JP2009093277A - Estimation support system, estimation support method and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To easily perform setting, operation or the like of a large number of estimation risk factors, and to repeatedly perform investigation of estimation in real time. <P>SOLUTION: An input means 11-1, 11-2, ..., 11-n uses a pack allowing operation of linear movement, curve movement, rotational movement or the like by taking it in a hand of a user, and an input detection means 13 detects a position of each the pack and movement of a movement amount, a rotation amount or the like of each the pack, and transmits them to a host computer 5. The host computer 5 determines a parameter of the risk factor necessary for the estimation from the position and the movement of the input means 11, and calculates an estimation result or the like. A video of the estimation result or the like calculated by the host computer 5 is projected on the input detection means 13 by an output device 7, and the estimation result is investigated by use of the input detection means 13 and the input means 11. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to an estimate support system, an estimate support method, and a program for supporting estimate work for software development, product development work, and the like.

  Conventionally, various methods for supporting estimation work related to development work of products and software have been proposed. For example, Patent Document 1 describes a method of supporting a whole series of estimation operations such as software scale, development man-hours, and development costs by prompting worker input. Patent Document 2 describes an estimation system that can estimate indirect costs in software development relatively easily by comparison with a development cost estimation reference database. Further, Patent Document 3 describes a project management system for maintaining a high-precision work man-hour estimate necessary for schedule planning against changes in productivity in design and manufacturing work in product development.

JP 2003-263320 A JP 2007-11697 A JP 2002-109173 A

In software development, there are various factors that determine various estimates in addition to development man-hours and costs (hereinafter referred to as “estimated risk factors” or “risks”), and all these risk factors are considered. However, the prior art including the techniques described in Patent Document 1 to Patent Document 3 does not necessarily have a user interface suitable for easily operating a wide variety of estimated risk factors. There is a problem that the estimation work becomes complicated because it is not provided for the estimation work.
In addition, for estimation work, it is necessary to consider estimations in various positions and cases (changing the parameters of estimated risk factors), but in the conventional technology, the parameters of multiple risk factors are changed to be used in various cases. On the other hand, there is a problem that it is not suitable for considering repeated estimation in real time.
The present invention has been made in view of the above points, and by using a tangible user interface (TUI), it is possible to easily set and operate a large number of estimated risk factors, and repeatedly examine estimates in real time. It is an object of the present invention to provide an estimate support system, an estimate support method, and a program capable of performing the above.

  In order to achieve the above-described object, the present invention supports an estimation taking into account a plurality of risk factors, and detects the input means, the input detection means for detecting the position and the movement amount of the input means, and the input detection means. The selected risk judgment means for specifying and selecting the risk factor from the position of the input means, and the amount of movement of the input means detected by the input detection means with respect to the risk factor selected by the selection risk judgment means A risk calculation unit for calculating a risk fluctuation amount from the risk calculation unit, an estimate calculation unit for calculating an estimate using the risk fluctuation amount calculated by the risk calculation unit, and a calculation result by the estimate calculation unit on the input detection unit Output means for outputting to the output.

In the present invention, a tangible user interface is used as input means and input detection means for detecting the position and movement amount of the input means. For example, a pack that can be moved by the user is used as the input means, and the input detection means is a sense table that detects the position and movement amount of the pack. Here, the movement amount indicates a movement amount due to movement such as linear movement, curved line movement, and rotational movement. The output means is a video projector or the like, and the calculated estimation result is output on the input detection means. Further, the input means moves along the output, and the position and amount of movement are also detected by the input detection means. Will be.
As described above, according to the present invention, the user can easily change the parameters regarding the risk factors of a plurality of estimates, and the study of the estimate considering the change can be performed in real time.

The present invention further includes case storage means for storing the risk factor and a countermeasure plan corresponding to the risk factor, wherein the risk calculation means corresponds to the risk factor selected by the selected risk determination means. A measure plan is read from the case storage means, and an estimate is calculated in consideration of the read measure plan.
In addition, the input means has an identification number, the input detection means detects the position and movement amount of the input means for each identification number, and the selection risk judgment means uses the input detection means for each identification number. The risk factor is identified and selected from the detected position of the input means, and the risk calculation means calculates a risk fluctuation amount from the movement amount of the input means for each identification number with respect to the selected risk factor. The estimate calculating means calculates an estimate for each identification number using the calculated risk fluctuation amount, and the output means outputs the calculation result by the estimate calculating means for each identification number. It outputs on a detection means, It is characterized by the above-mentioned.
In this way, it is possible to easily examine the estimate by a plurality of users.

  According to the present invention, it is possible to easily set and operate a large number of estimated risk factors, and repeatedly perform estimation in real time.

  Hereinafter, preferred embodiments of an estimate support system and an estimate support method according to the present invention will be described in detail with reference to the accompanying drawings. In the following description and the accompanying drawings, the same reference numerals are given to components having substantially the same functional configuration, and redundant description will be omitted.

FIG. 1 is a block diagram of the estimation support system. FIG. 2 is a diagram illustrating an example of a hardware configuration of the estimation support system. The estimate support system 1 is roughly composed of a tangible user interface (TUI) 3, a host computer 5, and an output device 7.
The tangible user interface 3 includes one or a plurality of input means 11-1, 11-2,. The input detection unit 13 includes a transmission unit 21 and a sensing unit 23.

  As shown in FIG. 2, the input means 11-1, 11-2,..., 11-n use packs that allow the user to perform operations such as linear movement, curved movement, and rotational movement. The pack, which is an input means, has a built-in coil. The table serving as the input detection means 13 is provided with a lattice antenna or the like for detecting the position and rotation angle of the coil built in the pack as the sensing unit 23, and detects the position and movement amount of the pack. The movement amount is a movement such as a straight line movement, a curve movement, and a rotational movement. When there are a plurality of input means 11, it is possible to detect the state of each input means by attaching an identification number (ID) to each input means. The transmission unit 21 transmits the pack position and amount of movement detected by the sensing unit 23 to the host computer 5.

  The output device 7 has data output means 51. The data output unit 51 includes a receiving unit 53 and a display unit 55. The output means 7 is, for example, a video projector. The receiving unit 53 receives data transmitted from the host computer 5 and transmits the data to the display unit 55. As shown in FIG. 2, the display unit 55 projects an image or the like on the input detection unit 13 based on the data. To do. When the user moves the input unit 11 or the like to a graphic icon or the like on the image projected on the input detection unit 13, the input detection unit 13 detects the movement of the input unit 11 and transmits it to the host computer 5. When the host computer 5 performs various processing on the position and movement amount of the input unit 11 and transmits the result to the output device 7, the output device 7 displays on the input detection unit 13 based on the result. .

Next, the host computer 5 will be described. The host computer 5 includes a reception unit 31, a selection risk determination unit 32, a condition determination unit 33, a risk check item selection unit 34, a risk calculation unit 35, a cost calculation unit 36, a related case selection unit 37, an output data generation unit 38, and a transmission. A unit 39, a risk factor database 41, a check item database 43, a case database 45, and the like.
The receiving unit 31 receives an input from the TUI 3. The selection risk determination unit 32 refers to the risk factor database 41 based on the data transmitted from the reception unit 31, for example, the identification number (IDp) of the pack that is the input means 11 and the position coordinates (Xp, Yp). To identify the relevant risk factors. This position coordinate is, for example, a coordinate representing a position on the input detection unit 13.

The risk factor database 41 stores risk factors that are items to be considered in the estimation, for example, items such as required specifications, delivery date, cost, and their position coordinates (detected position coordinates).
The condition determination unit 33 determines whether the operation requested by the user is “parameter adjustment”, “risk check”, or “example reference” based on the operation input from the input unit 11. Do.

When a “risk check” operation is requested by the user, the risk check item selection unit 34 identifies a risk item from the position of the input unit 11, the amount of movement, and the like, and selects the check item of the risk item from the check item database 43. Read and determine the next submenu to be displayed.
The check item database 43 hierarchically stores check items related to each risk factor.

The risk calculation means 35 calculates an estimate (cost) based on the parameters set for each risk factor based on the movement amount of the input means 11 and the like. For example, there is a method of calculating a cost based on “(risk coefficient × size × productivity)”, preparing a weight coefficient for each risk factor, obtaining the risk coefficient as the sum of the weights, and the like.
When the condition determination unit 33 determines that the case is referred to by the condition determination unit 33, the related case selection unit 37 reads out a case corresponding to the risk factor requested for reference from the case database 45.

The case database 45 stores success cases, failure cases related to each risk factor, countermeasure proposals derived thereby, and the like.
The output data generation means 38 generates data for outputting the cost calculated by the cost calculation means 36 or the case read by the related case selection means 37 to the output device 7.
The transmission unit 39 transmits the data created by the output data generation unit 38 to the output device 7.

Next, the operation of the estimate support system 1 will be described. FIG. 3 is a flowchart showing the operation of the estimate support system 1.
The output device 7 projects an initial image on the surface of the input detection means 13 (step S301).
The tangible user interface 3 accepts the movement of the input unit 11 on the surface of the input detection unit 13 (step S311). The sensing unit 23 recognizes the position coordinates (Xp, Yp) of the input unit 11 and the identification number IDp when there are a plurality of input units 11 (step S312), and the transmission unit 21 receives the input recognized by the sensing unit 23. The position coordinates and identification number of the means 11 are transmitted to the host computer 5 (step S313).

  FIG. 4 is a diagram illustrating an example of an image displayed on the input detection unit 13 by the output device 7. For example, assume that the risk radar chart 71 shown in FIG. 4 is projected on the input detection means 13 by the output device 7 in the initial state. Now, it is assumed that the pack as the input means 11a has been moved by the user and placed at the graphic icon position R1. The sensing unit 23 recognizes the identification number of the input unit 11a and the position coordinate of the position R1, and transmits the recognition result to the transmission unit 21. The transmission unit 21 transmits the identification number of the input unit 11a and the position coordinates of the position R1 to the host computer 5.

Next, the receiving unit 31 of the host computer 5 receives the position coordinates (Xp, Yp) and the identification number IDp of the input unit 11 (step S321), and transmits them to the selection risk determining unit 32. The selection risk determination unit 32 refers to the risk factor database 41 with respect to the received position coordinate (Xp, Yp), and compares it with the detected position coordinate (X _R , Y _R ) defined in the risk factor database 41. Thus, the risk factor is specified (step S322).

FIG. 5 is a diagram illustrating an example of data definition in the risk factor database 41.
As shown in FIG. 5, a risk ID (ID _R ) and position coordinates (X _R , Y _R ) are defined for each risk name. For example, the risk ID _R is “01”, the risk name N _R is “required specification”, and the position coordinates (X _R , Y _R ) are (10, 50). 4, when the user operates the pack as the input means 11a and the position R1 where the pack is placed is the position coordinates (X _R , Y _R ) shown in FIG. 5, that is, the position coordinates of the recognized pack. When (Xp, Yp) is equal to the position coordinates (10, 50) defined by the risk factor “required specification”, the selection risk determination unit 32 uses the input means 11a to select the risk factor “required specification” by the user. It is determined that is selected.

  Next, the condition determination unit 33 determines whether the user has (1) parameter adjustment, (2) risk check, or (3) case reference based on how the input unit 11 is moved (amount of movement). It is determined whether or not an operation is requested (step S323). For example, the condition determination unit 33 places a pack as the input means 11 on the position R1 which is a graphic icon, and (a) if the pack is rotated at that position, it is determined as (1), and (b) is determined. Conditions are determined in advance such that (2) is determined when the time is left as it is, (c) the pack is translated, or (3) is determined when a button or the like provided on the pack is clicked. The operation is determined based on how the input means 11 is actually moved. The arrangement of how to move the pack and the operation corresponding to it can be decided at any time according to the estimation case.

  In step S323, for example, when the input unit 11 is moved and rotated at the place, and the condition determination unit 33 determines that (1) parameter adjustment is performed, the risk calculation unit 35 calculates the position coordinate changes ΔXp and ΔYp. Risk fluctuation amount (variation amount of parameter of risk factor) ΔR is calculated (step S341). For example, in the radar chart 71 shown in FIG. 4, by moving and stopping the input means 11a along the axis of the recognized risk factor, the parameter value or change amount of the recognized risk factor is input. Is possible.

  The cost calculation unit 36 calculates the cost C in consideration of the risk fluctuation amount ΔR calculated by the risk calculation unit 35 (step S342). The output data generation unit 38 generates graphic data to be displayed based on the calculated cost C, and the transmission unit 39 transmits the graphic data to the output device 7 (step S343). The receiving unit 53 of the output device 7 receives data from the host computer 5, and the display unit 55 projects the image again on the input detecting means 13 based on the data (step S361).

  In step S323, for example, when the condition determination unit 33 determines that the input unit 11 remains at the position R1 for a predetermined time and (2) it is a risk check, the risk check item is selected. The means 34 determines the user's check from the change amounts ΔXp and ΔYp of the position coordinates, calculates the risk fluctuation amount ΔR, and refers to the check item database 43 to determine the submenu to be displayed next (step S331). ).

  FIG. 6 is a diagram illustrating an example of data definition in the check item database 43. As shown in FIG. 6, a subcategory is defined for each risk factor, and check items are further defined hierarchically for each item of the subcategory. For example, for the risk factor “required specification”, the name Ns is defined as “functional requirements”, “business flow”, and “maintenance conditions” as subcategories, each having an identification number IDs and position coordinates (Xs, Ys). Defined. Here, the position coordinates (Xs, Ys) are coordinates for displaying a submenu on the input detection means 13, that is, when the input means 11 is placed at the position coordinates, the menu is selected. .

  In addition, when the name of the subcategory is “functional requirement”, the check item identification number IDc, the check item Nc, “is the required range clear?”, “Is there a possibility of specification change?”, And position coordinates (Xc, Yc) is defined. Here, the position coordinates (Xc, Yc) are coordinates for displaying a submenu related to the check item on the input detection means 13.

  For example, in the risk radar chart 71 shown in FIG. 4, when the user leaves the input unit 11a at the position R1 of the graphic icon, the condition determination unit 33 determines that it is a risk check, and the risk check item selection unit 34 Refers to the subcategory of the check item database 43 shown in FIG. The output data generation means 38 generates data for displaying the submenu 73 from the subcategory name Ns and the position coordinates (Xs, Ys), and transmits the data to the output device 7. The output device 7 projects a submenu 73 on the input detection means 13.

  When the user moves the input unit 11a over the “functional requirement” on the submenu 73 and leaves it there, the condition determination unit 33 determines that “functional condition” has been selected, and the risk check item selection unit. Reference numeral 34 refers to the check item Nc defined for the subcategory “functional requirement” in the check item database 43 shown in FIG. 6 and its position coordinates (Xc, Yc). The output data generation means 38 generates data for displaying the submenu 75 from the check item Nc and its position coordinates (Xc, Yc), and transmits the data to the output device 7. The output device 7 projects a submenu 75 on the input detection means 13.

  When the change amounts ΔXp and ΔYp of the input means are input to the respective check items of the displayed submenu 75 by the operation of the input means 11a by the user, the risk calculation means 35 causes the risk fluctuation amount ΔR for each check item. Calculate

Next, in step S323 shown in FIG. 3, for example, when the input unit 11 is moved in parallel or a button or the like provided on the input unit is clicked, and (3) it is determined to refer to the case, the related case selection unit 37 is selected. Refers to the case database 45 from the change amounts ΔXp, ΔYp and selects an appropriate case (step S351).
FIG. 7 is a diagram illustrating an example of data defined in the case database 45. As shown in FIG. 7, the case database 45 includes an identification number ID _F of one or more countermeasure proposals for the risk factor identification number ID _R and the name N _R , the contents N _{F of the} countermeasure proposal, and the position coordinates (X _F , Y _F ) is stored. Here, the position coordinates (X _{F, Y F)} are the coordinates for displaying the contents N _F on the input detection unit 13, i.e., if the input unit 11 is placed in its position coordinates, that it is selected become.

Based on the data related to the case selected by the related case selection unit 37 in this way, the output data generation unit 38 generates graphic data for displaying the selected case ID _F , and the transmission unit 39 sends this data to the output device 7. Transmit (step S352). The receiving unit 53 of the output device 7 receives data from the host computer 5, and the display unit 55 projects an image on the input detection unit 13 based on the data (step S361).

  Note that the processing of the host computer 5 is realized by an application program stored in the host computer 5. This program can be stored in a storage medium and distributed, or can be distributed via a network.

8 and 9 are diagrams showing application examples of the present estimate support system 1. FIG.
The application example shown in FIG. 8 is an example in which the cost is reexamined by changing the parameter of the risk factor in accordance with the related cases and countermeasures stored in the case database 45. When it is found from the risk radar chart shown in FIG. 8 that the influence of the risk factor “human resource” is large, the user aligns the input means 11 with the graphic icon of “human resource” and, for example, the input means is left as it is. Requesting the display of a countermeasure proposal for a related case by performing a predetermined operation such as

The host computer 5 causes the output device 7 to display a submenu of items in risk. Further, when the user puts the input unit 11 in the state as it is in accordance with the graphic icon of “business expert” in the submenu, the host computer 5 displays data related to “business expert” on the output device 7. For example, when the host computer 5 determines that the menu “view countermeasure plan” has been selected by moving and moving the input means 11 onto the menu “view countermeasure plan”, the host computer 5 stores the case database 45 in the menu. It is possible to display the countermeasure plan on the output device 7 by referring to it, and to calculate the adjustment result such as how the risk is improved by applying the countermeasure plan and display it on the output device 7. .
In this way, using this system, it is possible to recalculate the cost by changing the parameter of the risk factor on the spot while referring to the result and the countermeasure plan of the related case, and to examine it repeatedly. This can be done easily by moving the input means 11 on the input detection means 13.

In addition, the application example shown in FIG. 9 compares the risk radar chart 83 of the project under consideration (this PJ) with the risk radar chart 85 of the past successful project (past PJ), and changes the parameter of the risk factor. This is an application example in which the calculation of the estimated overhead is considered.
For example, in the risk factor “human resource”, the host computer 5 calculates an estimate when the target business experience rate of the past PJ is applied to this PJ, and causes the output device 7 to output the result.
All the operation instructions such as menu selection in the application example shown in FIG. 9 are easily performed by the user's operation on the input means 11, and it is possible to easily perform estimation examination from various angles for various risk factors.

The estimate support system 1 can also be applied to cases where a plurality of users review estimates using a plurality of input means 11.
The input means 11 corresponding to each of a plurality of users can be identified by the identification number IDp of the input means 11 and displayed by changing the output display method for each identification number IDp of the input means 11.
For example, input means 11 having different identification numbers IDp are associated with software vendors and users, and parameters and the like are input by operating the input means 11. The host computer 5 estimates the vendor side and the user side respectively. Can be calculated and the estimation results and parameters of both can be output to the radar chart, making it possible to compare and evaluate the estimates of both.

  For example, by aligning the input unit 11 with a risk factor having a large influence from the estimation result and moving the input unit 11 on the axis of the radar chart, the movement amounts ΔXp and ΔYp are recognized, and the risk calculation unit 35 detects the risk fluctuation amount ΔR. The cost calculation unit 36 recalculates the cost (estimation) in consideration of the ΔR, and the recalculated result is displayed on the input detection unit 13 by the output device 7. By repeating this operation, various risk factor parameters can be changed and examined. Even when the same calculation formula is used, if the parameters entered by the vendor and the user are different, the estimates are different, and it is possible to examine which risk factor of both influences the estimation.

In addition, the system can be used even when an experienced person educates a new face or the like regarding an estimate. For example, the input means 11 having different identification numbers IDp is given to the experienced person and the new face, the estimate for the parameter input by the new face using the input means 11 is displayed, and the estimate for the parameter input by the experienced person is further displayed. By examining the differences, etc., it becomes possible for newcomers to obtain knowledge about the estimate.
In this way, by using this system, a table serving as the input detection unit 13 is shared by a plurality of users, and parameters are input using the input unit 11, so that estimation results of various cases among a plurality of users can be obtained. It is possible to easily examine parameters in real time.

  In the present embodiment, the position coordinates are used for recognizing the graphic icon or menu displayed on the input detecting means 13, but for example, the detected position coordinates of the graphic icon or menu may have a certain range. It is also possible to determine the detection range by a rectangular range or the like.

  The preferred embodiments of the estimate support system and the estimate support method according to the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to such examples. It will be apparent to those skilled in the art that various changes or modifications can be conceived within the scope of the technical idea disclosed in the present application, and these naturally belong to the technical scope of the present invention. Understood.

  As described above, according to the present invention, by performing various operations such as changing parameters of a plurality of risk factors using the tangible user interface, it is possible to easily perform an estimate that needs to be examined regarding a plurality of risk factors. . Further, by providing a plurality of input means for the tangible user interface, it is possible to examine estimates from various viewpoints and share the estimation results.

Block diagram of the estimate support system 1 The figure which shows the hardware structural example of the estimation assistance system 1 Flow chart showing the operation of the estimate support system 1 The figure which shows the example of a display on the input detection means 13 Figure showing an example of risk factor database definition Figure showing check item database definition example Figure showing example definition of case database The figure which shows the application example of the estimate assistance system 1 The figure which shows the application example of the estimate assistance system 1

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ......... Estimation support system 3 ......... Tangible user interface 5 ......... Host computer 7 ......... Output device 11, 11-1, 11-2, ..., 11-n, 11a ......... Input means 13 ... …… Input detection means 31 ............ Receiving section 32 ............ Selected risk determination section 33 ............ Condition determination section 34 ............ Risk check item selection means 35 ............ Risk calculation means 36 ............ Cost calculation means 37 ......... Related case selection means 38 ......... Output data generation means 39 ......... Transmission unit 41 ......... Risk factor database 43 ......... Check item database 45 ......... Case database 51 ......... Data output means

Claims (5)

An estimate support system that supports an estimate taking into account multiple risk factors,
Input means;
Input detection means for detecting the position and the amount of movement of the input means;
A selection risk determination means for specifying and selecting the risk factor from the position of the input means detected by the input detection means;
With respect to the risk factor selected by the selected risk judging means, a risk calculating means for calculating a risk fluctuation amount from a movement amount of the input means detected by the input detecting means;
An estimate calculating means for calculating an estimate using the risk fluctuation amount calculated by the risk calculating means;
An output means for outputting a calculation result by the estimate calculation means on the input detection means;
An estimate support system characterized by comprising: The risk factor and a case storage means for storing a countermeasure plan corresponding to the risk factor,
The risk calculation unit reads out a countermeasure plan corresponding to the risk factor selected by the selected risk determination unit from the case storage unit, and calculates an estimate in consideration of the read out countermeasure plan. The estimate support system according to 1. The input means has an identification number;
The input detection means detects the position and movement amount of the input means for each identification number,
The selection risk determination means specifies and selects the risk factor from the position of the input means detected by the input detection means for each identification number,
The risk calculation means calculates a risk fluctuation amount from the movement amount of the input means for each identification number with respect to the selected risk factor,
The estimate calculating means calculates an estimate for each identification number using the calculated risk fluctuation amount,
3. The estimation support system according to claim 1, wherein the output unit outputs a calculation result of the estimate calculation unit on the input detection unit for each identification number. An estimate support method for supporting an estimate in consideration of a plurality of risk factors,
Moving the input means on the input detection means;
Detecting the position and amount of movement of the input means on the input detection means;
Identifying and selecting the risk factor from the position of the input means detected by the input detection means;
Calculating a risk fluctuation amount of the selected risk factor from the movement amount of the input means detected by the input detection means;
Calculating an estimate using the risk fluctuation amount calculated by the risk calculating means;
Outputting the calculation result by the estimate calculation means on the input detection means;
An estimate support method characterized by comprising: Computer
Input means;
Input detection means for detecting the position and the amount of movement of the input means;
A selection risk judging means for identifying and selecting an estimated risk factor from the position of the input means detected by the input detecting means;
With respect to the risk factor selected by the selected risk judging means, a risk calculating means for calculating a risk fluctuation amount from a movement amount of the input means detected by the input detecting means;
An estimate calculating means for calculating an estimate using the risk fluctuation amount calculated by the risk calculating means;
An output means for outputting a calculation result by the estimate calculation means on the input detection means;
Program to function as.

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