JP2009064430A - Device and method for creating test data - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To correctly attain secret processing of production environment data with few resources. <P>SOLUTION: A feature description for an item name of production environment data stored in a database 41, attributes of the item and a format of actual data is stored in a feature storage part 42 together with points thereof. The feature description describes a feature pattern of each data by regular expression. A point counting part 11 counts, when matching of an item name, its attribute and sample data extracted from the item with the feature description is determined by a feature determination part 10, a point to the item. A data content determination part 12 determines, when the point reaches a fixed value, that data of the item is a known type of data having the feature. For data belonging to the item having the data whose feature is determined by the data content determination part 12, the data is partially or entirely substituted by test data. The text data is directly transferred to a production environment, or output in an optional file format. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a test data creation method and a creation method for creating test data used at the time of development of a database system based on data stored in an existing customer database.

  When testing a developed system during the development of a database system, it is required to perform the test in a state as close to the production environment as possible in order to identify problems with the developed system. Therefore, conventionally, a part of the production environment data is used as it is as the test data.

However, if part of the production environment data is used as it is for testing, there is a risk of loss of information assets such as personal information. As a method for improving this, as shown in Patent Literature 1 and Patent Literature 2, test data that conceals the content of production environment data is created by converting a part of production environment data into other characters. A method has been proposed.
JP 2004-326510 A JP 2008-65687 A Japanese Patent Laid-Open No. 07-36873

  However, the invention of Patent Document 1 develops work such as what data structure and attributes each data of production environment data has, or which part of production environment data is converted to test data. In a database that records many different types of data that must be specified by the developer, there is a problem that requires a great burden on the developer.

  On the other hand, the invention of Patent Document 2 creates test data by creating intermediate data for taking out from data recorded in a table format and associating the intermediate data with replacement master data prepared in advance. ing. For this reason, there is a problem that it is impossible to maintain the same attribute and data structure between the production environment data and the completed test data.

  In addition, the invention of Patent Document 3 prepares a dictionary database that collects first and last names, and compares each data in the production environment data with the first and last names recorded in the dictionary database, so that the production environment data becomes the full name. It is determined that the data is related to. Then, test data is generated by replacing the production environment data with the first and last names in the dictionary database.

  However, the invention of Patent Document 3 has a drawback that it is impossible to determine what the actual environment data is, ie, “name”, “last name only”, or “first name only”. It was. In addition, if the production environment database records various types of data such as names, addresses, pseudonyms, postal codes, phone numbers, dates, monetary values, and other character strings, etc. There is a problem that it is difficult to determine what kind of data is stored in which item. For example, for postal codes and telephone numbers, it could not be determined that all the numbers in the country were not registered in the database.

  The present invention has been proposed to solve the problems of the prior art as described above, and various data recorded in an existing database are analyzed by analyzing their patterns together with the data contents. By specifying the data type of the item and converting the data content according to the specified data type, the data content of the existing database is concealed and test data close to the data structure of the existing database is created It is an object of the present invention to provide a test data creation apparatus and creation method that make it possible.

The test data creation apparatus of the present invention has the following configuration requirements.
(a) A database that stores multiple types of data in a table format classified by item.
(b) A feature storage unit that stores, for a known type of data, a feature pattern of the data in a format described by a regular expression, and stores a predetermined point for the feature.
(c) Extracting a plurality of sample data for each item from the data stored in the database, and whether each sample data conforms to the pattern described by the regular expression stored in the feature storage unit A feature determination unit for determining whether or not.
(d) A point counting unit that counts a predetermined point for the detected feature for each item when a feature specific to each item is detected in each sample data by the feature determination unit.
(e) A data content determination unit that determines that the data of the item to which the sample data belongs is a known type of data having the characteristics when the points counted by the point counting unit reach a certain value .
(f) Concerning data belonging to the items whose data characteristics are determined by the data content determination unit in the data stored in the database, a part or all of the data is concealed based on the determined data content A data converter that replaces data.

  In addition, a test data creation method using each process executed in the test data creation apparatus having the above-described configuration as a configuration requirement is also an aspect of the present invention.

  In the present invention having the above-described configuration, the feature storage unit stores a feature unique to a known item name and points about the feature, and the feature determination unit is stored in a database. The table name item name is extracted, and it is analyzed whether or not the item name has a feature relating to a known item name stored in the feature storage unit, and the point counting unit includes the sample Along with data points, when a feature specific to an item name known to the item name is detected by the feature determination unit, a point predetermined for the detected feature is counted for the item name It is also an embodiment of the present invention.

  In the present invention having the above-described configuration, the database stores, for each item of data stored in a table format, a data format of data belonging to the item as an item attribute, and the data structure The determination unit determines the data structure of the data belonging to the item based on the comparison result between the attribute of the item and the data structure of the known data together with the points based on the sample data and / or the item name. This is also one embodiment of the present invention.

  In the present invention having the above-described configuration, the data conversion unit replaces part or all of data stored in the database for each known data type, and the replacement rule. It is also an aspect of the present invention that data conversion is performed with reference to a conversion data storage unit that stores dummy data used by.

  According to the present invention, the data structure pattern of the data belonging to each item of the database is taken into account by determining whether the sample data matches the pattern described by the regular expression stored in the feature storage unit. However, it is possible to determine the characteristics of the data. As a result, compared to the case where the character string itself is used as a feature description, it is possible to determine the characteristics of various data with less feature description, and the data processing for production environment can be concealed with less resources and more accurately. it can.

  An embodiment of the present invention will be specifically described with reference to the drawings. FIG. 1 is a block diagram showing the configuration of the present embodiment, and FIG. 2 is a flowchart showing its operation.

[Configuration and Action of Embodiment]
The test data creation apparatus according to the present embodiment includes an input device 1 such as a keyboard, an arithmetic device 2 such as a CPU, a memory 3 that develops programs and data, a production environment data database 41 and a feature storage unit 42, and a completed test. Data storage unit 43, storage device 4 such as a hard disk used as conversion data storage unit 44 storing replacement rules for replacing production environment data with test data and dummy data used by the replacement rules And hardware such as an output device 5 such as a display or a printer.

  And by making a computer program run on this hardware, the characteristic determination part 10, the point counting part 11, the data content determination part 12, the data conversion part 13, and the characteristic correction part which comprise the test data creation apparatus 6 of this invention 14 and the weight changing unit 15 are realized on a computer.

  The production environment data database 41 stores a plurality of types of data in a table format classified by item. In the present embodiment, the database stores item names, item attributes, and actual data classified for each item. As an example, these are as follows.

(1) Item name (column name)
(a) Date type
DATE
TIME
YEAR
(b) String type
ADDRESS
JYUSHO
PREFECTURE
NAME
(c) Numeric type
WEIGHT
LENGTH
AMOUNT
VOLUME

(2) Item attributes (data type)
(a) Date type
DATE
IMESTAMP
(b) String type
VARCHAR
CHAR
(c) Numeric type
INTEGER
BIGINT
FLOAT
REAL
DOUBLE

(3) Format of actual data
(a) Date Year, month, day, hour, minute, second Year, month, day
yyyy / MM / dd hh: mm: ss
yyyy-MM-dd
(b) Character string From prefecture name to details such as street address Prefecture postal code
(c) Numerical value 123
456
123,456

  The feature storage unit 42 includes the (1) item name, (2) item attribute, and (3) feature description about the actual data and its format for the production environment data stored in the database 41. It is remembered with the points. This feature description is a description of a feature pattern of each data by a regular expression. For example, the feature description is as follows. The example of the feature description does not necessarily correspond to the data example for the production environment.

(1) Item name (column name)
(a) Date
. * [Dd] [Aa] [Tt] [Ee]. *
. * [Tt] [Ii] [Mm] [Ee]. *
(b) Character string
. * [Aa] [Dd] [Dd] [Rr] [Ee] [Ss] [Ss] $
. * ([Jj] [Yy] | [Ji]) [Uu] [Ss] ([Hh] | [Yy]) [Oo] $
. * [Pp] [Rr] [Ee] [Ff] [Ee] [Cc] [Tt] [Uu] [Rr] [Ee]. *
(c) Numerical value
. * ([Ww] | [Hh]) [Ee] [Ii] [Gg] [Hh] [Tt] $

(2) Item attributes (data type)
(a) Date type
^ [Dd] [Aa] [Tt] [Ee] $
^ [Tt] [Ii] [Mm] [Ee] [Ss] [Tt] [Aa] [Mm] [Pp] $
(b) String type
^ (VARCHAR | CHAR) $
(c) Numeric type
^ (TINYINT | SMALLINT | INTEGER | BIGINT | FLOAT | REAL | DOUBLE | NUMERIC | DECIMAL) $

(3) Format of actual data
(a) Date
^. * Year. * Month. * Day. * Hour. * Minute. * Second $ ... year, month, day and time
. * Year. * Month. * Day $ ... date
^ ((19 | [2-9] [0-9]) [0-9] {2}) / ([1-9] | (0 [1-9] | 1 [0-2])) / ([1-9] | (0 [1-9] | ([12] [0-9] | 3 [01]))) $ ...
(b) Character
^ (Sato | Suzuki | Takahashi | Tanaka | Watanabe | Ito | Yamamoto). *… Name
^ (Sato | Suzuki | Takahashi | Tanaka | Watanabe | Ito | Yamamoto) $… Surname
^ [0-90-9] {1,} [-] [0-90-9] {1,} [-] [0-90-9] {1,} $ ... phone number
^ [0-90-9] {3} [-] [0-90-9] {4} $ ... zip code
(c) Numerical value
^ [0-9.,] {1,} $

Here, the regular expression used for each feature description has the following meaning.
(a) Period means any single character except line feed.
(b) The asterisk searches for the regular expression that is immediately before 0 or more times.
(c) If one of the characters enclosed in brackets (square brackets) is matched, it is judged as a match.
(d) “|” indicates a logical sum of patterns. The logical OR of this pattern is used when it is desired to search for “this character string or this character string”.
(e) Palen (parentheses, parentheses) is evaluated by grouping patterns. Since the priority is higher, evaluate the whole after evaluating the inside of the paren.
(d) A dollar sign means the end of a line.
(f) Caret means the beginning of a line.
(g) {n}, {n,}, and {n, m} specify the number of pattern repetitions. {n} is repeated n times, {n,} is repeated n times or more, {n, m} is repeated n times or more and m times or less.

  For the feature description as described above, a number of patterns are prepared in accordance with various item names, their attributes, and actual data formats expected to be used in the production environment data database.

  The feature determination unit 10 obtains sample data (for example, about 10 to about 20 pieces of data) arbitrarily extracted from the item name, item attribute, and actual data of the production environment data stored in the database 41. Extraction is performed (steps 1, 4, and 7 in FIG. 2), and it is determined whether or not each pattern matches a pattern described by the regular expression stored in the feature storage unit 42 (steps 2, 5, and 8).

  For example, if "name" data is recorded in the database, the item name includes "NAMAE" or "SHIMEI", the item attribute matches either "VARCHAR" or "CHAR", and the sample is taken out In the data, there should be "last name" and "first name" that are often seen in Japanese. Therefore, it is determined whether each item name, attribute, and sample data matches which feature description stored in the feature storage unit 42.

  Specifically, when the database to be developed stores a name, it is stored in one item as “name” or divided into two items, “last name” and “first name”. The test data to create will also be different.

  Therefore, in the feature storage unit 42, a regular expression feature description of ^ (Sato | Suzuki | Takahashi | Tanaka | Watanabe | Ito | Yamamoto). To prepare ^ (Sato | Suzuki | Takahashi | Tanaka | Watanabe | Ito | Yamamoto) $ for the regular expression feature description, and the sample data extracted from the production environment data Analysis is performed by the determination unit 10. For example, “name” includes one of the surnames commonly found in Japanese surnames such as “Sato | Suzuki | Takahashi | Tanaka | Watanabe | Ito | Yamamoto”, followed by “. *” Repeat characters (names are considered to follow) appear as regular expressions. On the other hand, when the sample data is “last name”, the regular expression ends with “$”, and no characters follow.

  Similarly, for phone numbers and postal codes, patterns such as the numbers used, the number of digits, and the location of the haiphong are described in regular expressions to determine what characteristics the sample data has. .

  When the feature determination unit 10 matches the item name, its attribute, sample data extracted from the item, and the feature description (if a feature specific to the item is detected), the point counting unit 11 ), A predetermined point for the detected feature is counted for the item (steps 3, 6 and 9).

That is, the point counting unit 11
(a) When an item name and a feature description match, the points determined corresponding to the feature description are counted.
(b) When an attribute and a feature description match, the points determined corresponding to the feature description are counted.
(c) When one feature description matches one of the sample data, the points determined corresponding to the feature description are counted.
In this way, points are added to the item every time it matches the feature description.

  When the point counted by the point counting unit 11 reaches a certain value (Yes in Step 11), the data content determination unit 12 determines that the item name, attribute, and item data to which the sample data belongs are It is determined that the data is a known type of data having characteristics.

  The data conversion unit 13 determines, based on the determined data content, the data belonging to the item whose data characteristics are determined by the data content determination unit 12 in the production environment data stored in the database 41. A part or all of the data is replaced with confidential data. That is, when the characteristics of the data belonging to the item are determined based on the item name, attribute, and sample data, the data conversion unit 13 converts the conversion rule stored in the conversion data storage unit 44 and the conversion data. Referring to the dummy data (step 12), the production environment data belonging to the item is converted into test data (step 13).

For example, as a conversion rule:
(a) When the data of the item is “name” or “address”, dummy data randomly extracted from a large number of “name” and “address” prepared in the conversion data storage unit 44 Convert.
(b) In the case of telephone numbers or numbers, replace them with random numbers or asterisks.
(c) Replace with other character strings in the production environment data without preparing dummy data. (d) For numbers and dates, replace only with a certain range of numbers.
(e) Replace only the value at a certain position in a character string or numerical value with dummy data.
(f) Number of test data to create.
Prepare a rule that can create test data with contents suitable for the operation check of the database system to be developed.

  The test data converted by the data conversion unit 13 is stored in the test data storage unit 43, and then output to the outside from the output device 5 or used as data of a database system to be developed. In this case, as shown in FIG. 3, when connection to the test environment is possible, the created test data can be directly transferred to the test environment database 45. On the other hand, if connection to the test environment is not possible, the file can be output to a file of any format such as XML, CSV, TSV.

  FIG. 4 is an example of a screen showing an example of the test data obtained in this way. The upper part shows the item names, attributes, etc. of each item constituting the database, and the middle part for the production environment before conversion. The test data after conversion is shown in the lower row.

[Effect of the embodiment]
According to the present embodiment having the above-described configuration, the feature of the item name, the attribute of the item, the content of the actual data belonging to the item, and the like are described using regular expressions. It becomes possible not only to match simple character strings and numerical values but also to grasp them as patterns. As a result, even for data such as telephone numbers and postal codes using the same numbers, it is possible to accurately distinguish both by recording the recording patterns of both. In addition, by using regular expressions, a large number of features can be easily aggregated and described, so there is an advantage that the storage capacity of the feature description unit 42 can be reduced.

  In the present embodiment, since the item name, the item attribute, the actual data belonging to the item, and the feature described by the regular expression are expressed by the character string as described in the above example, the description and The contents can be easily grasped, and the feature description can be easily corrected and added. In particular, in the present embodiment, the feature (pattern) description in the feature storage unit 42 is described in an external file and can be easily corrected without affecting the operation of the program.

  In addition, when the conversion data storage unit 44 has a rule that replaces only a certain range of character strings as a conversion rule, test data with sufficient consideration for consistency with the system to be developed is provided. Can be created. For example, when the production environment data is a number “X01-001”, “X01-” is an identification number, and if this is changed, data consistency is not achieved, and “YB4-” or the like is obtained. Will result in different identification numbers. However, in this embodiment, by using the conversion rule as described above, “X01-” in “X01-001” is not converted, and only “001” is converted to “002” or the like. Data can be obtained.

[Other Embodiments]
The present invention is not limited to the above-described embodiments, and includes other embodiments as follows.
(a) In the above-described embodiment, the characteristics are determined for all of the item name, item attribute, and actual data (sample data) belonging to the item, and the points are counted. It is also possible to determine the contents of the data to which it belongs.

(b) Features of various data stored in the feature storage unit 42 can be freely changed by adding / changing a description using a regular expression. In particular, by providing the feature correction unit 14 in the test data creation device itself, the regular expressions stored in the feature storage unit 42 are displayed on the output device 5 such as a display in the form of a list, and the input device The regular expression can be modified and added from 1.

(c) The feature points stored in the feature storage unit 42 together with the feature description can be freely changed. In this case, by providing the weight change unit 15 in the test data creation device itself, the points for each feature can be changed from the input device 1 in the same manner as the feature description. In particular, the item name and item attribute have one feature for each item, so the points are constant, but the actual data points belonging to the item are added according to the number of sample data, so the weight change It is desirable to balance the “item name, item attribute” points and the “sample data” points by the unit 15.

FIG. 5 shows an embodiment of the data content determination process of the present invention.
In the figure, reference numeral 51 denotes particularly sample data of a database storing production environment data. 52 is a processing content of the feature determination unit 10, the point counting unit 11 and the data content determination unit 12, 53 is a determination criterion thereof, 54 is a definition of a score associated with the feature description, and 55 is a score of the definition 54. A table that is numerically shown by fitting, 56 is a table that accumulates definition scores for each item, and 57 is a table that shows the determination results of the data contents of the items.

  As can be seen from FIG. 5, the discrimination method using the evaluation definition information of the present invention (information combining the feature description described in the embodiment and the point definition for each type determined according to the feature) is a numeric value. By changing the classification of the evaluation definition information for each data type, character data type, and date data type, the score determined for each type is attached, and the tendency of the number of data bases collecting the number of cases is read. The data type here refers to a type that defines the character of data called a database and the range of numerical values. As the points are accumulated, the number of bases increases, and the distribution of the points for each column of the database becomes clear as a ranking.

  According to this example, the table extracted from the database and the column list information data are subjected to separation processing through the evaluation definition information. The standard depends on the feature description of the embodiment. “Specific character match”, 2. 2. “Specific type match”; “Understanding the number of characters”; It is “determined by accumulation of the number of mothers”.

  As indicated by 53 in FIG. 5, in the standard 1, “specific character match” is determined by the score. If a specific character matches a table or column, it becomes easier to specify the data type. For example, in 51 of FIG. 5, the first column is described in a table such as “Tanaka” and “Sato”. In the general public, “Tanaka” and “Sato” are widely recognized as names often used for family names. Although it cannot be recognized as a word in computer processing, it is an XML format that uses a regular expression in the definition information storage unit (feature storage unit 42 in FIG. 1) as a characteristic word that is likely to be a name in advance. It is described. By registering this definition information, when “Tanaka” and “Sato” match the criteria, 1 point is given.

  The second row of 51 in FIG. 5 is the second row of data called “Ichiro” and “Mitsuko”, but “Ichiro” is a popular name used as a person name in ordinary society. Has been recognized. However, different names such as “Jiro” and “Saburo” must all correspond. In addition, “Sanko” can be inferred as a name and a place name.

  Therefore, words that cannot be recognized and recognized as words in computer processing, but words that tend to be often used with names such as “Buro” and “Son” are stored in advance in the definition information storage unit (feature storage unit 42 in FIG. 1). Is described in XML format using regular expressions. In the same manner as in the first row, when “Buro” and “Child” are matched as 2 criteria, 2 points are given as points.

  In criterion 2 indicated by 53 in FIG. 5, “specific format match” is determined by the score. There is a high possibility that it is possible to determine whether the date is a table or column according to whether or not it matches a predetermined format. Therefore, the definition information storage unit is divided into types such as date data types described in XML format using regular expressions, and matches the type of YYYY / MM / DD etc. used as the format of the notation method such as database etc. Evaluation of the presence or absence of.

  As an example, in the third column of 51 in FIG. 5, “2000/09/01” is described in the table. In the computer processing, when the date data type of the database on the computer is used in the feature storage unit 42 of the above embodiment, the evaluation judgment is made by matching with YYYY / MM / DD. Further, in combination with the standard 1, it can be determined by the fact that specific characters such as “year, month, day” are described. As in the first row, when “2000/09/01” matches as a reference, 4 points are assigned as points.

  In the standard 3 shown by 53 in FIG. 5, the determination is made based on the “ascertaining number of characters” score. There is a high possibility that “zip code”, “phone number”, etc. can be discriminated depending on whether or not the predetermined number of characters matches the table or column. Therefore, it is defined to be applied when there are consecutive numbers and characters such as 1 to 9 by regular expression, divided by type such as character data type described in XML format of definition information storage unit .

  As an example, “123-7456” is described in the table in the fourth column in the first row in FIG. In ordinary ordinary society, “123-7456” is recognized as a 7-digit specified identification number of a zip code. In computer processing, when the character data type described in the XML format of the definition information storage unit is used, the regular expression “[0-9] {3} [−] [0-9] {4}” is used. Since the evaluation judgment is made by entering the described 7-digit number, 5 points are given as the score.

  Then, in criterion 4 of 53 in FIG. 5, the determination is made based on the total number of points as described in “determination based on accumulation of the number of mother bodies”. As the score evaluation of the data progresses, an integrated image indicated by 56 in FIG. 5 is obtained. A total is obtained for each type obtained as a score, and a type with a high score is selected and converged to appropriate definition information. As a result, the data accumulated at the time of data replacement is more appropriately converted.

Functional block diagram of an embodiment of the present invention Processing flow diagram of the embodiment of FIG. Figure showing the output format of the created test data Display screen showing one example of production environment data and created test data The figure which shows the process of the evaluation definition information in the Example of this invention

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Input device 2 ... Arithmetic device 3 ... Memory 4 ... Storage device 41 ... Production environment data database 42 ... Feature storage unit 43 ... Test data storage unit 44 ... Conversion data storage unit 45 ... Test environment database 5 ... Output Device 6 ... Test data creation device 10 ... Feature determination unit 11 ... Point counting unit 12 ... Data content determination unit 13 ... Data conversion unit 14 ... Feature correction unit 15 ... Weight change unit

Claims (7)

A database that stores multiple types of data in a table format that is classified by item,
A feature storage unit that stores a pattern of a feature of the known type of data in a format described by a regular expression and stores a predetermined point for the feature;
A plurality of sample data is extracted for each item from the data stored in the database, and whether each sample data conforms to a pattern described by a regular expression stored in the feature storage unit. A feature determining unit for determining;
When a feature specific to each item is detected in each sample data by the feature determination unit, a point counting unit that counts a predetermined point for the detected feature for the item;
A data content determination unit for determining that the data of the item to which the sample data belongs when the point counted by the point counting unit reaches a certain value, is a known type of data having the characteristics;
In the data stored in the database, for data belonging to the items whose data characteristics have been determined by the data content determination unit, part or all of the data is replaced with confidential data based on the determined data content A data converter to
A test data creation device characterized by comprising: The feature storage unit stores a feature specific to a known item name and a point about the feature,
The feature determination unit extracts an item name in a table format stored in a database, and analyzes whether the item name has a feature related to a known item name stored in the storage unit. And
When the point counting unit detects a feature specific to the item name known to the item name by the feature determination unit together with the points of the sample data, the detected feature is predetermined for the item. The test data creating apparatus according to claim 1, wherein the points are counted. The database stores, for each item of data stored in a table format, the data format of the data belonging to that item as an item attribute,
The data content determination unit determines the data structure of the data belonging to the item based on the comparison result between the attribute of the item and the data structure of the known data together with the points based on the sample data and / or the item The test data creation device according to claim 1, wherein the test data creation device is a test data creation device.   Conversion data in which the data conversion unit stores a replacement rule for replacing part or all of the data stored in the database for each known data type and dummy data used by the replacement rule 4. The test data creation apparatus according to claim 1, wherein the test data creation apparatus performs data conversion with reference to a storage unit.   The feature storage unit includes a feature correction unit for correcting a pattern of a feature expressed by a regular expression stored therein by changing a description of the regular expression. The test data creation device according to any one of claims 1 to 4.   Weighting for changing the weighting of the points of the sample data counted by the point counting unit and the item name and / or item attribute points counted by the point counting unit by the data content determination unit 6. The test data creating apparatus according to claim 2, further comprising a changing unit. Extract multiple sample data from the data stored in a database that stores multiple types of data in a table format classified by item, and whether these sample data have the characteristics of known types of data Is determined by comparing the feature of the sample data and known data with a feature description described by a regular expression,
When a characteristic peculiar to the known type of data is detected in each sample data by the determination process, a predetermined point for the detected characteristic is set for an item in which the sample data is stored. Processing to count,
When the point counted by the point counting process reaches a certain value, a process of determining that the data of the item to which the sample data belongs is the known type of data;
A part or all of a plurality of data belonging to an item determined to be a known type of data by the determination process in the database is determined according to the characteristics of the determined known type of data. A process of converting a part or all into confidential data;
A test data creation method characterized by comprising: