WO2021234798A1 - Generation device, generation method, and generation program - Google Patents

Abstract

A generation device (10) obtains data of a specification sheet, extracts a character string in a chapter included in the obtained data of the specification sheet, and extracts a character string of a sentence that does not contain any expressions unrelated to a preset test among character strings of sentences of said chapter and thereafter. The generation device (10) then generates a list of test items on the basis of the extracted character string of the chapter and the character string of the sentence that does not contain any expressions unrelated to the test.

Description

Generator, generation method and generation program

The present invention relates to a generator, a generator, and a generator.

Conventionally, in order to improve the efficiency of the development process in system development, etc., a technology for automatically extracting test items from the required specifications has been proposed. In the prior art, for example, when a huge amount of past specifications are decomposed into words, the frequency of appearance in the specifications is machine-learned, and a new requirement specification is input, there are words with a high frequency of appearance learned. A method has been proposed in which the test items are extracted by assembling sentences from the relevant words.

Hirakiba, "Prototype of Spec2Test, a tool for automatically creating test cases from specifications using machine learning", [online], [Search on May 7, 2020], Internet <https://www.slideshare.net/FutaHirakoba / spec2test ＞ Sato et al., "Teacher data creation method for improving machine learning performance in software development efficiency method", IEICE Technical Report, vol. 118, no. 392, NS2018-184, pp. 35-40.

However, with the conventional method, there is a problem that test items cannot be easily generated when unsupported words are included in the specifications. That is, in the conventional method, for example, since learning is performed from a huge amount of past specifications, it is not possible to deal with unsupported words such as words unrelated to the test and new words. In addition, in the conventional method, for example, the applicable range is limited to a specific technology or product, a huge amount of specification learning is required, and it is not known what function the extracted item is related to, so that the test item is simply tested. There was a problem that it could not be generated.

In order to solve the above-mentioned problems and achieve the object, the generator of the present invention has an acquisition unit for acquiring specification data and a chapter character string included in the specification data acquired by the acquisition unit. And the character string of the chapter extracted by the extraction unit, and the extraction unit that extracts the character string of the sentence that does not include the preset expression not related to the test from the character strings of the sentences after the relevant chapter. It is characterized by having a generation unit that generates a list of test items based on a character string of a sentence that does not include expressions unrelated to the test.

According to the present invention, when an unsupported word such as a new word is included in the specification, the test item can be generated accurately and easily.

FIG. 1 is a diagram showing an example of the configuration of the generator according to the first embodiment. FIG. 2 is a diagram showing an example of config information. FIG. 3 is a diagram showing an example of extracted information. FIG. 4 is a diagram showing an example of irrelevant expression data. FIG. 5 is a diagram showing an example of specification data. FIG. 6 is a diagram showing an example of a list of test items. FIG. 7 is a diagram illustrating an outline of a test item list generation process. FIG. 8 is a flowchart showing an example of a flow of a test item list generation process in the generator according to the first embodiment. FIG. 9 is a diagram showing a computer that executes a generation program.

Hereinafter, embodiments of the generator, generation method, and generation program according to the present application will be described in detail with reference to the drawings. It should be noted that the generator, the generation method and the generation program according to the present application are not limited by this embodiment.

[First Embodiment]
In the following embodiment, the configuration of the generation device 10 and the processing flow of the generation device 10 according to the first embodiment will be described in order, and finally, the effect of the first embodiment will be described.

[Generator configuration]
First, the configuration of the generation device 10 will be described with reference to FIG. FIG. 1 is a diagram showing an example of the configuration of the generator according to the first embodiment. The generation device 10 is a device that automatically generates and outputs a list of test items from the required specifications under development in order to improve the efficiency of the development process in system development and the like. For example, the generation device 10 extracts chapters and sentences from a requirement specification document composed of a cover page, a table of contents, a correction history, chapters, sentences, figures, tables, and the like. Then, the generation device 10 refers to the data of the expression unrelated to the test stored in advance, excludes the sentence including the expression unrelated to the test from the extracted sentences, and extracts only the sentence that can be the test item. And automatically generate a list of test items.

As shown in FIG. 1, the generation device 10 includes an input unit 11, an output unit 12, a communication control unit 13, a control unit 14, and a storage unit 15. Each part will be described below.

The input unit 11 is realized by using an input device such as a keyboard or a mouse, and inputs various instruction information such as processing start to the control unit 14 in response to an input operation by the operator. The output unit 12 is realized by a display device such as a liquid crystal display, a printing device such as a printer, or the like. For example, a list of test items is output to the output unit 12.

The communication control unit 13 is a communication interface for transmitting and receiving various information to and from other devices connected via a network or the like. The communication control unit 13 is realized by a NIC (Network Interface Card) or the like, and communicates between the control unit 14 and another device via a telecommunication line such as a LAN (Local Area Network) or the Internet.

The storage unit 15 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk, and is a processing program for operating the generation device 10 or a processing program. Data used during execution is stored. For example, the storage unit 15 has a config information storage unit 15a, an extraction information storage unit 15b, and an irrelevant expression data storage unit 15c.

The config information storage unit 15a stores information for identifying chapters in the specifications, presence / absence of a table of contents in the specifications, and the name of the starting chapter as config information. As illustrated in FIG. 2, for example, the config information storage unit 15a stores "chapter identifier format", "table of contents presence / absence", and "start chapter name" as config information. FIG. 2 is a diagram showing an example of config information.

The config information storage unit 15a stores, for example, "number-number", "number.number", "(number)", etc. as the format of the chapter identifier. Further, the config information storage unit 15a stores "yes" or "no" as the presence or absence of the table of contents. Further, the config information storage unit 15a stores "function 1" in the example of FIG. 2 as the "name of the start chapter". The information stored in the config information storage unit 15a may be manually set or may be automatically set from the data obtained by analyzing the specifications. ..

The extraction information storage unit 15b stores the chapters included in the specifications extracted by the extraction unit 14b, which will be described later, in association with sentences that can be test items belonging to the chapters. For example, as illustrated in FIG. 3, the extraction information storage unit 15b stores "2-1 function 1" as a chapter and "requirement condition sentence 1" as a sentence in association with each other. FIG. 3 is a diagram showing an example of extracted information.

The irrelevant expression data storage unit 15c stores expressions unrelated to the preset test. For example, as illustrated in FIG. 4, the irrelevant expression data storage unit 15c stores expressions such as "is not subject to development", "is no function addition", and "no change" as expressions not related to the test. .. FIG. 4 is a diagram showing an example of irrelevant expression data. The data of the expression not related to the test stored in the irrelevant expression data storage unit 15c shall be obtained by, for example, machine learning. Any method of machine learning may be used. Further, the generation device 10 may perform machine learning and store it in the irrelevant expression data storage unit 15c, or the expression data obtained by performing machine learning by another device and not related to the test may be stored in the irrelevant expression data storage unit. It may be stored in the part 15c.

The control unit 14 has an internal memory for storing a program that defines various processing procedures and required data, and executes various processing by these. For example, the control unit 14 is an electronic circuit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). The control unit 14 has an acquisition unit 14a, an extraction unit 14b, and a generation unit 14c.

The acquisition unit 14a acquires the data of the specifications to be developed. For example, as illustrated in FIG. 5, the acquisition unit 14a accepts input of data of a specification sheet composed of a cover page, a correction history, a table of contents, a chapter, a sentence, and the like. The acquisition unit 14a may accept the input of the data of the specification to be developed via the input unit 11, or may input the data of the specification to be developed from another device via the communication control unit 13. It may be accepted, or the data of the specification to be developed may be read from the storage unit 15.

The extraction unit 14b extracts the character string of the chapter included in the data of the specification acquired by the acquisition unit 14a, and does not include the character string of the sentence after the chapter, which is not related to the preset test. Extract the sentence string. For example, the extraction unit 14b extracts the character string of the chapter and excludes the character string of the sentence including the expression not related to the test stored in the irrelevant expression data storage unit 15c from the character strings of the sentences after the chapter. Then, extract sentences that do not include expressions that are not related to the test. Then, the extraction unit 14b stores the extracted chapter and sentence character strings in the extraction information storage unit 15b.

More specifically, for example, the extraction unit 14b searches from the first page of the specification to specify the description position of the first chapter (extraction start chapter) of the specification, and has a table of contents in the config information. If it is set, the place that matches the name of the start chapter set in the config information for the second time is specified as the description location of the extraction start chapter, and the character string of the chapter is extracted. Further, when the extraction unit 14b is set without a table of contents in the config information, the extraction unit 14b identifies the place where the name of the start chapter set in the config information first matches as the description location of the extraction start chapter, and sets the chapter. Extract the character string.

Further, in order to extract the chapters after the extraction start chapter, the extraction unit 14b starts with an identifier whose format matches the "chapter identifier format" of the config information stored in the config information storage unit 15a, for example, and Extract a string without punctuation as a chapter string. Further, for example, the extraction unit 14b searches for the character string of the sentence after the character string of the chapter, is a character string having a punctuation mark at the end, and is not related to the test stored in the irrelevant expression data storage unit 15c. The character string that does not include the expression is extracted as the sentence of the test item.

The generation unit 14c generates a list of test items based on the character string of the chapter extracted by the extraction unit 14b and the character string of the sentence that does not include the expression unrelated to the test. For example, as illustrated in FIG. 6, the generation unit 14c generates a combination of the chapter character string extracted by the extraction unit 14b and the sentence character string as a test item list. FIG. 6 is a diagram showing an example of a list of test items. The generation unit 14c may output the generated test item list via the output unit 12, transmit it to an external device via the communication control unit 13, or store it in the storage unit 15. You may. The output mode of the list of test items is not limited, and the generation unit 14c may output in any mode.

Here, the process of generating the test item list will be described with reference to FIG. 7. FIG. 7 is a diagram illustrating an outline of a test item list generation process. As illustrated in FIG. 7, the generator 10 reads, for example, the specifications of the electronic file, and extracts the requirement statement and the chapter to which the corresponding statement belongs, which can be the test items after the chapter set in the config information. , Generate a test item list file and write the relevant chapter and requirement statement information.

The generator 10 searches from the first page of the specification to specify the description location of the extraction start chapter, and if it is set with a table of contents in the config information, the start chapter set in the config information. The place where the name is matched for the second time is specified as the description place of the extraction start chapter. Further, when the generation device 10 is set without a table of contents in the config information, the place where the name of the start chapter set in the config information first matches is specified as the description location of the extraction start chapter.

In the example of FIG. 7, in the generator 10, the name of the extraction start chapter set in the config information is "2-1 Function 1", and it is set with a table of contents. Specify as the description location of the extraction start chapter, and store "2-1 Function 1" in the area that temporarily stores the chapter to which the requirement statement belongs. In this area, the chapter name is updated each time the next chapter is found. The generation device 10 recognizes the chapters after the start chapter as chapters that start in the form of the chapter identifier set in the config information and do not include the punctuation mark ".".

Then, the generation device 10 searches for the character strings after "2-1 function 1" and extracts the sentences belonging to the chapter "2-1 function 1". The generation device 10 determines the sentence identification method based on whether or not there is a punctuation mark at the end of the character string. Further, the generation device 10 verifies whether or not the sentence is a requirement condition sentence by collating whether or not the character string registered in the irrelevant expression data is included in the corresponding sentence, and if it is not included, determines that the sentence is a request condition sentence. do.

In the example of FIG. 7, the generation device 10 extracts, for example, "requirement condition sentence 1 to be developed" as a sentence belonging to the chapter "2-1 function 1". The generation device 10 verifies whether the "requirement condition statement 1 to be developed" contains the character string registered in the irrelevant expression data, and determines that the character string registered in the irrelevant expression data is not included. 2-1 Write "Requirement statement 1 for development target" as a test item in the test item list file in association with "Function 1".

Further, the generation device 10 extracts, for example, "a sentence that defines the non-development target" as a sentence that belongs to the chapter "2-1 Function 1". The generator 10 verifies whether or not the character string registered in the irrelevant expression data is included in the "sentence that defines the non-development target.", And determines that the character string registered in the irrelevant expression data is included. Exclude "sentences that specify non-development targets" as a test item.

The generation device 10 repeats the process of extracting the above-mentioned sentences and chapters from the extraction start chapter of "2-1 Function 1" to the end of the specification. Then, when the processing is completed up to the end of the specification, the generation device 10 outputs the combination of the chapter written in the test item list file and the test item list file. For example, in the example of FIG. 7, the generator 10 has the chapter "2-1 function 1" and the test item (requirement sentence) "requirement condition sentence 1 to be developed", and the chapter "2-1 function 1" and the test. Outputs a list of test items including the item (requirement sentence) "Requirement condition sentence 2 for development target" and the chapter "2-1 Function 2" and the test item (requirement sentence) "Requirement condition sentence 3 for development target". do.

As described above, when the data of the expression unrelated to the test is included in the character string of the sentence of the specification, the generation device 10 does not extract it as a sentence that can be a test item. Further, the generation device 10 combines the function of identifying chapters and the function of identifying sentences with the above functions, so that the test item list is tested by associating a sentence that can be a test item with the name of the chapter to which it belongs. Output as an item list. Therefore, the generation device 10 can generate test items accurately and easily when unsupported words such as new words are included in the specifications.

[Processing procedure of generator]
Next, an example of a test item list generation processing procedure by the generation device 10 according to the first embodiment will be described with reference to FIG. FIG. 8 is a flowchart showing an example of a flow of a test item list generation process in the generator according to the first embodiment.

As illustrated in FIG. 8, the acquisition unit 14a of the generation device 10 acquires the specification file (step S101). The extraction unit 14b refers to the name of the start chapter of the config information, identifies the extraction start chapter in the specification, and searches for the subsequent character strings (step S102).

Then, the extraction unit 14b determines whether or not there is a punctuation mark at the end of the searched character string (step S103). As a result, when the extraction unit 14b determines that there is no punctuation mark at the end of the searched character string (denial in step S103), the extraction unit 14b proceeds to the process of step S107.

Further, when the extraction unit 14b determines that there is a punctuation mark at the end of the searched character string (affirmation in step S103), the extraction unit 14b determines whether the character string includes the character string registered in the irrelevant expression data storage unit 15c. (Step S104). As a result, when the extraction unit 14b determines that the character string registered in the irrelevant expression data storage unit 15c is included (step S104 affirmative), the extraction unit 14b proceeds to the process of step S107.

Further, when the extraction unit 14b determines that the character string registered in the irrelevant expression data storage unit 15c is included (negation in step S104), the extraction unit 14b updates the sentence setting field of the extraction information storage unit 15b (step S105). ).

Then, the extraction unit 14b writes the chapter and sentence information in the extraction information storage unit 15b in the test item list file (step S106). Subsequently, the extraction unit 14b searches for the next character string (step S107) and determines whether or not there is a character string (step S108).

As a result, when the extraction unit 14b determines that there is a character string (step S108 affirmative), the extraction unit 14b determines whether the character string is a chapter (step S109). For example, the extraction unit 14b determines that the character string is a chapter when the character string starts in the form of the chapter identifier set in the config information and does not include the punctuation mark ".".

Then, the extraction unit 14b updates the setting field of the chapter of the extraction information storage unit 15b, searches for the subsequent character strings (step S110), and returns to step S103. Further, in step S108, when the extraction unit 14b determines that there is no character string (denial in step S108), the extraction unit 14b ends the process of this flowchart. After that, the generation unit 14c generates and outputs a list of test items from the chapter and sentence information written in the test item list file at a predetermined timing.

[Effect of the first embodiment]
In this way, the generation device 10 acquires the data of the specification, extracts the character string of the chapter included in the acquired data of the specification, and sets a preset test among the character strings of the sentences after the chapter. Extract the character string of the sentence that does not contain the expression that is not related to. Then, the generation device 10 generates a list of test items based on the character string of the extracted chapter and the character string of the sentence not including the expression not related to the test. As a result, the generation device 10 can generate test items accurately and easily even when unsupported words such as new words are included in the specifications.

That is, since the generation device 10 does not extract expressions unrelated to the test as sentences of test items, it is possible to generate test items even if uncorresponding words such as new words are included. Further, since the generator 10 does not depend on a specific technique, it can be applied to specifications of various products.

Further, the generator 10 stores expressions not related to the test obtained by machine learning, extracts the character strings of the chapter, and expresses the character strings of the sentences after the chapter that are not related to the stored test. Exclude the strings of sentences that include, and extract sentences that do not contain expressions that are not related to the test. Therefore, the generation device 10 can generate test items accurately and easily without learning using a huge number of specifications. Further, since the expressions not related to the test do not depend on a specific function, the variations of the expressions unrelated to the test are limited, and it is not necessary to learn a huge amount of specifications as in the prior art. That is, unlike the conventional approach of learning correct answer data (data to be tested), the generation device 10 can easily generate test items by using the incorrect answer data obtained by learning.

Further, the generation device 10 starts with an identifier that matches the format of the preset chapter identifier as the character string of the chapter included in the data of the specification, and uses a character string without kuten as the character string of the chapter. Extract. Therefore, the generation device 10 can accurately extract the character string of the chapter and output it together with the test items.

Further, the generation device 10 searches for the character string after the character string of the chapter, and extracts the character string having a punctuation mark at the end and not including the expression unrelated to the test as the sentence of the test item. do. Therefore, the generation device 10 can accurately extract the sentences of the test items.

Further, the generation device 10 generates a combination of the extracted chapter character string and the sentence character string as a list of test items. Therefore, the generator 10 can output the test item and the chapter name together, and can clarify what function the test item relates to.

[System configuration, etc.]
Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of them may be functionally or physically distributed / physically distributed in any unit according to various loads and usage conditions. Can be integrated and configured. Further, each processing function performed by each device may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware by wired logic.

Further, among the processes described in the present embodiment, all or part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed. It is also possible to automatically perform all or part of the above by a known method. In addition, the processing procedure, control procedure, specific name, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified.

[program]
FIG. 9 is a diagram showing a computer that executes a generation program. The computer 1000 has, for example, a memory 1010 and a CPU 1020. The computer 1000 also has a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. Each of these parts is connected by a bus 1080.

The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012. The ROM 1011 stores, for example, a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to the hard disk drive 1090. The disk drive interface 1040 is connected to the disk drive 1100. For example, a removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to, for example, a mouse 1051 and a keyboard 1052. The video adapter 1060 is connected to, for example, the display 1061.

The hard disk drive 1090 stores, for example, the OS 1091, the application program 1092, the program module 1093, and the program data 1094. That is, the program that defines each process of the generation device 10 is implemented as a program module 1093 in which a code that can be executed by a computer is described. The program module 1093 is stored in, for example, the hard disk drive 1090. For example, the program module 1093 for executing the same processing as the functional configuration in the apparatus is stored in the hard disk drive 1090. The hard disk drive 1090 may be replaced by an SSD (Solid State Drive).

Further, the data used in the processing of the above-described embodiment is stored as program data 1094 in, for example, a memory 1010 or a hard disk drive 1090. Then, the CPU 1020 reads the program module 1093 and the program data 1094 stored in the memory 1010 and the hard disk drive 1090 into the RAM 1012 and executes them as needed.

The program module 1093 and the program data 1094 are not limited to those stored in the hard disk drive 1090, but may be stored in, for example, a removable storage medium and read by the CPU 1020 via the disk drive 1100 or the like. Alternatively, the program module 1093 and the program data 1094 may be stored in another computer connected via a network or WAN. Then, the program module 1093 and the program data 1094 may be read from another computer by the CPU 1020 via the network interface 1070.

10 Generator 11 Input unit 12 Output unit 13 Communication control unit 14 Control unit 14a Acquisition unit 14b Extraction unit 14c Generation unit 15 Storage unit 15a Config information storage unit 15b Extraction information storage unit 15c Unrelated expression data storage unit

Claims (7)

The acquisition unit that acquires the data of the specifications, and
The character strings of the chapters included in the data of the specifications acquired by the acquisition unit are extracted, and among the character strings of the sentences after the chapter, the character strings of the sentences that do not include the expressions unrelated to the preset test are selected. Extraction part to extract and
A generator characterized by having a generation unit that generates a list of test items based on a character string of a chapter extracted by the extraction unit and a character string of a sentence that does not include expressions unrelated to the test. .. A storage unit that stores expressions that are not related to the test obtained by machine learning,
The extraction unit extracts the character string of the chapter, and excludes the character string of the sentence including the expression not related to the test stored in the storage unit from the character strings of the sentences after the chapter, and the test. The generator according to claim 1, wherein a sentence not including an expression unrelated to the above is extracted. The extraction unit extracts a character string of a chapter included in the data of the specification, which starts with an identifier that matches the format of the preset chapter identifier and has no punctuation marks, as the character string of the chapter. The generator according to claim 1, wherein the generator is characterized by the above. The extraction unit searches for a character string after the character string in the chapter, and uses a character string having a punctuation mark at the end and not including an expression unrelated to the test as a sentence of the test item. The generator according to claim 1, wherein the generator is extracted. The generation device according to claim 1, wherein the generation unit generates a combination of a chapter character string and a sentence character string extracted by the extraction unit as a list of test items. A generation method performed by a generator,
The acquisition process to acquire the specification data and
The character strings of the chapters included in the data of the specifications acquired by the acquisition process are extracted, and among the character strings of the sentences after the chapter, the character strings of the sentences that do not include the expressions unrelated to the preset test are selected. The extraction process to extract and
A generation method including a generation step of generating a list of test items based on a character string of a chapter extracted by the extraction step and a character string of a sentence not including expressions unrelated to the test. .. The acquisition step to acquire the data of the specification, and
The character strings of the chapters included in the data of the specifications acquired by the acquisition step are extracted, and among the character strings of the sentences after the chapter, the character strings of the sentences that do not include the expressions unrelated to the preset test are selected. Extraction steps to extract and
It is characterized by having a computer execute a generation step that generates a list of test items based on the character string of the chapter extracted by the extraction step and the character string of a sentence that does not include expressions unrelated to the test. Generation program to do.

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