Server device, query volume prediction system, and query volume prediction method

Abstract

We propose a technology that accurately predicts future inquiry volumes, taking into account the specific circumstances of each software product. [Solution] This disclosure proposes a server device for predicting the number of future user inquiries for each function constituting a software product, which performs the following processes: obtaining past inquiry information from an inquiry information database and calculating a reference prediction value, which is a predicted value for the number of future inquiries for the function to be predicted, according to existing prediction techniques; and obtaining quality evaluation values ​​for documents related to the function to be predicted from a document quality database, modifying the reference prediction value based on the obtained quality evaluation values ​​for the documents, and calculating a first modified prediction value.

Description

Technical Field

[0001] The present disclosure relates to a server device, an inquiry number prediction system, and an inquiry number prediction method.

Background Art

[0002] When deploying a software product, a system may be provided to handle inquiries from its users. Inquiry data determined to be unable to be handled at the representative inquiry window is routed through a dedicated handling window and accumulated in the system together with the handling results.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the current system only accumulates and holds inquiry data, statistically processes this data, and presents to the administrator the annual total, the number of received inquiries per month, etc. Using the current system, the future number of inquiries can be predicted from the average value of the accumulated past inquiry data, etc., but by itself, it cannot reflect in the prediction of the future number of inquiries the document quality such as the quality of the software product and the quality of the product manual, and the specific circumstances of the software product including the usage frequency of each function of the software product.

[0005] In view of such circumstances, the present disclosure proposes a technique for accurately predicting the future number of inquiries that reflects the specific circumstances of each software product.

Means for Solving the Problems

[0006] To solve the above problems, this disclosure provides a server device that predicts the number of future user inquiries for each function constituting a software product, A storage device that holds a program for predicting the number of future queries, The system includes a computer that reads the program from the storage device and performs a process to predict the number of future queries, The aforementioned storage device or another storage device includes a query information database that holds past query information up to the day before the day on which the process of predicting the number of future queries is performed, and a document quality database that holds quality evaluation values ​​for each document related to each of the aforementioned functions, The aforementioned computer, The process involves obtaining past query information from the query information database and calculating a baseline predicted value, which is a predicted value for the future number of queries for the function to be predicted, according to existing prediction techniques. The process involves obtaining quality evaluation values ​​for documents related to the function to be predicted from the document quality database, modifying the baseline prediction value based on the obtained document quality evaluation values, and calculating a first modified prediction value. We propose a server device to execute this.

[0007] Further features relating to this disclosure will become apparent from the description herein and the accompanying drawings. Furthermore, aspects of this disclosure are achieved and realized by elements and various combinations of elements and the aspects of the hereafter detailed description and the accompanying claims. It should be understood that the descriptions herein are merely typical examples and do not limit the scope of the claims or applications in any way. [Effects of the Invention]

[0008] The technology described herein makes it possible to accurately predict future inquiry volumes while taking into account the specific circumstances of each software product. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows an example of a schematic configuration of the inquiry volume prediction system 100 according to an embodiment of the present disclosure. [Figure 2] This figure shows various programs stored in the main memory 1011 and various data (example software configuration) stored in the auxiliary memory 1013 of the server device 101 according to this embodiment. [Figure 3A] This diagram shows the processing sequence from the request for registration of software information and / or documents to the output of document quality evaluation results and / or software quality evaluation results. [Figure 3B] This diagram shows the processing sequence from the registration request for information other than software information and documentation to the output of the query count prediction result. [Figure 4] This figure shows an example configuration of the prediction data editing screen 400. [Figure 5] This figure shows an example configuration of the function classification editing screen 500. [Figure 6] This figure shows an example configuration of the function classification list screen 600. [Figure 7] This diagram shows an example configuration of the event information editing screen 700. [Figure 8] This figure shows an example configuration of the event information list screen 800. [Figure 9] This figure shows an example of the configuration of the document information editing screen 900. [Figure 10] This diagram shows an example configuration of the document information list screen 1000. [Figure 11] This diagram shows an example configuration of the software information editing screen 1100. [Figure 12] This diagram shows an example configuration of the software information list screen 1200. [Figure 13] This figure shows an example configuration of the editing screen 1300 used for editing. [Figure 14] This diagram shows an example configuration of the "List of Technologies Used" screen 1400. [Figure 15]FIG. is a diagram showing a prediction result display screen 1500 that displays a graph of the transition of future predicted values of the number of inquiries by function. [Figure 16] FIG. is a diagram showing a pre- and post-modification display screen 1600 of a prediction result, which is the result of the predicted number of cases before and after modification of each function (here, for example, functions AAA, BBB, and CCC) constituting a software product. [Figure 17] FIG. is a diagram showing an internal configuration example of a function master 10134. [Figure 18] FIG. is a diagram showing an internal configuration example of a calendar master 10135. [Figure 19] FIG. is a diagram showing an internal configuration example of an event master 10136. [Figure 20] FIG. is a diagram showing an internal configuration example of a usage frequency master 2000. [Figure 21] FIG. is a diagram showing an internal configuration example of a document quality master 10137. [Figure 22] FIG. is a diagram showing an internal configuration example of a software quality master 10138. [Figure 23] FIG. is a diagram showing an internal configuration example of a used technology master 10139. [Figure 24] FIG. is a flowchart for explaining the function-by-function inquiry count calculation process (only for the first time). [Figure 25] FIG. is a flowchart for explaining the function-by-function inquiry count calculation process (daily). [Figure 26] FIG. is a flowchart for explaining the document quality evaluation process. [Figure 27A] FIG. is a flowchart for explaining the software quality evaluation process. [Figure 27B] FIG. is a flowchart (continuation of FIG. 27A) for explaining the software quality evaluation process. [Figure 28] FIG. is a flowchart for explaining the inquiry count prediction process.

BEST MODE FOR CARRYING OUT THE INVENTION

[0010] This embodiment will be described below with reference to the attached drawings. In the attached drawings, functionally identical elements may be shown with the same number, and redundant explanations will be omitted. The attached drawings show specific embodiments and examples in accordance with the principles of this disclosure, but these are for the purpose of understanding this disclosure and are not to be used in any way to restrict the interpretation of this disclosure.

[0011] While this embodiment is described in sufficient detail for those skilled in the art to implement the disclosure, it is important to understand that other implementations and forms are possible, and that the configuration and structure can be modified and various elements replaced without departing from the scope and spirit of the technical idea of ​​this disclosure. Therefore, the following description should not be construed as limiting to this.

[0012] <Example hardware configuration for inquiry volume prediction system 100> Figure 1 shows a schematic example of the inquiry count prediction system 100 according to an embodiment of the present disclosure.

[0013] The inquiry volume prediction system 100 is a system that predicts the future number of inquiries, taking into account the specific circumstances of each software product. For example, it includes a server device 101 composed of a computer, a recording medium reader 103 that reads data stored in a recording medium 102, an input device 104 composed of a keyboard, touch panel, mouse, etc., an output display device 105 composed of a display or printer, etc. that outputs the inquiry prediction results, a plurality of client devices 106_1 to 106_N (computers of software product users), and a network 107 that connects the server device 101 to each of the client devices 106_1 to 106_N.

[0014] The server device 101 includes a main memory 1011 for storing a program for query prediction processing, an arithmetic unit (composed of a processor such as a CPU or GPU) 1012 for reading and executing the program for query prediction processing from the main memory 1011, an auxiliary memory 1013 for storing various data and parameters, and a communication device 1014 for communicating with each client device 106_1 to 106_N.

[0015] The arithmetic unit 1012 reads various programs corresponding to the flowcharts described later (see Figures 24 to 28) from the main memory 1011 and expands them into internal memory (not shown) to generate each processing unit (see Figure 2).

[0016] <Various programs stored in the main memory 1011 of the server device 101 and various data stored in the auxiliary memory 1013> Figure 2 shows various programs stored in the main memory 1011 and various data (example of software configuration) stored in the auxiliary memory 1013 of the server device 101 according to this embodiment.

[0017] (i) The program held in the main memory 1011 is read by the arithmetic unit 1012 and expanded into the internal memory (not shown) of the arithmetic unit to construct various processing units. These various functional units include a query count prediction processing unit 10111, a function-specific query count calculation processing unit (first time only) 10112, a document quality evaluation processing unit 10113, a query management unit 10114, a function-specific query count calculation processing unit (daily) 10115, and a software quality evaluation processing unit 10116.

[0018] The inquiry count prediction processing unit 10111 performs a process to predict the future number of inquiries based on past inquiry counts, software quality, and document quality.

[0019] The function-specific query count calculation processing unit (first time only) 10112 performs a process of classifying all of the accumulated past query data according to each function included in the software product.

[0020] The document quality evaluation processing unit 10113 performs a process to evaluate the quality of documentation such as software product manuals based on the amount of information provided in the documentation (whether the explanation of each function is sufficient, average, or insufficient).

[0021] The inquiry management unit 10114 responds to requests from software product development personnel (hereinafter referred to as developers) by registering software information and / or documentation, or information other than documentation and software information, and outputs evaluation results of documentation quality and software quality, as well as the number of inquiries by function (predicted results).

[0022] The daily inquiry count calculation processing unit 10115 calculates the difference between the inquiries received each day and the previous day, and then classifies the difference in inquiry data according to each function included in the software product.

[0023] The software quality evaluation processing unit 10116 checks the number of defects and evaluates the quality of the software product based on data (such as bug data) collected during the development process of each function of the target software product.

[0024] (ii) The auxiliary storage device 1013 holds the query information database (database: hereinafter referred to as "DB") 10131, the function-specific query information DB 10132, the prediction result DB 10133, the function master 10134, the calendar master 10135, the event master 10136, the document quality master 10137, the software quality master 10138, the technology used master 10139, and other information.

[0025] The query information DB 10131 holds query information (data) from clients (software product users and system engineers). The function-specific query information DB 10132 holds query data classified by function by the function-specific query count calculation processing unit (first time only) 10112 and the function-specific query count calculation processing unit (daily) 10115. The prediction results DB 10133 holds prediction results (predicted values) calculated by the query count prediction processing unit 10111.

[0026] Function Master 10134 holds definition information for various functions of the software product. Calendar Master 10135 holds calendar information. Event Master 10136 holds information (date and time) about software product version upgrade events for each client, as well as information (date and time) about the release of the software product itself.

[0027] The document quality master 10137 holds the evaluation results for each document evaluated by the document quality evaluation processing unit 10113. The software quality master 10138 holds the quality evaluation results for each software product evaluated by the software quality evaluation processing unit 10116. The technology usage master 10139 holds information indicating the difficulty level of the technologies (e.g., programming languages) used in each software product.

[0028] <Sequence of Quality Evaluation Process> (i) Figure 3A shows the processing sequence from the request for registration of software information and / or documents to the output of document quality evaluation results and / or software quality evaluation results.

[0029] Developer 301 uses the input device 104 to input document information and / or software information, along with a registration request, to the server device 101 from the prediction data editing screen (see Figure 4) displayed on the output display device 105.

[0030] In response to a registration request from the developer 301, the inquiry management unit 10114 stores the received document information and / or software information in the document quality master 10137 and software quality master 10138 of the auxiliary storage device 1013, respectively. The inquiry management unit 10114 also instructs the document quality evaluation processing unit 10113 and / or software quality evaluation processing unit 10116 (hereinafter sometimes simply referred to as the "quality evaluation processing unit") to evaluate the quality of newly acquired software information and document information (including updated information).

[0031] The quality evaluation processing unit evaluates the quality of added or updated documents by referring to the document information (Figure 10) and appends the evaluation results to the document information in the document quality master 10137. Similarly, the quality evaluation processing unit evaluates the quality of added or updated software products by referring to the software information (Figure 12) and appends the evaluation results to the software information in the software quality master 10138. These quality evaluation processes will be described in detail later with reference to Figures 26 and 27.

[0032] The quality evaluation processing unit notifies the inquiry management unit 10114 that the update of the evaluation results is complete and the evaluation results are available. Upon receiving the notification, the inquiry management unit 10114 outputs the evaluation results to the display screen of the output display device 105.

[0033] (ii) Figure 3B shows the processing sequence from the registration request for information other than software information and documentation to the output of the query count prediction result.

[0034] Developer 301 uses the input device 104 to input information other than document information and software information to the server device 101, along with a registration request, from the prediction data editing screen (see Figure 4) displayed on the output display device 105. Here, information other than document information and software information includes event information such as software product information, information on the functions of software products, and information on the technologies used in software products.

[0035] In response to a registration request from the developer 301, the inquiry management unit 10114 stores information other than the received document information and software information in the function master 10134 and event master 10136 of the auxiliary storage device 1013, respectively. The inquiry management unit 10114 also notifies the inquiry count prediction processing unit 10111 that new information other than software information and document information has been acquired (including updates).

[0036] Upon receiving the notification, the inquiry count prediction processing unit 10111 instructs the function-specific inquiry count calculation processing unit (first time only) 10112 or the function-specific inquiry count calculation processing unit (daily) 10115 (hereinafter sometimes referred to as the "function-specific inquiry count calculation processing unit") to classify the number of inquiries by function.

[0037] The function-specific query count calculation processing unit classifies at least one query into a query for each function, calculates the number of queries for each function, and updates the function-specific query information DB 10132. Then, the function-specific query count calculation processing unit notifies (returns) the daily number of queries for each function to the query count prediction processing unit 10111.

[0038] The query count prediction processing unit 10111 uses existing technologies such as OSS (Open Source Software: for example, Prophet) to predict the daily number of queries in the future (for example, up to several years from now) (calculates predicted values). Then, the query count prediction processing unit 10111 modifies the predicted values ​​calculated using existing technologies based on the quality of the software product and the quality of the documentation, and stores the modified predicted values ​​in the prediction result DB 10133. The query count prediction processing unit 10111 also returns the predicted values ​​of the number of queries by function (prediction results) to the query management unit 10114.

[0039] The inquiry management unit 10114 graphs the trend in the number of inquiries (see Figure 15) and outputs the predicted value and / or the graph of the trend in the number of inquiries to the output display device 105.

[0040] <Example of the configuration of the forecast data editing screen> Figure 4 shows an example of the configuration of the prediction data editing screen 400. The prediction data editing screen 400 includes a function classification editing button 401, an event information editing button 402, a document information editing button 403, a software information editing button 404, and a technology usage editing button 405.

[0041] The function classification editing button 401 is pressed when registering a function of the software. When this button is pressed, the function classification editing screen 500 (see Figure 5) is displayed.

[0042] The event information editing button 402 is pressed when registering event information. When this button is pressed, the event information editing screen 700 (see Figure 7) is displayed.

[0043] The document information editing button 403 is pressed when registering document information. When this button is pressed, the document information editing screen 900 (see Figure 9) is displayed.

[0044] The software information editing button 404 is pressed when registering software information. When this button is pressed, the software information editing screen 1100 (see Figure 11) is displayed.

[0045] The "Edit Used Technology" button 405 is pressed when registering information about the technology being used. When this button is pressed, the "Edit Used Technology" screen 1300 (see Figure 13) is displayed.

[0046] <Example configuration of Function Classification Editing Screen 500> Figure 5 shows an example of the configuration of the function classification editing screen 500. The function classification editing screen 500 includes a function name input area 501, a usage frequency input area 502, a register button 503, a back button 504, and a list display button 505.

[0047] The function name input area 501 is an area for the developer 301 to input the function name of the target software product. The usage frequency input area 502 is an area for the developer 301 to input information on the usage frequency of the function entered in the function name input area 501 (e.g., high, medium (normal), low) based on a given criterion. An example of a high-frequency function is a login screen. An example of a low-frequency function is a function used only by a specific user (client) (customization function). A medium-frequency function is a function other than high-frequency and low-frequency functions.

[0048] The registration button 503 is used to register the information entered in the function name input area 501 and the usage frequency input area 502 into the function master 10134.

[0049] The back button 504 is used to return to the previous screen (prediction data editing screen 400).

[0050] The list display button 505 is a button used to display the function classification list screen 600 (see Figure 6).

[0051] <Example configuration of the function classification list screen 600> Figure 6 shows an example of the configuration of the function classification list screen 600. The function classification list screen 600 includes a function classification list display area containing the function name 601, usage frequency 602, and document name 603, as well as an edit button 604 and a back button 605.

[0052] Function name 601 and usage frequency 602 indicate the function name and usage frequency information of the software product entered according to the function classification editing screen 500.

[0053] Document name 603 indicates information about a document (such as a description of the function) related to the registered function (function name 601). The name of this related document corresponds to the document name registered as a document related to the function name by the document information editing screen 900 described later. For example, the C Guide is a manual that covers the entire software, so it is registered across multiple functions.

[0054] The edit button 604 is used to display the function classification editing screen 500 again. The back button 605 is used to return to the prediction data editing screen 400 (Figure 4).

[0055] <Event Information Editing Screen 700> Figure 7 shows an example of the configuration of the event information editing screen 700. The event information editing screen 700 includes an event name input area 701 for entering the event name, a function name input area 702 for entering the function name related to the event, an event scheduled date input area 703 for entering the scheduled date of the event, a register button 704, a back button 705, and a list display button 706.

[0056] Event name input area 701 is an area for entering the name of an event such as the addition of a new feature or the updating / improvement of a feature.

[0057] The function name input area 702 is an area for entering the name of the function to be added, updated, or improved.

[0058] Event schedule date input area 703 is for entering the scheduled date for adding new features or updating / improving existing features.

[0059] The registration button 704 is used to register the input content to the event master 10136.

[0060] The back button 705 is used to return to the previous screen (prediction data editing screen 400).

[0061] The list display button 706 is used to display the event information list screen 800 (see Figure 8).

[0062] <Example configuration of event information list screen 800> Figure 8 shows an example of the configuration of the event information list screen 800. The event information list screen 800 includes an event information list display area containing the event ID 801, event name 802, function name 803, and scheduled date 804, as well as an edit button 805 and a back button 806.

[0063] Event ID 801 is information used to uniquely identify and specify the registered event. Event name 802, function name 803, and scheduled date 804 indicate the event name, function name, and scheduled date information of the software product entered according to the event information editing screen 700.

[0064] The edit button 805 is used to display the event information editing screen 700 again. The back button 806 is used to return to the prediction data editing screen 400 (Figure 4).

[0065] <Example configuration of document information editing screen 900> Figure 9 shows an example of the configuration of the document information editing screen 900. The document information editing screen 900 includes a document name input area 901 for entering the document name, a material path input area 902 for entering the material path of the document, a function name input area 903 for entering the name of the function, a register button 904, a back button 905, and a list display button 906.

[0066] The document name input area 901 is an area for entering the name of a new or updated document.

[0067] The document path input area 902 contains information indicating the location where a new or updated document is saved.

[0068] The function name input area 903 is an area for entering the name of the function associated with the new or updated document to be registered.

[0069] The registration button 904 is used to register the input content to the document quality master 10137.

[0070] The back button 905 is used to return to the previous screen (prediction data editing screen 400).

[0071] The list display button 906 is used to display the document information list screen 1000 (see Figure 10).

[0072] <Example configuration of document information list screen 1000> Figure 10 shows an example of the configuration of the document information list screen 1000. The document information list screen 1000 includes a document information list display area that includes the document name 1001, material path 1002, function name 1003, and evaluation 1004, as well as an edit button 1005 and a back button 1006.

[0073] Document name 1001, material path 1002, and function name 1003 correspond to the information on the document name, material path, and function name of the software product entered according to the document information editing screen 900.

[0074] Evaluation 1004 is an area that stores the evaluation value of the target document calculated by the document quality evaluation processing unit 10113.

[0075] The edit button 1005 is used to display the document information editing screen 900 again. The back button 1006 is used to return to the prediction data editing screen 400 (Figure 4).

[0076] <Example configuration of software information editing screen 1100> Figure 11 shows an example of the configuration of the software information editing screen 1100. The software information editing screen 1100 includes a function name input area 1101 for entering the function names included in the software product, a development scale input area 1102 for entering the development scale (ks) of the target function, a defect count input area 1103 for entering the number of defects (number of defects) in the target function, a technology usage input area 1104 for entering the technologies used, a defect density threshold input area 1105 for entering the defect density threshold (defects / ks), a register button 1106, a back button 1107, and a list display button 1108.

[0077] The function name input area 1101 is an area for entering the name of a new function or an updated / improved function.

[0078] The development scale input area 1102 is an area for inputting the development scale (number of lines of code related to the function) of a new function or an updated / improved function.

[0079] The defect count input area 1103 is an area for inputting the number of defects (defect count) discovered during the development process of new functions or updated / improved functions.

[0080] The technology input area 1104 is an area for inputting the technologies used (such as the programming language) when creating a function.

[0081] The defect density threshold input area 1105 is an area for inputting a threshold that serves as the basis for calculating the evaluation of new functions or updated / improved functions.

[0082] The registration button 1106 is used to register the input content to the software quality master 10138.

[0083] The back button 1107 is used to return to the previous screen (prediction data editing screen 400).

[0084] The list display button 1108 is a button used to display the software information list screen 1200 (see Figure 12).

[0085] <Example configuration of software information list screen 1200> Figure 12 shows an example of the configuration of the software information list screen 1200. The software information list screen 1200 includes a software information list display area that includes the function name 1201, development scale 1202, number of defects 1203, defect density 1204, threshold 1205, technology used 1206, and evaluation 1207, as well as an edit button 1208 and a back button 1209.

[0086] The function name 1201, development scale 1202, number of defects 1203, technology used 1206, and threshold 1205 correspond to the function name of the software product, the development scale (ks), the number of defects (items), the technology used, and the defect density threshold (items / ks) of the function, which are entered according to the software information editing screen 1100.

[0087] Evaluation 1207 is an area that stores the evaluation value of the target software (or target function) calculated by the software quality evaluation processing unit 10116.

[0088] The edit button 1208 is used to display the software information editing screen 1100 again. The back button 1209 is used to return to the prediction data editing screen 400 (Figure 4).

[0089] <Example configuration of the 1300 editing screen for used technology> Figure 13 shows an example of the configuration of the technology usage editing screen 1300. The technology usage editing screen 1300 includes a technology usage name input area 1301 for entering the technology usage name, a rank input area 1302 for entering the rank of the technology usage, a register button 1303, a back button 1304, and a list display button 1305.

[0090] The input field 1301 for the name of the technology used is for entering the programming language used when creating a new function or an updated / improved function.

[0091] Rank input area 1302 is an area for inputting the rank (difficulty level) of the programming language used when creating a new function or an updated / improved function.

[0092] The registration button 1303 is used to register the input content in the technology master 10139.

[0093] The back button 1304 is used to return to the previous screen (prediction data editing screen 400).

[0094] The list display button 1305 is a button for displaying the list of technologies used screen 1400 (see Figure 14).

[0095] <Example configuration of the "List of Technologies Used" screen 1400> Figure 14 shows an example of the configuration of the list of technologies used screen 1400. The list of technologies used screen 1400 includes a display area for the list of technologies used, which includes the technology name 1401 and rank 1402, as well as an edit button 1403 and a back button 1404.

[0096] The technology name 1401 and rank 1402 correspond to the name of the technology used and the rank information of the technology used when creating the software product, as entered according to the technology editing screen 1300.

[0097] The edit button 1403 is used to display the technology editing screen 1300 again. The back button 1404 is used to return to the prediction data editing screen 400 (Figure 4).

[0098] <Example of the prediction result display screen 1500> Figure 15 shows the prediction result display screen 1500, which displays a graph showing the trend of future predicted values ​​for the number of inquiries by function. The prediction result display screen 1500 shows, for example, the trend of future predicted values ​​for the number of inquiries by function when the inquiry count prediction process according to this embodiment is executed on January 31, 2024. Figure 15 also shows the trend of predicted values ​​for the number of inquiries for function AAA1501, function BBB1502, and function CCC1503.

[0099] For each function, there are several days when the predicted number of inquiries increases, which is due to events occurring. Therefore, it can be seen that the predicted value stabilizes once the event ends. Furthermore, since the predicted number of inquiries for function AAA1501 does not fluctuate much, it can be seen that function AAA1501 is a stable function and does not require frequent updates or events. Function CCC1503 is a function that requires frequent updates (a function that can appeal to users) and events occur relatively frequently. Function BBB1502 can be seen as having properties intermediate between functions AAA1501 and CCC1503.

[0100] <Regarding the revisions to the prediction results> Figure 16 shows a screen 1600 displaying the prediction results before and after correction, which are the predicted number of occurrences before and after correction for each function (here, for example, functions AAA, BBB, and CCC) that make up the software product.

[0101] The screen 1600 displaying the prediction results before and after correction includes the date 1601, the function name 1602, the number of predictions before correction 1603, and the number of predictions after correction 1604 as constituent items.

[0102] The date 1601 indicates the date of the predicted value. In other words, this is information for the target date (in this example, February 1st to 3rd, 2024) for which the predicted value was calculated based on query information up to the prediction reference date (January 31st, 2024 in this example).

[0103] Function name 1602 is information indicating the names of each function that make up the target software product. In this example, functions AAA, BBB, and CCC are shown, but the number of functions is not limited to three.

[0104] The uncorrected predicted number of 1603 represents the number of predictions calculated using conventional prediction technology (for example, Prophet mentioned above).

[0105] The revised predicted number of 1604 represents the predicted number obtained by correcting the original predicted number of 1603 using the query count prediction process according to this embodiment (see Figure 28 below).

[0106] <Example configuration of Function Master 10134> Figure 17 shows an example of the internal structure of the function master 10134. The function master 10134 includes the function ID 1701, function name 1702, usage frequency ID 1703, and document ID 1704 as its constituent items.

[0107] Function ID 1701 is information used to uniquely identify and specify each function that makes up the target software product. Function name 1702 is information that indicates the name of each function that makes up the target software product.

[0108] Usage frequency ID 1703 is identification information indicating the frequency of use of each function (see Figure 20). Document ID 1794 is identification information for the document containing the description of each function.

[0109] <Example configuration of Calendar Master 10135> Figure 18 shows an example of the internal structure of Calendar Master 10135. Calendar Master 10135 includes the following components: Year 1801, Month 1802, Day 1803, Day of the Week 1804, Event ID 1805, and Remarks 1806.

[0110] The year 1801, month 1802, day 1803, and day of the week 1804 specify the year, month, day, and day of the week in the calendar.

[0111] Event ID 1805 is information used to identify and specify the event being held, and is entered in the fields corresponding to the date and day of the week in which the event will take place. Remark 1806 is information that indicates, for example, whether support is needed.

[0112] <Example configuration of Event Master 10136> Figure 19 shows an example of the internal structure of event master 10136. Event master 10136 includes the following configuration items: event ID 1901, event name 1902, function ID 1903 of the related function, and scheduled event occurrence date 1904.

[0113] Event ID 1901 is information used to identify and specify the event being held. Event Name 1902 is information indicating the name of the event being held.

[0114] The related function ID 1903 is information used to identify the function to which the event is related. The scheduled event date 1904 is information about the date on which the event is scheduled to take place.

[0115] <Example configuration of the usage frequency master 2000> Figure 20 shows an example of the internal configuration of the usage frequency master 2000. The usage frequency master 2000 is one of the databases held in the auxiliary storage device 1013, although it is not shown in Figure 2.

[0116] The usage frequency master 2000 includes usage frequency ID 2001 and rank 2002 as constituent items.

[0117] Usage frequency ID 2001 is information used to uniquely identify and specify the usage frequency rank. Rank 2002 is information used to indicate the usage frequency of each function as high, medium, or low. As explained in Figure 6, functions with "high" usage frequency include the login screen, while functions with "low" usage frequency include functions used only by specific clients. Functions with "medium" usage frequency include functions other than those with "high" and "low" usage frequency.

[0118] <Example of Document Quality Master 10137 Configuration> Figure 21 shows an example of the internal structure of the document quality master 10137. The document quality master 10137 includes the following components: document ID 2101, document name 2102, material path 2103, target function ID 2104, and evaluation 2105.

[0119] Document ID 2101 is information used to uniquely identify and specify the document being managed. Document Name 2102 is information indicating the name of the document being managed. Document Path 2103 is information indicating the location where the document being managed is stored. Target Function ID 2104 is information indicating the function to which the document being managed is related (the function described by the document).

[0120] Evaluation 2105 is information that shows the result of the document quality evaluation process (see Figure 26). Evaluation 2105 is expressed on a scale of, for example, 5 levels (1: low evaluation; 5: high evaluation).

[0121] <Example configuration of Software Quality Master 10138> Figure 22 shows an example of the internal structure of the software quality master 10138. The software quality master 10138 includes the following components: function ID 2201, development scale 2202, number of defects 2203, threshold 2204, technology used ID 2205, and evaluation 2206.

[0122] Function ID 2201 is information used to uniquely identify and specify the function being managed. Development size 2202 is information indicating the development size (ks) of the function being managed. Number of defects 2203 is information indicating the number of defects found during the development process of the function being managed. Threshold 2204 is information indicating the threshold used as the basis for calculating the evaluation of the function being managed. The higher the defect density is above this threshold, the lower the evaluation will be.

[0123] Technology ID 2205 indicates the programming language used to implement the managed function. Evaluation 2206 indicates the results of the software quality evaluation process (see Figures 27A and B).

[0124] <Example configuration of the used technology master 10139> Figure 23 shows an example of the internal structure of the Technology Usage Master 10139. The Technology Usage Master 10139 includes Technology Usage ID 2301, Technology Usage Name 2302, and Rank 2303 as its constituent items.

[0125] The Technology Used ID 2301 is information used to uniquely identify the technology used when creating each function. The Technology Used Name 2302 is information indicating the name of the technology being managed. The Rank 2303 is information indicating the rank (difficulty level) of the technology being managed.

[0126] <Details of the process for calculating the number of inquiries by function (first time only)> Figure 24 is a flowchart illustrating the process for calculating the number of queries by function (first time only). The process for calculating the number of queries by function (first time only) is executed when calculating the number of queries by function for the first time with respect to the query information stored in query information DB10131.

[0127] (i) Step S2401 The function-specific query count calculation processing unit (first time only) 10112 initializes the loop count I to 0.

[0128] (ii) Step S2402 The function-specific query count calculation processing unit (first time only) 10112 initializes the maximum loop count Imax to 0.

[0129] (iii) Step S2403 The function-specific query count calculation processing unit (first time only) 10112 initializes the loop count J to 0.

[0130] (iv) Step S2404 The function-specific query count calculation processing unit (first time only) 10112 initializes the maximum loop count Jmax to 0.

[0131] (v) Step S2405 The function-specific inquiry count calculation processing unit (first time only) 10112 refers to the function master 10134 and obtains information on a list of functions that make up the software product to be processed.

[0132] (vi) Step S2406 The function-specific query count calculation processing unit (first time only) 10112 refers to the query information DB 10131 and retrieves query information received on each day up to the day before the day the processing is being executed.

[0133] (vii) Step S2407 The function-specific inquiry count calculation processing unit (first time only) 10112 calculates the number of days for the inquiry information obtained in step S2406.

[0134] (viii) Step S2408 The function-specific inquiry count calculation processing unit (first time only) 10112 sets the number of days calculated in step S2407 to Jmax.

[0135] (ix) Step 2409 The function-specific inquiry count calculation processing unit (only for the first time) 10112 determines whether J < Jmax. If J < Jmax (Yes in step S2409), the process proceeds to step S2410. If J ≧ Jmax (No in step S2409), since the calculation of the function-specific inquiry count for all inquiry information is completed, the process ends.

[0136] (x) Step S2410 The function-specific inquiry count calculation processing unit (only for the first time) 10112 sets Imax to the inquiry count on the (k + 1)-th day (k = 10 to Jmax).

[0137] (xi) Step S2411 The function-specific inquiry count calculation processing unit (only for the first time) 10112 determines whether I < Imax. If I < Imax (Yes in step S2411), the process proceeds to step S2412. If I ≧ Imax (No in step S2411), the process proceeds to step S2415.

[0138] (xii) Step S2412 The function-specific inquiry count calculation processing unit (only for the first time) 10112 refers to the function related to the inquiry information to be processed. For example, the function-specific inquiry count calculation processing unit (only for the first time) 10112 checks which function the m-th (1 ≦ m ≦ Imax) inquiry on a certain date in 2024 is related to.

[0139] (xiii) Step S2413 The function-specific inquiry count calculation processing unit (only for the first time) 10112 adds 1 to the inquiry count of the corresponding function.

[0140] (xiv) Step S2414 The function-specific inquiry count calculation processing unit (only for the first time) 10112 increments I by I + 1 and executes the process from step S2411 again.

[0141] (xv) Step S2415 The function-specific query count calculation processing unit (first time only) 10112 increments J to J+1 and executes the process from step S2409 again.

[0142] <Details of the daily calculation process for the number of inquiries by function> Figure 25 is a flowchart illustrating the daily process for calculating the number of queries by function. The daily process for calculating the number of queries by function is executed after the initial execution of the daily process for calculating the number of queries by function, based on the query information (differences) newly accumulated in the query information DB10131. This daily process for calculating the number of queries by function may be executed daily or at predetermined intervals.

[0143] (i) Step S2501 The daily query count calculation unit 10115 initializes the loop count I to 0.

[0144] (ii) Step S2502 The daily query count calculation unit 10115 for each function initializes the maximum loop count Imax to 0.

[0145] (iii) Step S2503 The daily inquiry count calculation processing unit 10115 refers to the function master 10134 and obtains information on a list of functions that make up the software product to be processed.

[0146] (iv) Step S2504 The daily query count calculation processing unit 10115 refers to the query information DB 10131 and, after executing the previous daily query count calculation process, retrieves the query information received on the day before the day the daily query count calculation process is executed.

[0147] (v) Step S2505 The function-specific inquiry count calculation processing unit (daily) 10115 sets the number of inquiries obtained in step S2504 to Imax.

[0148] (vi) Step 2506 The function-specific inquiry count calculation processing unit (daily) 10115 determines whether I < Imax. If I < Imax (Yes in step S2506), the process proceeds to step S2507. If I ≥ Imax (No in step S2506), since the calculation of the function-specific inquiry count for all inquiry information is complete, the process ends.

[0149] (vii) Step S2507 The function-specific inquiry count calculation processing unit (daily) 10115 refers to the function related to the inquiry information to be processed. For example, the function-specific inquiry count calculation processing unit (daily) 10115 checks which function the nth (1 ≤ n ≤ Imax) inquiry on △ / △ / 2024 is related to.

[0150] (viii) Step S2508 The function-specific inquiry count calculation processing unit (daily) 10115 adds 1 to the inquiry count of the corresponding function.

[0151] (ix) Step S2509 The function-specific inquiry count calculation processing unit (daily) 10115 increments I by 1 (I = I + 1) and executes again the process starting from step S2506.

[0152] <Details of Document Quality Evaluation Processing> Figure 26 is a flowchart for explaining the document quality evaluation processing.

[0153] (i) Step S2601 The document quality evaluation processing unit 10113 initializes the loop count I to 0.

[0154] (ii) Step S2602 The document quality evaluation processing unit 10113 initializes the maximum value Imax of the loop count to 0.

[0155] (iii) Step S2603 The document quality evaluation processing unit 10113 refers to the document information in the document quality master 10137 and acquires the document path 1002 of the document to be quality-evaluated.

[0156] (iv) Step S2604 The document quality evaluation processing unit 10113 refers to the function master 10134 (Fig. 21) and acquires the function ID 1701 related to the document to be quality-evaluated. Also, the document quality evaluation processing unit 10113 counts the number of function IDs related to the document to be quality-evaluated.

[0157] (v) Step S2605 The document quality evaluation processing unit 10113 sets the number of function IDs acquired in Step S2604 to Imax.

[0158] (vi) Step S2606 The document quality evaluation processing unit 10113 determines whether I < Imax. If I < Imax (Yes in Step S2606), the process proceeds to Step S2607. If I ≥ Imax (No in Step S2606), since the evaluation of the documents corresponding to all function IDs has been completed, the process ends.

[0159] (vii) Step S2607 The document quality evaluation processing unit 10113 measures the amount of description of the corresponding function.

[0160] (viii) Step S2608 The document quality evaluation processing unit 10113 calculates the ratio of the amount of description of the corresponding function to the total amount of the document.

[0161] (ix) Step S2609 The document quality evaluation processing unit 10113 calculates the quality of the document to be evaluated based on the proportion of the amount of information calculated in step S2608 and a preset criterion (for example, whether the proportion of the amount of information P is greater than a predetermined threshold).

[0162] (x) Step S2610 The document quality evaluation processing unit 10113 increments I to I+1 (I=I+1) and executes the process from step S2606 again.

[0163] <Details of the software quality evaluation process> Figures 27A and 27B are flowcharts illustrating the software quality evaluation process.

[0164] (i) Step S2701 The software quality evaluation processing unit 10116 refers to the software quality master 10138 (Figure 22) and obtains information on the development scale 2202 of the function to be evaluated.

[0165] (ii) Step S2702 The software quality evaluation processing unit 10116 refers to the software quality master 10138 (Figure 22) and obtains information on the number of defects (2203) for the function being evaluated.

[0166] (iii) Step S2703 The software quality evaluation processing unit 10116 calculates the defect density = number of defects / development scale from the development scale information obtained in step S2701 and the number of defects information obtained in step S2702.

[0167] (iv) Step S2704 The software quality evaluation processing unit 10116 refers to the software quality master 10138 (Figure 22) and obtains information on the threshold 2204.

[0168] (v) Step S2705 The software quality evaluation processing unit 10116 refers to the software quality master 10138 (Figure 22) to obtain the technology usage ID 2205, and then refers to the technology usage master 10139 (Figure 23) to obtain information on the technology usage corresponding to that technology usage ID (including rank 2303).

[0169] (vi) Step S2706 The software quality evaluation processing unit 10116 compares the defect density calculated in step S2703 with the threshold obtained in step S2704 to determine whether the defect density > threshold. If the defect density > threshold (if Yes in S2706), the process proceeds to S2707. If the defect density ≤ threshold (if No in step S2706), the process proceeds to S2710.

[0170] (vii) Step S2707 The software quality evaluation processing unit 10116 checks the rank of the technology used, which was obtained in step S2705. If the rank of the technology used is "1 (low)", the process proceeds to step S2708. If the rank of the technology used is "2 (medium)" or "3 (high)", the process proceeds to step S2709.

[0171] (viii) Step S2708 The software quality evaluation unit 10116 sets the evaluation value of the function being evaluated to 1 (very poor quality).

[0172] (ix) Step S2709 The software quality evaluation processing unit 10116 sets the evaluation value of the function being evaluated to 2 (poor quality).

[0173] (x) Step S2710 The software quality evaluation processing unit 10116 determines whether the defect density equals the threshold. If the defect density equals the threshold (if Yes in step S2710), the process proceeds to step S2711. If the defect density is less than the threshold (if No in step S2710), the process proceeds to step S2715.

[0174] (xi) Step S2711 The software quality evaluation processing unit 10116 checks the rank of the technology used, which was obtained in step S2705. If the rank of the technology used is "3 (high)", the process proceeds to step S2712. If the rank of the technology used is "2 (medium)", the process proceeds to step S2713. If the rank of the technology used is "1 (low)", the process proceeds to step S2714.

[0175] (xii) Step S2712 The software quality evaluation processing unit 10116 sets the evaluation value of the function being evaluated to 4 (good quality).

[0176] (xiii) Step S2713 The software quality evaluation processing unit 10116 sets the evaluation value of the function to be evaluated to 3 (fair quality).

[0177] (xiv) Step S2714 The software quality evaluation processing unit 10116 sets the evaluation value of the function being evaluated to 2 (poor quality).

[0178] (xv) Step S2715 The software quality evaluation processing unit 10116 determines whether the rank of the technology used, obtained in step S2705, is "3 (high)". If the rank of the technology used is "3 (high)" (Yes in step S2715), the process proceeds to step S2716. If the rank of the technology used is not "2 (high)" (No in step S2715), the process proceeds to step S2717.

[0179] (xvi) Step S2716 The software quality evaluation processing unit 10116 sets the evaluation value of the function being evaluated to 4 (good quality).

[0180] (xvii) Step S2717 The software quality evaluation processing unit 10116 compares the defect density calculated in step S2703 with half of the threshold (threshold / 2) obtained in step S2704 to determine whether the defect density < threshold / 2. If the defect density < threshold / 2 (if Yes in S2717), the process proceeds to S2718. If the defect density ≥ threshold / 2 (if No in step S2717), the process proceeds to S2719.

[0181] (xviii) Step S2718 The software quality evaluation unit 10116 sets the evaluation value of the function being evaluated to 5 (very good quality).

[0182] (xix) Step S2719 The software quality evaluation processing unit 10116 sets the evaluation value of the function being evaluated to 4 (good quality).

[0183] <Details of the inquiry volume prediction process> Figure 28 is a flowchart illustrating the process of predicting the number of inquiries.

[0184] (i) Step S2801 The inquiry count prediction processing unit 10111 determines whether this inquiry count prediction process is being performed for the first time. If it is the first time, the process proceeds to step S2802. If it is not the first time (it is the second or subsequent time), the process proceeds to step S2803.

[0185] (ii) Step S2802 The query count prediction processing unit 10111 instructs the function-specific query count calculation processing unit (first time only) 10112 to execute the function-specific query count calculation process (first time only) shown in Figure 24, and receives the processing result.

[0186] (iii) Step S2803 The query count prediction processing unit 10111 instructs the daily query count calculation processing unit 10115 by function to execute the daily query count calculation process by function shown in Figure 25, and receives the processing result.

[0187] (iv) Step S2804 The query count prediction processing unit 10111 uses the query count information by function, calendar information, and event information obtained in step S2802 or step S2803 to perform query count prediction processing by function (using existing technology: OSS (e.g., Prophet)) and predicts the daily query count for the future (e.g., up to several years from now) (calculation of baseline prediction value).

[0188] (v) Step S2805 The query count prediction processing unit 10111 stores the reference prediction value calculated in step S2804 in the prediction result DB 10133 of the auxiliary storage device 1013. This prediction value corresponds to the uncorrected predicted number of queries, 1603, shown in Figure 16.

[0189] (vi) Iteration The following steps S2806 to S2808 are repeated for the number of days in the forecast range, and the forecast result calculated in step S2804 (pre-correction forecast value: base forecast value) is corrected.

[0190] (vii) Step S2806 The inquiry count prediction processing unit 10111 obtains the quality evaluation value of the document related to the function to be predicted from the document quality master 10137, and modifies the prediction result (first prediction result value) by multiplying the predicted value calculated in step S2804 by the coefficient corresponding to the document evaluation value. The coefficient corresponding to the document evaluation value is predetermined and can be set so that the coefficient is "1.2" when the document quality is "1", "1.1" when the document quality is "2", "1.0" when the document quality is "3", "0.9" when the document quality is "4", and "0.8" when the document quality is "5". In other words, the coefficient can be set so that the number of inquiries is reduced as the document quality improves.

[0191] (viii) Step S2807 The query count prediction processing unit 10111 obtains the software quality evaluation value related to the function to be predicted from the software quality master 10138, and further modifies the prediction result (second prediction result) by multiplying the corrected prediction value (first prediction result value) obtained in step S2806 by a coefficient corresponding to the software evaluation value. The coefficient corresponding to the software evaluation value is also predetermined and can be set so that the coefficient is "1.2" when the software quality is "1", "1.1" when the software quality is "2", "1.0" when the software quality is "3", "0.9" when the software quality is "4", and "0.8" when the software quality is "5". In other words, the coefficient can be set so that the number of queries is corrected to be less as the software quality is better.

[0192] (ix) Step S2808 The inquiry count prediction processing unit 10111 obtains the usage frequency related to the function to be predicted from the function master 10134, and further modifies the prediction result (third prediction result) by multiplying the modified prediction value (second prediction result value) obtained in step S2807 by a coefficient corresponding to the usage frequency. The coefficient corresponding to the usage frequency is also predetermined and can be set to "1.1" when the usage frequency is "high", "1.0" when the usage frequency is "medium", and "0.9" when the usage frequency is "low". In other words, the coefficient can be set so that the number of inquiries is modified more as the usage frequency increases.

[0193] (x) Step S2809 The query count prediction processing unit 10111 stores the prediction result (corrected prediction value: third prediction result value) in the prediction result DB 10133.

[0194] <Other> The functions of this embodiment can also be realized by software program code. In this case, a storage medium containing the program code is provided to a system or device, and the computer (or CPU or MPU) of that system or device reads the program code stored on the storage medium. In this case, the program code read from the storage medium itself realizes the functions of the embodiment described above, and the program code itself and the storage medium that stores it constitute the present disclosure. Examples of storage media used to supply such program code include flexible disks, CD-ROMs, DVD-ROMs, hard disks, optical disks, magneto-optical disks, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, and the like.

[0195] Furthermore, based on the instructions in the program code, the operating system (OS) running on the computer may perform some or all of the actual processing, thereby realizing the functions of the embodiment described above. In addition, after the program code read from the storage medium is written to the computer's memory, the computer's CPU may perform some or all of the actual processing based on the instructions in the program code, thereby realizing the functions of the embodiment described above.

[0196] Furthermore, the program code for the software that realizes the functions of the embodiment may be distributed via a network and stored in a storage means such as a hard disk or memory of the system or device, or in a storage medium such as a CD-RW or CD-R, so that when in use the system or device's computer (or CPU or MPU) reads and executes the program code stored in the storage means or storage medium.

[0197] The processes and technologies described herein are not inherently related to any specific device and can be implemented by combining the components. Various types of general-purpose devices can also be added. Dedicated devices may be constructed to perform the functions of this embodiment. Furthermore, various functions can be formed by appropriately combining the multiple components disclosed in this embodiment. For example, some components may be removed from all the components shown in the embodiment, or components from different configurations may be appropriately combined.

[0198] This disclosure provides specific examples, which are for illustrative purposes (to understand the technology of this disclosure) and not for limitation in any respect. A person with ordinary skill in the art will likely understand that there are many combinations of hardware, software, and firmware suitable for implementing the technology of this disclosure. For example, the described software can be implemented in a wide range of programming or scripting languages, such as assembler, C / C++, Perl, Shell, PHP, and Java®.

[0199] Furthermore, in the embodiments described above, the control lines and information lines shown are those deemed necessary for illustrative purposes, and not all control lines and information lines are necessarily shown in the actual product. All components may be interconnected.

[0200] In addition, any person with ordinary skill in the art can see from the consideration of this embodiment that other implementations of this disclosure may be apparent. The specification and examples are typical, and the scope and spirit of the art of this disclosure are shown in the subsequent claims. [Explanation of Symbols]

[0201] 100 Inquiry Count Prediction System 101 Server device 102 Recording media 103 Recording medium reading device 104 Input device 105 Output display device 106_1 to 106_N Client Devices 107 Network 1011 Main storage 1012 Arithmetic equipment 1013 Auxiliary storage 1014 Communication devices 10111 Inquiry Count Prediction Processing Unit 10112 Function-specific inquiry count calculation processing unit (first time only) 10113 Document Quality Evaluation Processing Unit 10114 Inquiry Management Department 10115 Function-specific inquiry count calculation processing unit (daily) 10116 Software Quality Evaluation Processing Unit

Claims

1. A server device that predicts the number of future user inquiries for each function that constitutes a software product, A storage device that holds a program for predicting the number of future queries, The system includes a computer that reads the program from the storage device and performs a process to predict the number of future queries, The aforementioned storage device or another storage device includes a query information database that holds past query information up to the day before the day on which the process of predicting the number of future queries is performed, and a document quality database that holds quality evaluation values ​​for each document related to each of the aforementioned functions, The aforementioned computer, The process involves obtaining past query information from the query information database and calculating a baseline predicted value, which is a predicted value for the future number of queries for the function to be predicted, according to existing prediction techniques. The process involves obtaining quality evaluation values ​​for documents related to the function to be predicted from the document quality database, modifying the baseline prediction value based on the obtained document quality evaluation values, and calculating a first modified prediction value. A server device that performs the following actions.

2. In claim 1, The storage device or the other storage device further includes a software quality database that holds quality evaluation values ​​for each of the functions, The computer is a server device that further obtains quality evaluation values ​​for the function to be predicted from the software quality database, modifies the first corrected predicted value based on the obtained quality evaluation values ​​for the function, and calculates a second corrected predicted value.

3. In claim 2, The storage device or the other storage device further includes a function database that holds information on the frequency of use of each of the functions, The computer is a server device that obtains information on the frequency of use of the function to be predicted from the function database, modifies the second modified predicted value based on the obtained frequency of use information, and calculates the third modified predicted value.

4. In claim 1, The computer is a server device that classifies the past inquiry information by function, calculates the number of inquiries by function, and calculates the standard predicted value based on the number of inquiries by function of the function to be predicted.

5. In claim 1, A first correction coefficient corresponding to the quality evaluation value of the aforementioned document is defined in advance. The computer is a server device that calculates the first corrected predicted value by multiplying the reference predicted value by the first correction coefficient.

6. In claim 2, A second correction coefficient corresponding to the quality evaluation value of the aforementioned function is defined in advance. The computer is a server device that calculates the second corrected predicted value by multiplying the first corrected predicted value by the second correction coefficient.

7. In claim 3, A third correction coefficient is predetermined according to the frequency of use of the aforementioned function. The computer is a server device that calculates the third corrected predicted value by multiplying the second corrected predicted value by the third correction coefficient.

8. In claim 1, The computer is a server device that determines a quality evaluation value for each document based on the amount of information contained in each document related to each of the functions.

9. In claim 2, The computer is a server device that determines the quality evaluation value for each function based on the defect density value calculated using the development scale of each function and the number of defects during the development process of each function.

10. In claim 9, The computer is a server device that further determines a quality evaluation value for each of the functions based on the rank of the technology used to realize each of the functions and the value of the defect density.

11. A server device according to any one of claims 1 to 10, Multiple client devices, A network connecting the server device and the plurality of client devices, A system that predicts the number of inquiries.

12. A method for predicting the number of future inquiries from users regarding each function that constitutes a software product, using a computer, To provide a storage device that holds a program for predicting the number of future queries, The storage device or another storage device shall contain a query information database that holds past query information up to the day before the day on which the process of predicting the number of future queries is performed, and a document quality database that holds quality evaluation values ​​for each document related to each of the functions, The process involves obtaining past query information from the query information database, calculating a baseline predicted value which is a predicted value for the future number of queries for the function to be predicted, in accordance with existing prediction techniques, The process involves obtaining quality evaluation values ​​for documents related to the function to be predicted from the document quality database, modifying the baseline prediction value based on the obtained document quality evaluation values, and calculating the first modified prediction value. A method for predicting the number of inquiries, including the number of inquiries.

13. In claim 12, The storage device or the other storage device further includes a software quality database that holds quality evaluation values ​​for each of the functions, The computer further obtains quality evaluation values ​​for the function to be predicted from the software quality database, modifies the first corrected predicted value based on the obtained quality evaluation values ​​for the function, and performs a process to calculate a second corrected predicted value, thereby providing a method for predicting the number of queries.

14. In claim 13, The storage device or the other storage device further includes a function database that holds information on the frequency of use of each of the functions, A method for predicting the number of queries, wherein the computer obtains information on the frequency of use of the function to be predicted from the function database, modifies the second modified predicted value based on the obtained frequency of use information, and calculates the third modified predicted value.

15. In claim 12, The aforementioned computer classifies the past inquiry information by function, calculates the number of inquiries by function, and calculates the standard predicted value based on the number of inquiries by function of the function to be predicted, in an inquiry count prediction method.

16. In claim 12, A first correction coefficient corresponding to the quality evaluation value of the aforementioned document is defined in advance. The computer calculates the first corrected predicted value by multiplying the reference predicted value by the first correction coefficient, which is a method for predicting the number of inquiries.

17. In claim 13, A second correction coefficient corresponding to the quality evaluation value of the aforementioned function is defined in advance. The computer calculates the second corrected predicted value by multiplying the first corrected predicted value by the second correction coefficient, which is a method for predicting the number of inquiries.

18. In claim 14, A third correction coefficient is predetermined according to the frequency of use of the aforementioned function. The computer calculates the third corrected predicted value by multiplying the second corrected predicted value by the third correction coefficient, which is a method for predicting the number of inquiries.

19. In claim 12, A method for predicting the number of inquiries, wherein the computer determines a quality evaluation value for each document related to each of the functions described above, based on the amount of information contained in each document.

20. In claim 13, The computer determines the quality evaluation value for each function based on a defect density value calculated using the development scale of each function and the number of defects during the development process of each function, as a method for predicting the number of inquiries.

21. In claim 20, The computer further determines a quality evaluation value for each of the functions based on the rank of the technology used to realize each of the functions and the value of the defect density, as an inquiry count prediction method.

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