information other than the project analysis device 100.

[0048] This embodiment makes it possible to quantify the influence on the development project of management items that are currently occurring in the development project and management items that will occur in the future. [Example]

[0049] In Example 2, the severity S in formula (1) in Example 1 is n The following describes a method of normalization that differs from that of the first embodiment. Note that the following mainly describes the differences from the first embodiment, and a description of the common parts will be omitted.

[0050] FIG. 11 shows the severity S in Example 2. n11 is a diagram for explaining normalization of the defect ID 100. FIG. 11 is a schematic diagram of a development flow generated for each defect in the extracted data of FIG. 9. The impact calculation unit 224 generates such a schematic diagram for each defect included in the extracted data of FIG. 9. In the schematic diagram 800, a task 802 exists for each development process 801, and the tasks 802 are connected by relationships 803 to represent a series of development flows. A defect in task 804 (a task in a diagonal lane) at the defect creation location generates defect response man-hours in tasks 805 to 811 connected by relationships 803 in subsequent processes. In general, the further away from the task at the defect creation location, the greater the defect response man-hours. In FIG. 11, the darker the fill color of a task, the greater the defect response man-hours. Therefore, the further a task is from task 804 at the defect creation location, the darker the fill color. As an example of a method for calculating the man-hours required to deal with defects in each of the tasks 805 to 811, the actual man-hours required to deal with defects in similar projects are used, and calculations are made using the rate of change in the man-hours required to deal with defects between each process. Then, the sum of the man-hours required to deal with defects in each of the tasks 805 to 811 is calculated as the man-hours required to deal with defects S n_prediction Similarly, for each defect included in the extracted data, the defect response man-hours S n_prediction Calculate the maximum value of S max Then, using equation (3), the severity of each defect S n Calculate.

[0051]

number

[0052] In the above example, the process p8 in which the terminal task 811 exists is used as the reference. n_prediction However, S is calculated based on different processes. n_prediction may be calculated.

[0053] S n_realis calculated as the man-hours for dealing with defects from the defect creation point to the defect discovery point, that is, from process p4 to process p7 in Fig. 11. Therefore, for defects where the defect discovery point is different, S n_real When calculating S n_real The target processes of S are different. n_prediction is calculated as the effort required to deal with the defect up to the reference process, that is, process p8 in Figure 11, regardless of the defect location. n_prediction is calculated as the effort required to deal with defects up to the standard process for all defects, so S n_prediction The accuracy of the maximum value of is improved.

[0054] Here, the example has been explained using the severity of a defect, but the same calculation can be performed when predicting the work management ticket data 122, traceability data 124, deliverable data 125, etc. For example, suppose that the work management ticket data 122 and deliverable data 125 are used to calculate the impact of a deliverable that is likely to require rework. In this case, process 801 is the granularity of the development process, and relationship 803 connects the work tickets in which the man-hours for each deliverable are recorded. Then, for each deliverable, the total man-hours for which rework has occurred for each process is set as Sreal, and S is calculated using the rate of change in the man-hours for rework for deliverables between each process. n_prediction and S max Calculate the severity S n Furthermore, the development process 801 in FIG. 11 is an example, and the present invention is not limited to this.

[0055] The present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0056] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD. In addition, the control lines and information lines are shown as those considered necessary for the explanation, and not all control lines and information lines are necessarily shown in the drawings. In reality, it can be considered that almost all components are interconnected. [Explanation of symbols]

[0057] 100...Project analysis device, 210...Input interface, 220...Processor, 221...Similar project selection unit, 222...Data complementation unit, 223...Data extraction unit, 224...Influence calculation unit, 230...Memory unit, 240...Output interface.

Claims

1. A project analysis device that analyzes a development project based on project management data of a development project and past projects managed by a project management tool, comprising: a storage unit that stores project characteristics for each project managed by the project management tool and project data that is generated for each project based on the project management data and that represents management items for each development process in the project; a data complementation unit that defines the past projects whose project characteristics are similar to those of the development project as similar projects, compares project data of the similar projects with project data of the development project, and adds the management items that are not included in the project data of the development project but are included in the project data of the similar projects to the project data of the development project as management items that may arise in the development project in the future; a data extraction unit that extracts a plurality of data representing the impact on the development project for target management items that satisfy extraction conditions set for the target management items to be extracted from the management items included in the project data of the development project, and generates extracted data; an impact calculation unit that calculates the impact on the development project for each target management item based on the extracted data and generates impact data in which the target management items and the impact are expressed in a tabular format. A project analysis device characterized by:

2. The project analysis device according to claim 1 , The system further includes a similar project selection unit that selects the similar project based on a degree of coincidence between the project characteristics of the past project and the project characteristics of the development project. A project analysis device characterized by:

3. The project analysis device according to claim 1 , When input of the project data of the similar project is insufficient, the data complementing unit complements the management matters that may arise in the development project in the future in the project data of the development project using a data complementing model that has previously learned the management matters that will need to be complemented in the development project from the project data. A project analysis device characterized by:

4. The project analysis device according to claim 1 , The data extraction unit adds data of the target management items to the extracted data in accordance with additional extraction conditions set to specify items to be additionally extracted from the target management items that do not satisfy the extraction conditions. A project analysis device characterized by:

5. The project analysis device according to claim 1 , The impact calculation unit arranges the target management items in the impact data in descending order of the impact. A project analysis device characterized by:

6. A project analysis method using a project analysis device that analyzes a development project based on project management data of a development project and past projects managed by a project management tool, comprising: the project analysis device includes a storage unit, a data complement unit, a data extraction unit, and an influence degree calculation unit; the storage unit stores project characteristics for each project managed by the project management tool, and project data that is generated for each project based on the project management data and that represents management items for each development process in the project; the data complementation unit determines the past projects whose project characteristics are similar to those of the development project as similar projects, compares project data of the similar projects with project data of the development project, and adds the management items that are not included in the project data of the development project but are included in the project data of the similar projects to the project data of the development project as management items that may arise in the development project in the future; the data extraction unit extracts, from the management items included in the project data of the development project, a plurality of data representing the impact on the development project for target management items that satisfy extraction conditions set for the target management items to be extracted, thereby generating extracted data; The impact calculation unit calculates the impact on the development project for each target management item based on the extracted data, and generates impact data in which the target management items and the impacts are expressed in a table format. A project analysis method characterized by:

7. A program that causes a computer to function as the project analysis device according to any one of claims 1 to 5.

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