Data processing device, data processing method and data processing program

Abstract

To obtain a suitable test item on each specification item in a plurality of viewpoints.SOLUTION: A data processing apparatus comprises: a category classifying section 201 that analyzes for each specification item an appearing tendency of a word in new specifications data 600 including a plurality of specification items to describe a specification of an object of development using one or more words and classifies each specification item into any of a plurality of test categories each of which is of a category of testing, based on an analysis result of appearing tendency of the word on each specification item; and a test-item presenting section 202 that refers to past classification-result information 153 associating a test item describing a test content with a test category and presents for each specification item a test item that the specification item is associated with a classified category through the past classification result information 153.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This disclosure relates to testing performed in product development. [Background technology]

[0002] In product development, testing is carried out to check the performance of the product under development. For example, in software development, testing is carried out to determine whether the software under development complies with the requirements and design specifications in the specifications. Prior to testing, the software developer (test designer) extracts the areas to be tested (test points) from the specifications from multiple perspectives. The test designer then designs test items from the extracted test points. Test designers consider multiple perspectives, for example, envisioning the types of tests (functional tests, non-functional tests) that correspond to the descriptions of items (specification items) included in the specifications, and consider the goals that each test should achieve, the parameters that each test should verify, etc. Non-functional tests include stress tests, security tests, disaster recovery tests, etc. Then, test designers design test items that specify the envisioned test goals, parameters, etc. for each specification item.

[0003] This work requires understanding the content of the specifications and designing test items from multiple perspectives. As a result, it takes a long time to go from understanding the specifications to designing test items. Furthermore, this work is heavily dependent on the skills and experience of the test designer. For this reason, there is a demand for technology that can automatically extract test points from the content of the specifications and design test items.

[0004] For example, Patent Document 1 discloses a technology that tags test sections in software specifications and automatically generates test items from the tags. More specifically, Patent Document 1 analyzes the text of the specifications using natural language processing and tags specific sentence structures. Test items are then automatically generated from the tagged specifications. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-126100 Summary of the Invention [Problem to be solved by the invention]

[0006] As mentioned above, for each specification item, the test designer envisions the tests that should be performed in response to that specification item, and designs test items that specify the goals, parameters, etc. of the envisioned tests. In other words, for each specification item, the test designer designs test items that correspond to the description of the specification item. However, the technology in Patent Document 1 only assigns tags to specific sentence structures within the specifications. As a result, the technology in Patent Document 1 has the problem that it cannot design test items from sentences that do not fit the sentence structure, and therefore cannot obtain appropriate test items for each specification item.

[0007] One of the main objectives of the present disclosure is to solve the above-mentioned problems. Specifically, the main objective is to obtain appropriate test items for each specification item from multiple perspectives. [Means for solving the problem]

[0008] The data processing device according to the present disclosure includes: a category classification unit that analyzes, for each specification item, the appearance tendency of words in specification data including a plurality of specification items each describing the specification of a development object using one or more words, and classifies each specification item into one of a plurality of test categories, each of which is a test category, based on the analysis result of the appearance tendency of words for each specification item; The test item presentation unit refers to test item category information that associates test items that describe test content with test categories, and presents, for each specification item, the test category into which the specification item is classified and the test items that are associated in the test item category information. [Effects of the Invention]

[0009] According to the present disclosure, appropriate test items can be obtained for each specification item from multiple perspectives. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing an example of a functional configuration of a test design support system according to a first embodiment. [Figure 2] FIG. 1 is a diagram showing an example of a hardware configuration of a test design support system according to a first embodiment. [Figure 3] 4 is a flowchart showing an example of the operation of the learning device according to the first embodiment. [Figure 4] 4 is a flowchart showing an example of the operation of the test item presentation device according to the first embodiment. [Figure 5] FIG. 4 is a diagram showing an example of test item appearance tendency information according to the first embodiment. [Figure 6] FIG. 3 is a diagram showing an example of the operation of a category classification unit of the learning device according to the first embodiment. [Figure 7] FIG. 4 is a diagram showing an example of past classification result information according to the first embodiment. [Figure 8] FIG. 4 is a diagram showing an example of the operation of a learning unit according to the first embodiment. [Figure 9] 4 is a diagram showing an example of the operation of a category classification unit of the test item presentation device according to the first embodiment. FIG. [Figure 10] FIG. 4 is a diagram showing an example of new classification result information according to the first embodiment. [Figure 11] FIG. 3 is a diagram showing an example of test item information according to the first embodiment. [Figure 12] FIG. 10 is a diagram showing an example of the functional configuration of a test design support system according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments will be described with reference to the drawings. In the following description of the embodiments and the drawings, the same reference numerals denote the same or corresponding parts.

[0012] Embodiment 1 ***Configuration Description*** FIG. 1 shows an example of the functional configuration of a test design support system 300 according to this embodiment. FIG. 2 shows an example of the hardware configuration of a test design support system 300 according to this embodiment.

[0013] First, an example of the functional configuration of a test design support system 300 will be described with reference to FIG. The test design support system 300 is composed of a learning device 100 and a test item presentation device 200 . In this embodiment, we assume that test design support system 300 is implemented on a single computer. That is, in this embodiment, we assume that learning device 100 and test item presentation device 200 are implemented on a single computer. However, learning device 100 and test item presentation device 200 may also be implemented on two or more different computers.

[0014] The learning device 100 acquires the past specification data 400 and the past test item data 500, and performs learning using the past specification data 400 and the past test item data 500. The test item presentation device 200 analyzes the new specification data 600 using the learning results of the learning device 100, and presents test items corresponding to the new specification data 600 as test item information 252 to the user (test design engineer) of the test design support system 300. The operating procedure of learning device 100 corresponds to a learning method, and the program that realizes the operation of learning device 100 corresponds to a learning program. The test item presentation device 200 corresponds to a data processing device. The operation procedure of the test item presentation device 200 corresponds to a data processing method. The program that realizes the operation of the test item presentation device 200 corresponds to a data processing program.

[0015] The past specification data 400 is specification data that was generated in the past. The past test item data 500 is test item data that was generated in the past. The new specification data 600 is newly generated specification data.

[0016] Specifications of development objects (e.g., software) are described in the past specification data 400 and the new specification data 600. The past specification data 400 and the new specification data 600 include requirements definition documents, basic design documents, detailed design documents, program design specifications, etc. The past specification data 400 and the new specification data 600 include multiple specification items. A specification item is a detailed specification of the development target, and is a sentence described in each specification document. For example, in a requirements definition document, it is a sentence described in items such as basic requirements, functional requirements, etc. Each specification item is described using one or more words. The specification items included in the past specification data 400 are called past specification items. The specification items included in the new specification data 600 are called new specification items.

[0017] The past test item data 500 describes the test contents for the development object. There are multiple tests depending on the development stage. For example, unit tests, integration tests, system tests, etc. exist depending on the development stage. There are also multiple tests from the perspective of quality. For example, from the perspective of quality, there are function tests, stress tests, security tests, and disaster recovery tests. In this embodiment, the types of tests classified from the perspective of quality are called test categories. The past test item data 500 includes a plurality of test items. A test item is a detailed description of the test implementation. The test item describes the environment in which the test is performed, the parameters used in the test, the test judgment conditions, etc. Each test item is described using one or more words. Hereinafter, the test items included in the past test item data 500 are referred to as past test items.

[0018] In the learning device 100, the similarity calculation unit 101 acquires multiple pieces of past specification data 400 and multiple pieces of past test item data 500. The similarity calculation unit 101 may acquire multiple pieces of past specification data 400 and multiple pieces of past test item data 500 from outside the learning device 100, or may acquire multiple pieces of past specification data 400 and multiple pieces of past test item data 500 from an auxiliary storage device 903 (described later) of the test design support system 300. It is assumed that the past specification data 400 and the past test item data 500 are associated with each other. In other words, there are a plurality of pairs of the past specification data 400 and the past test item data 500. The similarity calculation unit 101 acquires the past specification data 400 and the past test item data 500 for each pair. Then, the similarity calculation unit 101 calculates the similarity between each of the multiple words included in the past specification data 400 and each of the multiple words included in the past test item data 500 between the paired past specification data 400 and past test item data 500. The similarity calculation unit 101 calculates the similarity for each pair of the past specification data 400 and the past test item data 500 . The similarity calculation unit 101 outputs similarity information 151 indicating the calculation result of the similarity to the association unit 102. When the similarity calculation unit 101 acquires the past specification data 400 and the past test item data 500 from outside, it stores the acquired past specification data 400 and past test item data 500 in the auxiliary storage device 903. In the following description, when the association unit 102, the category classification unit 103, the learning unit 104, the category classification unit 201, and the test item presentation unit 202 refer to the past specification data 400 and / or the past test item data 500, it is assumed that the past specification data 400 and / or the past test item data 500 have been read from the auxiliary storage device 903. The processing performed by the similarity calculation unit 101 corresponds to the similarity calculation processing in the learning program.

[0019] The association unit 102 acquires the similarity information 151 . Furthermore, the association unit 102 associates past specification items with past test items between the paired past specification data 400 and past test item data 500 based on the similarity indicated in the similarity information 151. The past specification items and past test items associated by the association unit 102 are called corresponding specification items and corresponding test items, respectively. A plurality of pairs of corresponding specification items and corresponding test items are obtained between the paired past specification data 400 and past test item data 500. The association unit 102 associates the corresponding specification items with the corresponding test items for each of a plurality of pairs of the past specification data 400 and the past test item data 500 . Then, the association unit 102 outputs association information 152 indicating the association results to the categorization unit 103 and the test item presentation device 200. The processing performed by the association unit 102 corresponds to the association processing in the learning program.

[0020] The categorization unit 103 acquires the association information 152 and the test item appearance tendency information 150. The categorization unit 103 may acquire the test item appearance tendency information 150 from outside the learning device 100, or may acquire the test item appearance tendency information 150 from the auxiliary storage device 903. Furthermore, the categorization unit 103 analyzes the words in the corresponding test items for each pair of corresponding specification items and corresponding test items shown in the association information 152. If a numerical value is included as a word, the categorization unit 103 analyzes the appearance tendency of the word in the corresponding test items, including the appearance tendency of the numerical value. Then, based on the analysis result and the test item appearance tendency information 150, the categorization unit 103 classifies the corresponding test item into one of multiple test categories. The test item appearance tendency information 150 indicates, for each test category, the appearance tendency of words in test items classified into that test category. The categorization unit 103 categorizes the corresponding test items into test categories whose appearance trends are indicated in the test item appearance trend information 150 and match the appearance trends identified by the analysis. Furthermore, the categorization unit 103 categorizes the corresponding specification items into the same test category as the corresponding test items. Then, the category classification unit 103 outputs past classification result information 153 indicating the classification results to the learning unit 104 and the test item presentation device 200. The past classification result information 153 is information that associates past test items with test categories, and corresponds to test item category information. The processing performed by the category classification unit 103 corresponds to the category classification processing in the learning program.

[0021] The learning unit 104 acquires the past classification result information 153 . Furthermore, the learning unit 104 refers to the past classification result information 153 and learns, for each test category, the tendency of words to appear in the corresponding specification items classified into the test category in the plurality of past specification data 400. Then, the learning unit 104 outputs to the test item presentation device 200 specification item appearance tendency information 154 indicating the appearance tendency of words in a plurality of pieces of past specification data 400 for each test category. The processing performed by the learning unit 104 corresponds to the learning processing in the learning program.

[0022] In the test item presentation device 200 , the category classification unit 201 acquires the new specification data 600 , the association information 152 , and the specification item appearance tendency information 154 . The category classification unit 201 may acquire the new specification data 600 from outside the test item presentation device 200 or may acquire the new specification data 600 from the auxiliary storage device 903 . Then, the categorization unit 201 analyzes the appearance trends of words in the new specification data 600 for each new specification item. If a numerical value is included as a word, the categorization unit 201 analyzes the appearance trends of words in the new specification data 600, including the appearance trends of numerical values. Furthermore, the categorization unit 201 classifies each new specification item into one of the test categories based on the analysis results of the appearance trends of words for each new specification item. Specifically, the categorization unit 201 refers to the specification item appearance trend information 154 and classifies each new specification item into a test category whose appearance trend is indicated in the specification item appearance trend information 154 and matches the appearance trend determined by the analysis. Then, the categorization unit 201 outputs new classification result information 251 indicating the classification result to the test item presenting unit 202. When the category classification unit 201 acquires the new specification data 600 from outside, it stores the acquired new specification data 600 in the auxiliary storage device 903. In the following description, when the test item presentation unit 202 refers to the new specification data 600, it is assumed that the new specification data 600 has been read from the auxiliary storage device 903. The processing performed by the category classification unit 201 corresponds to the category classification processing in the data processing program.

[0023] The test item presenting unit 202 acquires the past classification result information 153 and the new classification result information 251 . Furthermore, the test item presentation unit 202 refers to the new classification result information 251 and recognizes the test category into which each new specification item is classified. Then, the test item presentation unit 202 refers to the past classification result information 153 and presents, for each new specification item, the past test items specified in the past classification result information 153 for the test category into which each new specification item is classified. Specifically, the test item presenting unit 202 generates test item information 252 that indicates the past test items to be presented for each new specification item. Then, the test item presenting unit 202 displays the test item information 252 on the display of the input / output device 905, which will be described later. The processing performed by the test item presenting unit 202 corresponds to the test item presenting processing in the data processing program.

[0024] Next, an example of the hardware configuration of the test design support system 300 will be described with reference to FIG.

[0025] The test design support system 300 according to this embodiment is a computer. The test design support system 300 includes, as hardware, a processor 901, a main storage device 902, an auxiliary storage device 903, a communication device 904, and an input / output device 905. The functions of the components of the learning device 100 (similarity calculation unit 101, association unit 102, category classification unit 103, and learning unit 104) and the components of the test item presentation device 200 (category classification unit 201 and test item presentation unit 202) shown in FIG. 1 are realized, for example, by a program. Auxiliary storage device 903 stores programs that implement the functions of the components of learning device 100 and test item presentation device 200. These programs are loaded from the auxiliary storage device 903 into the main storage device 902. The processor 901 then executes these programs to perform the operations of the components of the learning device 100 and the components of the test item presentation device 200. FIG. 2 schematically shows a state in which processor 901 is executing a program that implements the functions of the components of learning device 100 and test item presentation device 200. The communication device 904 is used for communication with the outside of the test design support system 300 . The input / output device 905 includes, for example, a keyboard, a mouse, and a display.

[0026] ***Explanation of Operation*** Next, an example of the operation of the test design support system 300 according to this embodiment will be described. Fig. 3 shows an example of the operation of the study device 100. Fig. 4 shows an example of the operation of the test item presentation device 200. First, an example of the operation of the learning device 100 will be described with reference to FIG. 3, steps S102 to S104 are performed for each pair of the past specification data 400 and the past test item data 500. Step S105 is performed for each test category.

[0027] In step S101, the similarity calculation unit 101 acquires a plurality of pieces of past specification data 400 and a plurality of pieces of past test item data 500.

[0028] In step S102, the similarity calculation unit 101 calculates the similarity between words. That is, the similarity calculation unit 101 calculates the similarity between the words included in the past specification data 400 and the words included in the past test item data 500 . Here, step S102 will be described in detail using a specific example. In the following description, it is assumed that the past specification data 400 includes three past specification items (A1, A2, A3). It is also assumed that the past test item data 500 includes three past test items (Z1, Z2, Z3).

[0029] The similarity calculation unit 101 performs a morphological analysis of the past specification items and divides the past specification items into words. Here, it is assumed that a past specification item A1 is divided into words A11, A12, and A13. Also, it is assumed that the past specification item A2 is divided into words A21, A22, and A23. Furthermore, it is assumed that the past specification item A3 is divided into words A31, A32, and A33. Furthermore, the similarity calculation unit 101 performs a morphological analysis of the past test items and divides the past test items into words. Here, it is assumed that past test item Z1 is divided into words Z11, Z12, and Z13. Also, it is assumed that past test item Z2 is divided into words Z21, Z22, and Z23. Furthermore, it is assumed that past test item Z3 is divided into words Z31, Z32, and Z33.

[0030] Next, the similarity calculation unit 101 performs vector conversion on each word. That is, the similarity calculation unit 101 converts each of the words A11, A12, A13,... A31, A32, A33 into vector data. Furthermore, the similarity calculation unit 101 converts each of the words Z11, Z12, Z13,... Z31, Z32, Z33 into vector data. The similarity calculation unit 101 performs vector conversion using, for example, an existing technique, such as Word2Vec.

[0031] Next, the similarity calculation unit 101 uses the vector data to calculate the similarity between each word in the past specification item and each word in the past test item. Specifically, the similarity calculation unit 101 calculates the similarity between word A11 and each of words Z11, Z12, Z13,... Z31, Z32, and Z33. The similarity calculation unit 101 also calculates the similarity between word A12 and each of words Z11, Z12, Z13,... Z31, Z32, and Z33. The similarity calculation unit 101 also calculates the similarity between word A13 and each of words Z11, Z12, Z13,... Z31, Z32, and Z33. The same applies to words in other past specification items. The similarity calculation unit 101 calculates the similarity between words by using, for example, an existing technique, such as cosine similarity.

[0032] When the similarity calculation unit 101 has completed the calculation of the similarity, it outputs similarity information 151 indicating the calculation result of the similarity to the association unit 102. For example, if the similarity between word A11 and word Z11 is 0.7, then "A11-Z11-0.7" is indicated in the similarity information 151. Note that the method for describing the similarity information 151 is not limited to this.

[0033] Next, in step S103, the association unit 102 associates the past specification items with the past test items. More specifically, the association unit 102 refers to the similarity information 151 and associates the past specification items with the past test items based on the similarity between words. For example, it is assumed that the similarity between word A11 and word Z11 is high, and the similarity between word A12 and word Z13 is high. In this case, the association unit 102 associates the past specification item A1 with the past test item Z1. The level of similarity required for the associating unit 102 to associate the past specification items with the past test items is arbitrarily determined by the user of the test design support system 300 .

[0034] The association unit 102 outputs association information 152 indicating the association result to the categorization unit 103 and the categorization unit 201. For example, when a past specification item A1 and a past test item Z1 are associated with each other, "A1-Z1" is indicated in the association information 152. Note that the description method of the association information 152 is not limited to this.

[0035] Next, in step S104, the categorization unit 103 categorizes the past specification items and past test items into any one of the test categories. For example, the categorization unit 103 categorizes the past specification items and past test items into any one of the test categories in the following procedure.

[0036] The categorization unit 103 analyzes the appearance tendency of words in each past test item. The categorization unit 103 also refers to the test item appearance tendency information 150. The categorization unit 103 then categorizes the past test item into a test category whose appearance tendency, which matches the appearance tendency determined by the analysis, is indicated in the test item appearance tendency information 150. For example, assume that a past test item is "500 people accessing simultaneously for 60 minutes." The word appearance tendency for this past test item is "500 (numeral) people (numeral particles) access (noun) simultaneously (noun) for 60 (numeral) minutes (numeral particles) (verb)." The category classification unit 103 collates the test item appearance tendency information 150 illustrated in FIG. 5. FIG. 5 shows the word appearance tendency in the stress test. The appearance tendency of words in the past test items matches "simultaneous" and "access" in the "related terms" of the stress test, "numbers" in the metrics (numerical particles), and "person" and "minutes" in the units (numerical particles), as shown in Figure 6. Therefore, the categorization unit 103 categorizes the past test item in question as a stress test. Furthermore, the categorization unit 103 also categorizes past specification items (corresponding specification items) associated with the past test items (corresponding test items) as stress tests.

[0037] The category classification unit 103 outputs to the learning unit 104 past classification result information 153 indicating the classification results for past specification items (corresponding specification items) and past test items (corresponding test items). The category classification unit 103 also outputs the past classification result information 153 to the test item presentation device 200 . In the past classification result information 153, as shown in FIG. 7, the tag of the corresponding test category and the serial number are set for the pair of the past specification item (corresponding specification item) and the past test item (corresponding test item). For example, if a past specification item A1 and a past test item Z1 are associated with each other and are classified as a stress test, then, for example, "A1-Z1-ST01" is indicated in the past classification result information 153. Note that the description method of the past classification result information 153 is not limited to that shown in FIG.

[0038] Next, in step S105, the learning unit 104 learns the tendency of words to appear in past specification items for each test category. For example, the learning unit 104 learns the tendency of words to appear in past specification items in the following procedure.

[0039] FIG. 8 shows an example of a procedure for learning the appearance tendency of words by the learning unit 104. As shown in past classification result information 153 in FIG. 7, past specification items A1, A2, A54, A55, etc. are classified as stress test (tag ST). As shown in FIG. 8, past specification item A1 describes "up to 100 users shall be allowed to log in simultaneously on weekdays." Furthermore, past specification item A2 describes "up to 300 users shall be allowed to log in simultaneously on holidays." Furthermore, past specification item A54 describes "CPU usage rate shall be kept within a maximum of 25% on weekdays." Furthermore, past specification item A55 describes "CPU usage rate shall be kept within a maximum of 40% on holidays." 7, there are stress test tags ST01 to ST20, and 20 past specification items are classified as stress tests. However, for drawing purposes, only past specification items A1, A2, A54, and A55 are shown in FIG. The learning unit 104 analyzes the appearance trends of words in 20 past specification items to which stress test tags ST01 to ST20 are set. For simplicity of explanation, the following mainly describes an example in which the learning unit 104 analyzes the appearance trends of words in past specification items A1, A2, A54, and A55.

[0040] The learning unit 104 performs a morphological analysis on each of the past specification items for each test category, and divides each of the past specification items into words. The learning unit 104 may use the division result obtained by the similarity calculation unit 101 in step S102.

[0041] Then, the learning unit 104 extracts frequently occurring words in the past specification items and describes the extracted frequently occurring words in the specification item appearance tendency information 154 . 8, the learning unit 104 enters "weekday (noun)" that appears in past specification item A1 and past specification item A54 as a frequently occurring word in the "related term" column of the specification item appearance tendency information 154. In addition, the learning unit 104 enters past specification item A1 and past specification item A54, which are the sources from which "weekday (noun)" was extracted, in the "extraction source" column of the specification item appearance tendency information 154. Furthermore, the learning unit 104 enters "holiday (noun)" that appears in past specification item A2 and past specification item A55 as a frequently occurring word in the "related term" column of the specification item appearance tendency information 154. Furthermore, the learning unit 104 enters past specification item A2 and past specification item A55, from which "holiday (noun)" was extracted, in the "extraction source" column of the specification item appearance tendency information 154. Furthermore, the learning unit 104 enters "user (noun)," "simultaneous," and "login (noun)," which appear in the past specification items A1 and A2, as frequently occurring words in the "related terms" column of the specification item appearance tendency information 154. Furthermore, the learning unit 104 enters the past specification items A1 and A2, which are the sources from which these words were extracted, in the "extraction source" column of the specification item appearance tendency information 154. Furthermore, the learning unit 104 enters "CPU usage rate (noun)" and "maximum (noun)" that appear in past specification item A54 and past specification item A55 as frequently occurring words in the "related term" column of the specification item appearance tendency information 154. Furthermore, the learning unit 104 enters past specification item A54 and past specification item A55, which are the sources from which "CPU usage rate (noun)" and "maximum (noun)" were extracted, in the "extraction source" column of the specification item appearance tendency information 154.

[0042] Furthermore, the learning unit 104 extracts metrics, units, and ranges related to the extracted frequently occurring words, and describes the extracted metrics, units, and ranges in the specification item appearance tendency information 154 . For example, in the example of FIG. 8 , the learning unit 104 extracts "100" and "300," which are numerical values ​​(numerals) associated with the frequently occurring word "user (noun)." Then, the learning unit 104 learns that numbers tend to appear in association with "user (noun)," and describes "numbers, Chinese numerals" as metrics. Furthermore, the learning unit 104 learns that "person" appears as a unit for "100" and "300," which are numerical values ​​associated with "user (noun)." The learning unit 104 describes "person" as a unit in the specification item appearance tendency information 154. Furthermore, the learning unit 104 learns that "up to" appears as a range for "100" and "300," which are numerical values ​​associated with "user (noun)." The learning unit 104 describes "up to" as a range in the specification item appearance tendency information 154. In the example of Figure 8, the learning unit 104 also extracts "numbers, Chinese numerals" as metrics, "%, percent" as units, and "within" as range for the frequently occurring word "CPU usage rate (noun)", and describes these in the specification item appearance tendency information 154.

[0043] The learning unit 104 performs the above procedure for each test category to complete the specification item appearance tendency information 154. Then, in step S106, the learning unit 104 outputs the specification item appearance tendency information 154 to the test item presentation device 200.

[0044] Next, an example of the operation of the test item presentation device 200 will be described with reference to FIG. In FIG. 4, steps S203 to S205 are performed for each new specification item.

[0045] First, in step S201, the test item presenting unit 202 acquires the past classification result information 153, and the category classifying unit 201 acquires the association information 152 and the specification item appearance tendency information 154. The past classification result information 153, the association information 152, and the specification item appearance tendency information 154 may be acquired at the same time or at different times.

[0046] Next, in step S202, the category classification unit 201 acquires the new specification data 600. Here, it is assumed that the new specification data 600 includes new specification items B1 to B4 as shown in FIG. It is assumed that the new specification item B1 describes that "up to 1000 users can log in simultaneously during the day." It is assumed that the new specification item B2 describes that "up to 2000 users can log in simultaneously at night." The new specification item B3 states that "CPU usage should be kept to a maximum of 30 percent during the day." The new specification item B4 states that "CPU usage should be kept to a maximum of 50 percent during the night."

[0047] Next, in step S203, the category classification unit 201 classifies each new specification item included in the new specification data 600 into one of the test categories. Specifically, the categorization unit 201 performs a morphological analysis of the new specification item and divides the new specification item into words. Then, the categorization unit 201 analyzes the appearance trends of words in the new specification item. Furthermore, the categorization unit 201 compares the appearance trends of words in the new specification item with the appearance trends of words indicated in the specification item appearance trend information 154. Then, the categorization unit 201 extracts a test category indicating the appearance trends of words that match the appearance trends of words in the new specification item, and classifies the new specification item into the extracted test category.

[0048] 9, among the words included in new specification item B1, "user," "1000," "people," "up to," "simultaneous," and "login" match the appearance tendency of stress test in specification item appearance tendency information 154. Therefore, the categorization unit 201 classifies new specification item B1 as stress test. Similarly, among the words included in the new specification item B2, “user,” “2000 people,” “up to,” “simultaneous,” and “login” match the appearance tendency of the stress test in the specification item appearance tendency information 154. Therefore, the category classification unit 201 classifies the new specification item B2 as a stress test. Furthermore, among the words included in new specification item B3, "CPU usage rate," "30," "percent," "within," and "maximum" match the appearance tendency of stress test in specification item appearance tendency information 154. Therefore, the category classification unit 201 classifies new specification item B3 as stress test. Similarly, among the words included in new specification item B4, "CPU usage rate," "50," "percent," "within," and "maximum" match the appearance tendency of stress test in specification item appearance tendency information 154. Therefore, the category classification unit 201 classifies new specification item B4 as stress test. The degree of agreement between the appearance tendency of words in a new specification item and the appearance tendency of words in the specification item appearance tendency information 154, which is required to distinguish the test category, can be determined arbitrarily by the user.

[0049] After categorizing all new specification items into test categories, the categorization unit 201 outputs new classification result information 251 indicating the classification results of the new specification items to the test item presentation unit 202 . The new classification result information 251 indicates, for example, as shown in FIG. 10, new specification items, test category tags, and past specification items from which words used to classify the new specification items were extracted. 9 is classified as a stress test, and the previous specification items from which the words were extracted are "A1, A2." Therefore, the new classification result information 251 indicates, for example, "B1-ST-A1, A2." Similarly, for the new specification item B2, the new classification result information 251 indicates "B2-ST-A1, A2." Furthermore, for the new specification item B3 and the new specification item B4, the new classification result information 251 indicates "B3-ST-A54, A55" and "B4-ST-A54, A55." The description method of the new classification result information 251 is not limited to that shown in FIG.

[0050] Next, in step S204, the test item presenting unit 202 selects, for each new specification item, a past specification item that has similar words. Specifically, the test item presenting unit 202 selects the past specification item from which the word indicated in the new classification result information 251 is extracted as the past specification item having similar words. For new specification item B1, past specification item A1 and past specification item A2 are selected. For new specification item B2, past specification item A1 and past specification item A2 are selected. For new specification item B3 and new specification item B4, past specification item A54 and past specification item A55 are selected.

[0051] Next, in step S205, the test item presenting unit 202 extracts, for each new specification item, test items associated in the past classification result information 153 with the past specification items selected in step S204. In the above example, past specification item A1 and past specification item A2 are selected for new specification item B1 in step S204. Then, in the past classification result information 153, as shown in Fig. 7, past test item Z1 is associated with past specification item A1. Also, past test item Z2 is associated with past specification item A2. Therefore, the test item presenting unit 202 extracts past test items Z1 and Z2 for the new specification item B1. For the new specification item B2, the past specification items A1 and A2 have also been selected in step S204. Therefore, the test item presenting unit 202 extracts the past test items Z1 and Z2 for the new specification item B2. For new specification item B3 and new specification item B4, past specification item A54 and past specification item A55 are selected in step S204. In the past classification result information 153, as shown in Fig. 7, past specification item A54 is associated with past test item Z33, and past specification item A55 is associated with past test item Z34. Therefore, the test item presenting unit 202 extracts test items Z33 and Z34 for the new specification items B3 and B4.

[0052] In addition, there are cases where the number of past specification items from which the words shown in the new classification result information 251 are extracted is equal to or exceeds a predetermined upper limit (for example, three), and the total number of test items associated with the past specification items from which the words are extracted in the past classification result information 153 is equal to or exceeds the upper limit. In such a case, in step S204, the test item presenting unit 202 compares the words contained in each of the past specification items from which the words were extracted with the words contained in the new specification item. Then, the test item presenting unit 202 selects past specification items that contain many of the same words as the words contained in the new specification item, the number of which is less than the upper limit (for example, two). Furthermore, in step S205, the test item presenting unit 202 extracts test items associated with the selected past specification items in the past classification result information 153.

[0053] After steps S203 to S205 are completed for all new specification items, the test item presenting unit 202 generates test item information 252 in step S206. In the test item information 252, the test items extracted in step S205 are shown for each new specification item.

[0054] Then, in step S207, the test item presenting unit 202 presents the test item information 252 generated in step S206 to the user of the test design support system 300. For example, the test item presenting unit 202 displays the test item information 252 on a display that is part of the input / output device 905 . In the test item information 252, for example, as shown in FIG. 11, the past test items corresponding to each new specification item are presented.

[0055] The user of the test design support system 300 uses the past test items presented in the test item information 252 as a template, and modifies the past test items to match the description of each new specification item to generate new test items. The user of the test design support system 300 modifies some of the past test items presented in the test item information 252 using the keyboard of the input / output device 905 to match the description of the new specification item. For example, for new specification item B1, the user of the test design support system 300 modifies the description of past test item Z1 to "Perform simultaneous logins on 1,200 terminals at 12:00 on weekdays." Also, the user modifies the description of past test item Z2 to "Perform simultaneous logins on 1,200 terminals at 12:00 on holidays." The user of the test design support system 300 stores the new test item data including the generated new test items in the auxiliary storage device 903 in association with the new specification data 600 .

[0056] ***Explanation of the effect of the embodiment*** According to this embodiment, appropriate test items can be obtained for each specification item from multiple viewpoints. That is, in this embodiment, each of the multiple new specification items included in the new specification data is classified into one of multiple test categories. Then, in this embodiment, past test items in the classified test category are presented to the test designer for each new specification item. Therefore, according to this embodiment, it is possible to present to the test designer, for each specification item, the type of test to be performed corresponding to the specification item, and test items that specify the test execution environment, parameters, etc.

[0057] Embodiment 2 In this embodiment, differences from the first embodiment will be mainly described. The matters not explained below are the same as those in the first embodiment.

[0058] FIG. 12 shows an example of the functional configuration of a test design support system 300 according to this embodiment. The hardware configuration of the test design support system 300 according to this embodiment is as shown in FIG. In FIG. 12, an information update unit 105 is added compared to FIG. The components of the learning device 100 other than the information update unit 105 are the same as those shown in Fig. 1. The information update unit 105, like the similarity calculation unit 101 and the like, is realized by a program. The components of the test item presentation device 200 are the same as those shown in FIG. The new test item data 700 is test item data that the user has newly created by referring to the test item information 252 and corresponding to the new specification data 600 . 12 also shows the state after the user has created new test item data 700. For this reason, the new specification data 600 and test item information 252 shown in FIG. 1 are omitted from the illustration in FIG.

[0059] The information updating unit 105 learns the appearance trends of words in new test items, which are test items included in the new test item data 700, and updates the test item appearance trend information 150. The information updating unit 105 stores the updated test item appearance trend information 150 in, for example, the auxiliary storage device 903. The categorization unit 103 can refer to the test item appearance tendency information 150 after it has been updated by the information update unit 105 .

[0060] Furthermore, the learning device 100 can use the new specification data 600 as the past specification data 400, and the new test item data 700 as the past test item data 500. That is, the learning device 100 can use a plurality of past specification data 400 including the new specification data 600 and a plurality of past test item data 500 including the new test item data 700 to generate new specification item appearance tendency information 154 according to the procedure shown in embodiment 1. In this case, the test item appearance tendency information 150 updated by the information update unit 105 can be used. Then, the test item presentation device 200 may use the new specification item appearance tendency information 154 to generate new test item information 252 for the new new specification data in the procedure shown in embodiment 1, and present the new test item information 252 to the user.

[0061] In this embodiment, new test item data is used to update the test item appearance tendency information. Therefore, according to this embodiment, the test item appearance tendency information can be kept up to date. As a result, the classification accuracy of test categories can be improved.

[0062] Although the first and second embodiments have been described above, these two embodiments may be combined and implemented. Alternatively, one of these two embodiments may be partially implemented. Alternatively, these two embodiments may be partially combined and implemented. Furthermore, the configurations and procedures described in these two embodiments may be modified as necessary.

[0063] ***Additional hardware configuration information*** Finally, a supplementary explanation of the hardware configuration of the test design support system 300 will be given. The processor 901 shown in FIG. 2 is an integrated circuit (IC) that performs processing. The processor 901 is a CPU (Central Processing Unit), a DSP (Digital Signal Processor), or the like. The main storage device 902 shown in FIG. 2 is a RAM (Random Access Memory). The auxiliary storage device 903 shown in FIG. 2 is a ROM (Read Only Memory), a flash memory, an HDD (Hard Disk Drive), or the like. The communication device 904 shown in FIG. 2 is an electronic circuit that performs data communication processing. The communication device 904 is, for example, a communication chip or a NIC (Network Interface Card).

[0064] The auxiliary storage device 903 also stores an OS (Operating System). At least a part of the OS is executed by the processor 901 . The processor 901 executes at least a part of the OS, and executes programs that implement the functions of the components of the learning device 100 and the components of the test item presentation device 200. The processor 901 executes the OS, which performs task management, memory management, file management, communication control, and the like. In addition, at least one of information, data, signal values, and variable values ​​indicating the results of processing of the components of the learning device 100 and the components of the test item presentation device 200 is stored in at least one of the main memory device 902, the auxiliary memory device 903, and the register and cache memory in the processor 901. Furthermore, the programs that realize the functions of the components of learning device 100 and test item presentation device 200 may be stored on portable recording media such as magnetic disks, flexible disks, optical disks, compact disks, Blu-ray (registered trademark) disks, DVDs, etc. Portable recording media that store the programs that realize the functions of the components of learning device 100 and test item presentation device 200 may be distributed.

[0065] Furthermore, the "part" of at least one of the components of the learning device 100 and the test item presentation device 200 may be read as a "circuit" or a "process" or a "procedure" or a "process" or a "circuitry." The test design support system 300 may also be realized by a processing circuit, such as a logic integrated circuit (IC), a gate array (GA), an application specific integrated circuit (ASIC), or a field-programmable gate array (FPGA). In this case, the components of the learning device 100 and the components of the test item presentation device 200 are each realized as part of a processing circuit. In this specification, the term "processing circuitry" refers to a generic concept that encompasses a processor and a processing circuit. That is, a processor and a processing circuit are each specific examples of "processing circuitry." [Explanation of symbols]

[0066] 100 Learning device, 101 Similarity calculation unit, 102 Correspondence unit, 103 Category classification unit, 104 Learning unit, 105 Information update unit, 150 Test item appearance trend information, 151 Similarity information, 152 Correspondence information, 153 Past classification result information, 154 Specification item appearance trend information, 200 Test item presentation device, 201 Category classification unit, 202 Test item presentation unit, 251 New classification result information, 252 Test item information, 300 Test design support system, 400 Past specification data, 500 Past test item data, 600 New specification data, 700 New test item data, 901 Processor, 902 Main memory device, 903 Auxiliary memory device, 904 Communication device, 905 Input / output device.

Claims

1. a category classification unit that analyzes, for each specification item, the appearance tendency of words in specification data including a plurality of specification items each describing a specification of a development object using one or more words, and classifies each specification item into one of a plurality of test categories, each of which is a test category, based on the analysis result of the appearance tendency of words for each specification item; a test item presentation unit that refers to test item category information that associates test items describing test contents with test categories, and presents, for each specification item, the test category into which the specification item is classified and the test items that are associated by the test item category information; The category classification unit Refer to specification item appearance trend information that indicates the appearance trend of words in specification items classified into each test category, A data processing device that classifies each specification item into a test category whose appearance tendency matches the appearance tendency determined by analysis and is indicated in the specification item appearance tendency information.

2. a category classification unit that analyzes, for each specification item, the appearance tendency of words in specification data including a plurality of specification items each describing a specification of a development object using one or more words, and classifies each specification item into one of a plurality of test categories, each of which is a test category, based on the analysis result of the appearance tendency of words for each specification item; a test item presentation unit that refers to test item category information that associates test items describing test contents with test categories, and presents, for each specification item, the test category into which the specification item is classified and the test items that are associated by the test item category information; In the test item category information, a plurality of test items are associated with test categories, and each test item is associated with a past specification item, which is a specification item that describes a specification of a past development object using one or more words; The test item presenting unit a data processing device that, for each specification item, when the number of past specification items that are associated in the test item category information with the test category into which the specification item is classified is equal to or greater than a predetermined upper limit, and when the number of past specification items that are associated in the test item category information with the upper limit or more is equal to or greater than the upper limit, selects, from the upper limit or more past specification items, past specification items that use words similar to words used in the specification item, but in a number less than the upper limit, and presents the test items that are associated in the test item category information with the selected past specification items.

3. The specification data may contain numerical values ​​as words, The category classification unit 3. The data processing device according to claim 1, wherein the data processing device analyzes the tendency of words appearing in the specification data, including the tendency of numerical values ​​appearing, for each specification item.

4. The computer analyzes, for each specification item, the appearance tendency of words in specification data including a plurality of specification items each describing the specification of the development object using one or more words, and classifies each specification item into one of a plurality of test categories, each of which is a test category, based on the analysis result of the appearance tendency of words for each specification item; The computer refers to test item category information that associates test items that describe test content with test categories, and presents, for each specification item, test items that are associated in the test item category information with the test category into which the specification item is classified; In classifying each specification item into any one of the plurality of test categories, The computer Refer to specification item appearance trend information that indicates the appearance trend of words in specification items classified into each test category, A data processing method for classifying each specification item into a test category whose appearance tendency matches the appearance tendency determined by analysis and is indicated in the specification item appearance tendency information.

5. The computer analyzes, for each specification item, the appearance tendency of words in specification data including a plurality of specification items each describing the specification of the development object using one or more words, and classifies each specification item into one of a plurality of test categories, each of which is a test category, based on the analysis result of the appearance tendency of words for each specification item; The computer refers to test item category information that associates test items that describe test content with test categories, and presents, for each specification item, test items that are associated in the test item category information with the test category into which the specification item is classified; In the test item category information, a plurality of test items are associated with test categories, and each test item is associated with a past specification item, which is a specification item that describes a specification of a past development object using one or more words; In presenting the test items, The computer A data processing method that, for each specification item, when the number of past specification items that are associated in the test item category information with the test category into which the specification item is classified is equal to or greater than a predetermined upper limit, and the number of past specification items that are associated in the test item category information with the upper limit or more is equal to or greater than the upper limit, selects, from the upper limit or more past specification items, past specification items that use words similar to words used in the specification item, but in a number less than the upper limit, and presents the test items that are associated in the test item category information with the selected past specification items.

6. a category classification process that analyzes the appearance tendency of words in specification data including a plurality of specification items that describe the specifications of the development object using one or more words for each specification item, and classifies each specification item into one of a plurality of test categories, each of which is a test category, based on the analysis result of the appearance tendency of words for each specification item; A data processing program that causes a computer to execute a test item presentation process that refers to test item category information that associates test items describing test content with test categories, and presents, for each specification item, the test category into which the specification item is classified and the test items that are associated in the test item category information, In the category classification process, The computer, Refer to specification item appearance trend information that indicates the appearance trend of words in specification items classified into each test category, A data processing program that classifies each specification item into a test category whose appearance tendency matches the appearance tendency determined by analysis and is indicated in the specification item appearance tendency information.

7. a category classification process that analyzes the appearance tendency of words in specification data including a plurality of specification items that describe the specifications of the development object using one or more words for each specification item, and classifies each specification item into one of a plurality of test categories, each of which is a test category, based on the analysis result of the appearance tendency of words for each specification item; A data processing program that causes a computer to execute a test item presentation process that refers to test item category information that associates test items describing test content with test categories, and presents, for each specification item, the test category into which the specification item is classified and the test items that are associated in the test item category information, In the test item category information, a plurality of test items are associated with test categories, and each test item is associated with a past specification item, which is a specification item that describes a specification of a past development object using one or more words; In the test item presentation process, The computer, A data processing program that, for each specification item, when the number of past specification items that are associated in the test item category information with the test category into which the specification item is classified is equal to or greater than a predetermined upper limit, and the number of past specification items that are associated in the test item category information with the upper limit or more is equal to or greater than the upper limit, selects, from the upper limit or more past specification items, past specification items that use words similar to words used in the specification item, in a number less than the upper limit, and presents the test items that are associated in the test item category information with the selected past specification items.

Images