Fault classification inference correction device

Abstract

To provide a fault classification inference correcting device capable of contributing to accurate inference in a development phase that may cause a fault.SOLUTION: A fault classification inference correction device according to the present disclosure includes: a first evaluation unit that evaluates relevance between a fault ticket containing information related to a fault having occurred in software development, and an element parameter that is a parameter related to the fault ticket; a second evaluation unit that evaluates relevance between the fault ticket and a development phase based on a first evaluation value that is an evaluation result by the first evaluation unit and a second evaluation value indicating relevance between a development phase of the software development and the element parameter; and a correction unit that provides a correction based on a third evaluation value being the evaluation result of the second evaluation unit, to a fault classification inference unit that infers development phase that may cause the fault to the input fault ticket that is a target for classification, based on a classification information added fault information in which information on the development phase that may cause the fault is added to the fault ticket.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a fault classification inference correction device used when analyzing fault information occurring in software development. [Background technology]

[0002] In software development, in order to reduce development costs, it is necessary to reduce the number of defects that occur during development and after the product (the product equipped with the developed software) is shipped. One possible way to reduce the number of defects is to identify development phases (requirements definition, basic design, detailed design, implementation, etc.) that are likely to cause defects, and to increase the amount of resources allocated to those development phases.

[0003] When a software development management system is used in software development, when a problem occurs, the problem information about the problem and the activity record of the problem response are managed in the development management system in the form of a problem ticket. With the aim of reducing the development costs of software development, the information contained in multiple problem tickets may be aggregated in order to check past problem tickets and identify the development phase in which the problem originated.

[0004] Although a problem ticket contains information after the problem is recognized, in many cases, it does not contain information about the cause of the problem, such as how the defect was embedded, which is information before the problem was recognized. When manually identifying the development phase in which the problem originates, it is necessary to check the activity records contained in the problem ticket and various information referenced from the activity records, which is very time-consuming. Therefore, a mechanism is required to automatically infer the development phase in which the problem originates from the problem ticket.

[0005] Here, we consider building the above-mentioned system by using AI (Artificial Intelligence) technology to train a fault information database that accumulates past fault tickets, and then applying a classification algorithm based on the learning results.

[0006] Conventionally, in order to improve the accuracy of AI technology, a technology has been disclosed in which corrections are made to data at the data preparation stage before learning, based on similarity metrics that evaluate the similarity of the data, with information that makes classification clearer (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Patent No. 6568935 Summary of the Invention [Problem to be solved by the invention]

[0008] However, in the past, there was no method for calculating specific evaluation parameters required to classify fault information, such as various situations at the development site, for example, how many discussions were held by the relevant parties in what positions in the development management system, or how much the fault information was referred to when accumulating the know-how of the development site, etc. Therefore, in the past, it could not be said that the development phase in which the fault originated was accurately inferred, and there was room for improvement.

[0009] The present disclosure has been made to solve such problems, and aims to provide a fault classification inference correction device that can contribute to accurate inference of the development phase where the cause of the fault occurs. [Means for solving the problem]

[0010] In order to solve the above problems, the fault classification inference correction device disclosed herein includes a first evaluation unit that evaluates the relevance between a fault ticket including information about a fault that occurred in software development and an element parameter that is a parameter related to the fault ticket, a second evaluation unit that evaluates the relevance between the fault ticket and a development phase based on a first evaluation value that is the evaluation result of the first evaluation unit and a second evaluation value that indicates the relevance between the development phase of software development and the element parameter, and a correction unit that applies a correction based on a third evaluation value that is the evaluation result of the second evaluation unit to the fault classification inference unit that infers the development phase in which the fault is caused for an input fault ticket, which is the target of classification, based on fault information with classification information in which information about the development phase in which the fault is caused is added to the fault ticket. Effect of the Invention

[0011] The present disclosure can contribute to accurate inference of the development phase in which a fault occurs. [Brief description of the drawings]

[0012] [Figure 1] 1 is a block diagram showing a configuration of a fault classification inference correction device according to an embodiment. [Diagram 2] FIG. 11 is a diagram showing an example of a failure information DB according to the embodiment. [Diagram 3] FIG. 11 is a diagram showing an example of element parameters according to the embodiment. [Figure 4] FIG. 2 is a diagram illustrating an example of a configuration of a trouble ticket-element parameter relevance evaluation unit according to an embodiment. [Diagram 5] FIG. 13 is a diagram showing an example of a trouble ticket-element parameter relevance evaluation value according to an embodiment. [Figure 6] FIG. 13 is a diagram showing an example of a trouble ticket-element parameter relevance evaluation value according to an embodiment. [Figure 7] FIG. 13 is a diagram showing an example of a development phase-element parameter relevance evaluation value according to an embodiment. [Figure 8]FIG. 13 is a diagram showing an example of a development phase-element parameter relevance evaluation value according to an embodiment. [Figure 9] FIG. 13 is a diagram showing an example of calculation of a trouble ticket-development phase relevance evaluation value according to an embodiment. [Figure 10] FIG. 13 is a diagram showing an example of a trouble ticket-development phase relevance evaluation value according to an embodiment. [Figure 11] FIG. 11 is a diagram showing an example of a failure information DB with classification information according to the embodiment. [Figure 12] FIG. 13 is a diagram showing an example of an input failure ticket according to an embodiment. [Figure 13] FIG. 2 is a diagram illustrating an example of a configuration of a fault classification inference correction unit according to an embodiment. [Figure 14] 2 is a block diagram showing an example of a configuration of a fault classification inference unit according to an embodiment. FIG. [Figure 15] FIG. 13 is a diagram showing an example of a fault classification inference result according to an embodiment. [Figure 16] FIG. 4 is a diagram showing an example of teacher data according to the embodiment. [Figure 17] FIG. 11 is a diagram showing an example of input data according to the embodiment. [Figure 18] A figure showing an example of added teacher data according to an embodiment. [Figure 19] FIG. 13 is a diagram showing an example of input data after addition according to the embodiment. [Figure 20] FIG. 11 is a diagram showing an example of an inference result according to the embodiment. [Figure 21] A figure showing an example of calculation of a corrected inference result according to an embodiment. [Figure 22] A figure showing an example of a corrected inference result according to an embodiment. [Diagram 23] 1 is a block diagram showing an example of a hardware configuration of a fault classification inference correction device according to an embodiment. [Figure 24] 1 is a block diagram showing an example of a hardware configuration of a fault classification inference correction device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] <Embodiment> <Configuration> FIG. 1 is a block diagram showing a configuration of a fault classification inference correction device 1 according to an embodiment.

[0014] The fault classification inference correction device 1 includes a fault ticket-element parameter relevance evaluation unit 4 (first evaluation unit), a fault ticket-development phase relevance evaluation unit 7 (second evaluation unit), and a fault classification inference correction unit 11. The fault classification inference correction device 1 receives a fault ticket and an element parameter 3 stored in a fault information DB (Database: the same applies below) 2 as input, and performs correction on the fault classification inference unit 12.

[0015] FIG. 2 is a diagram showing an example of the failure information DB2.

[0016] The problem information DB2 stores information on at least one problem ticket 201. The problem ticket 201 includes problem information 202, access history 203, person in charge history 204, comment history 205, and citation history 206. That is, the problem ticket 201 includes information on a problem that occurred in software development. Note that in FIG. 2, a problem ticket Foo is illustrated as the problem ticket 201.

[0017] The trouble information 202 includes a trouble response period, a trouble content, a trouble response content, and a status. The access history 203 includes a date and time when the trouble ticket was accessed, an accessor who accessed the trouble ticket, and a location accessed by the accessor immediately after accessing the trouble ticket.

[0018] The person in charge history 204 includes the date and time when the information of the person in charge related to the trouble ticket 201 was changed, the type of the changed event, the person in charge before the change (Person in Charge (From)), and the person in charge after the change (Person in Charge (To)). In other words, the person in charge history 204 is the history of the person in charge who was in charge of the trouble ticket 201.

[0019] The comment history 205 includes the date and time when a comment was posted on the problem ticket 201, the commenter, and the comment content. The citation history 206 includes the date and time when the problem ticket 201 was cited from another article in the development management system, the citer, and the location of the article that cited the problem ticket (citation location).

[0020] FIG. 3 is a diagram showing an example of the element parameter 3. As shown in FIG.

[0021] The element parameters 3 are parameters related to the problem ticket 201 stored in the problem information DB 2. Examples of the element parameters 3 include parameters such as the person involved 301 or the article 302 related to the problem ticket 201.

[0022] FIG. 4 is a diagram showing an example of the configuration of the trouble ticket-element parameter relevance evaluation unit 4. As shown in FIG.

[0023] The trouble ticket-element parameter relevance evaluation unit 4 includes an access count extraction unit 401, a comment count extraction unit 402, a related person URL attachment count extraction unit 403, a person in charge period extraction unit 404, an article URL attachment count extraction unit 405, and a immediately subsequent accessed article extraction unit 406. The trouble ticket-element parameter relevance evaluation unit 4 may be configured to include at least one of these extraction units.

[0024] The access count extraction unit 401 counts the number of times an accesser has accessed the trouble ticket 201 from the information of the access history 203 in the trouble ticket 201. If there are multiple accessers, the number of times is counted for each accesser. The accesser corresponds to the related party, which is the element parameter 3.

[0025] The comment count extraction unit 402 counts the number of times a commenter has posted a comment on the problem ticket 201 from the information of the comment history 205 of the problem ticket 201. If there are multiple commenters, the count is calculated for each commenter. The commenter corresponds to the related party, which is the element parameter 3.

[0026] The related party URL attachment count extraction unit 403 counts the number of times that a quoter has attached a URL of a trouble ticket to an article (the number of times that a related party has cited the trouble ticket) from the information of the citation history 206 in the trouble ticket 201. If there are multiple quoters, the count is calculated for each quoter. The quoter and the article correspond to the related party and the article, which are the element parameter 3, respectively.

[0027] The person in charge period extraction unit 404 counts the period during which a person was in charge of the trouble ticket 201 from the information of the person in charge history 204 in the trouble ticket 201. When there are multiple people in charge, the period is counted for each person in charge.

[0028] The article URL attachment count extraction unit 405 counts the number of times the URL of the trouble ticket is attached to an article (the number of times the trouble ticket is quoted in an article) from the information of the citation history 206 in the trouble ticket 201. If there are multiple articles, the count is calculated for each article.

[0029] The immediately-accessed article extraction unit 406 counts the number of times an accesser accessed an article immediately after a trouble ticket from the information of the access history 203 in the trouble ticket 201. If there are multiple articles, the number of times is counted for each article.

[0030] 5 and 6 are diagrams showing an example of a trouble ticket-element parameter relevance evaluation value 5 (first evaluation value). The trouble ticket-element parameter relevance evaluation value 5 is an evaluation result of the trouble ticket-element parameter relevance evaluation unit 4, and includes a person concerned-trouble ticket relevance evaluation value 501 (FIG. 5) and an article-trouble ticket relevance evaluation value 502 (FIG. 6). In FIGS. 5 and 6, the trouble ticket 201 is trouble ticket Foo.

[0031] 5, a related person-trouble ticket relevance evaluation value 501 indicates the relevance between a related person and a trouble ticket 201. Evaluation parameters 503 include access 504, comments 505, URL pasted in other articles 506, and responsible person period 507.

[0032] Access 504 is an evaluation value calculated by the access count extraction unit 401. In Fig. 5, it is shown that Mr. A, who is a related person (accessor), has accessed the trouble ticket Foo eight times.

[0033] A comment 505 is an evaluation value calculated by the comment count extraction unit 402. In Fig. 5, it is shown that a related person (commenter), B, has posted six comments on the trouble ticket Foo.

[0034] The URL pasted in another article 506 is an evaluation value calculated by the related person URL pasting count extraction unit 403. In Fig. 5, it is shown that Mr. C, a related person (quoter), pasted the URL of the trouble ticket Foo once in another article.

[0035] The person in charge period 507 is an evaluation value by the person in charge period extraction unit 404. In Fig. 5, it is shown that Mr. A, who is a related person (person in charge), was in charge of the trouble ticket 201 for one day.

[0036] 6, the article-trouble ticket relevance evaluation value 502 indicates the relevance between the article and the trouble ticket 201. The evaluation parameters 503 include affixing the URL of the trouble ticket to the article 508 and accessing the article immediately after the trouble ticket 509.

[0037] The URL of the trouble ticket is attached to the article 508 is an evaluation value calculated by the article URL attachment count extraction unit 405. In Fig. 6, it is shown that the URL of the trouble ticket Foo is attached to the article Hoge once.

[0038] The article accessed immediately after a trouble ticket 509 is an evaluation value calculated by the immediately-accessed article extraction unit 406. Fig. 6 shows that the number of times an accesser accessed article Fuga immediately after a trouble ticket Foo is 7 times.

[0039] 7 and 8 are diagrams showing an example of a development phase-element parameter relevance evaluation value 6 (second evaluation value). The development phase-element parameter relevance evaluation value 6 includes a related person-development phase relevance evaluation value 601 (FIG. 7) and an article-development phase relevance evaluation value 602 (FIG. 8). In FIGS. 7 and 8, the phase refers to the development phase.

[0040] The participant-development phase relevance evaluation value 601 is derived based on the structure of the development project, etc. In Fig. 7, the participant-development phase relevance evaluation value 601 of participant A is "0.7" for phase 0, "0.2" for phase 1, and "0.1" for phase 2.

[0041] The article-development phase relevance evaluation value 602 is derived from the posting date and time of the article, etc. In Fig. 8, the article-development phase relevance evaluation value 602 of the article Hoge is "0.6" for phase 0, "0.1" for phase 1, and "0.2" for phase 2.

[0042] FIG. 9 is a diagram showing an example of calculation of the trouble ticket-development phase relevance evaluation value 8 (third evaluation value).

[0043] As shown in FIG. 9, the trouble ticket-development phase relevance evaluation unit 7 calculates a trouble ticket-development phase relevance evaluation value 8 based on the trouble ticket-element parameter relevance evaluation value 5 and the development phase-element parameter relevance evaluation value 6.

[0044] FIG. 10 is a diagram showing an example of the trouble ticket-development phase relevance evaluation value 8. As shown in FIG.

[0045] In FIG. 10, the problem ticket-development phase relevance evaluation value 8 of the problem ticket Foo is “0.24” for phase 0, “0.77” for phase 1, and “0.58” for phase 2.

[0046] FIG. 11 is a diagram showing an example of the classification information-attached fault information DB 9. As shown in FIG.

[0047] The classification-information-attached fault information DB 9 stores the fault ticket 201 in the fault information DB 2 by adding information on the development phase 901 in which the fault originated, based on manual confirmation or the like.

[0048] FIG. 12 is a diagram showing an example of the input error ticket 10. As shown in FIG.

[0049] The input problem ticket 10 is a problem ticket that is to be classified when identifying the development phase in which the problem originates. The input problem ticket 10 corresponds to one of the problem tickets 201 stored in the problem information DB2, in which the development phase in which the problem originates is not identified.

[0050] FIG. 13 is a diagram illustrating an example of the configuration of the fault classification inference corrector 11. As shown in FIG.

[0051] The fault classification inference correction unit 11 includes a feature item adding unit 1101 and an inference result correction unit 1102. Note that the fault classification inference correction unit 11 may be configured to include only either the feature item adding unit 1101 or the inference result correction unit 1102. Details of the feature item adding unit 1101 and the inference result correction unit 1102 will be described later.

[0052] FIG. 14 is a block diagram showing an example of the configuration of the fault classification inference unit 12. As shown in FIG.

[0053] The fault classification inference unit 12 comprises a preprocessing unit 1201, a learning unit 1206, and an inference unit 1208. These will be described in detail later.

[0054] FIG. 15 is a diagram showing an example of the fault classification inference result 13. As shown in FIG.

[0055] The fault classification inference result 13 is the development phase in which the fault occurred for the input fault ticket 10. In FIG.

[0056] FIG. 16 is a diagram showing an example of the teacher data 1202.

[0057] The teacher data 1202 is composed of a set of feature items and features, and is obtained by applying a general feature extraction algorithm to the classification information-attached fault information DB 9 as preprocessing 1201. In Fig. 16, the feature item is "occurrence frequency of the word 'current'", and the feature amount is "0.6".

[0058] FIG. 17 is a diagram showing an example of the input data 1203. As shown in FIG.

[0059] The input data 1203 consists of a set of feature items and features, and is obtained by applying a general feature extraction algorithm to the input trouble ticket 10 as preprocessing 1201. In Fig. 17, the feature item is "occurrence frequency of the word 'electric current'" and the feature is "0.2".

[0060] FIG. 18 is a diagram showing an example of the added teacher data 1204. As shown in FIG.

[0061] The added teacher data 1204 is obtained by adding information on the trouble ticket-development phase relevance evaluation value 8 to the teacher data 1202 by the feature item adding unit 1101 as a pair of a feature item and a feature.

[0062] FIG. 19 is a diagram showing an example of the added input data 1205. As shown in FIG.

[0063] The added input data 1205 is obtained by adding information on the trouble ticket-development phase relevance evaluation value 8 to the input data 1203 by the feature item adding unit 1101 as a set of a feature item and a feature.

[0064] FIG. 20 is a diagram showing an example of the inference result 1209.

[0065] An inference result 1209 indicates the suitability of the development phases that cause the trouble for the input trouble ticket 10, obtained by the inference 1208. In Fig. 20, the suitability of the development phases that cause the trouble for the trouble ticket Bar is "0.67" for phase 0, "0.72" for phase 1, and "0.18" for phase 2.

[0066] FIG. 21 is a diagram showing an example of calculation of the corrected inference result 1210. As shown in FIG.

[0067] As shown in FIG. 21, the inference result correcting unit 1102 corrects the inference result 1209 (degree of suitability of the development phase in which the problem originates) based on the inference result 1209 and the problem ticket-development phase relevance evaluation value 8 .

[0068] FIG. 22 is a diagram showing an example of the corrected inference result 1210. As shown in FIG.

[0069] The corrected inference result 1210 is calculated by the inference result correction unit 1102 correcting the inference result 1209. In Fig. 22, the suitability of the development phase causing the fault for the fault ticket Bar is "0.72" for phase 0, "0.63" for phase 1, and "0.28" for phase 2.

[0070] <Algorithm of Fault Classification Inference Unit 12> The algorithm of the fault classification inference unit 12 will be described in detail with reference to FIG.

[0071] First, in pre-processing 1201 , the trouble tickets stored in the classification-information-attached trouble information DB 9 are converted into teacher data 1202 , and the input trouble ticket 10 is converted into input data 1203 .

[0072] Next, when the feature item adding unit 1101 is used, a trouble ticket-development phase relevance evaluation value of 8 is added to the teacher data 1202 to obtain post-addition teacher data 1204. Also, a trouble ticket-development phase relevance evaluation value of 8 is added to the input data 1203 to obtain post-addition input data 1205. On the other hand, when the feature item adding unit 1101 is not used, the teacher data 1202 and the input data 1203 become the post-addition teacher data 1204 and the post-addition input data 1205 as they are.

[0073] Next, learning 1206 is performed on the added teacher data 1204 to generate a classifier 120 that links a trouble ticket with a development phase in which the trouble occurs.

[0074] Next, inference 1208 is performed on the added input data 1205 by a classifier 1207, and an inference result 1209 indicating the degree of suitability of the development phase in which the fault is caused is obtained.

[0075] Next, when the inference result correction unit 1102 is used, a specific calculation formula (for example, see FIG. 21) is applied to the inference result 1209 and the trouble ticket-development phase relevance evaluation value 8 to calculate the corrected inference result 1210. On the other hand, when the inference result correction unit 1102 is not used, the inference result 1209 becomes the corrected inference result 1210 as it is.

[0076] Finally, the development phase that is most likely to match the corrected inference result 1210 (the development phase that is most likely to be the cause of the problem in the input problem ticket 10 ) is set as the problem classification inference result 13 .

[0077] <Effects> According to the present disclosure, when inference is made to find the development phase in which a fault is caused for the fault ticket 201 stored in the fault information DB2 in past development, a more accurate inference result can be obtained by correcting at least one of the pre-learning teacher data 1202 (pre-learning data) and the inference result 1209 according to various situations such as the people involved in the development site and other articles on the development system. That is, the fault classification inference correction device 1 can contribute to accurate inference of the development phase in which a fault is caused.

[0078] <Hardware configuration> The functions of the fault ticket-element parameter relevance evaluation unit 4, the fault ticket-development phase relevance evaluation unit 7, and the fault classification inference correction unit 11 in the fault classification inference correction device 1 described above are realized by a processing circuit. That is, the fault classification inference correction device 1 includes a processing circuit for evaluating the relevance between the fault ticket and the element parameter, evaluating the relevance between the fault ticket and the development phase, and inferring the development phase in which the fault is caused for the fault ticket. The processing circuit may be a dedicated hardware, or may be a processor (also called a CPU, central processing unit, processing unit, arithmetic unit, microprocessor, microcomputer, or DSP (Digital Signal Processor)) that executes a program stored in a memory.

[0079] When the processing circuit is a dedicated hardware, the processing circuit 14 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination of these, as shown in Fig. 23. Each function of the trouble ticket-element parameter relevance evaluation unit 4, the trouble ticket-development phase relevance evaluation unit 7, and the trouble classification inference correction unit 11 may be realized by the processing circuit 14, or each function may be realized collectively by one processing circuit 14.

[0080] When the processing circuit 14 is the processor 15 shown in FIG. 24, the functions of the trouble ticket-element parameter relevance evaluation unit 4, the trouble ticket-development phase relevance evaluation unit 7, and the trouble classification inference correction unit 11 are realized by software, firmware, or a combination of software and firmware. The software or firmware is written as a program and stored in the memory 16. The processor 15 realizes each function by reading and executing the program recorded in the memory 16. That is, the trouble classification inference correction device 1 includes the memory 16 for storing a program that results in the execution of a step of evaluating the relevance between the trouble ticket and the element parameter, a step of evaluating the relevance between the trouble ticket and the development phase, and a step of inferring the development phase in which the trouble is caused for the trouble ticket. In addition, it can be said that these programs cause a computer to execute the procedures or methods of the trouble ticket-element parameter relevance evaluation unit 4, the trouble ticket-development phase relevance evaluation unit 7, and the trouble classification inference correction unit 11. Here, the memory may be, for example, a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), a magnetic disk, a flexible disk, an optical disk, a compact disk, a DVD (Digital Versatile Disc), or any storage medium that will be used in the future.

[0081] In addition, with regard to each function of the trouble ticket-element parameter relevance evaluation unit 4, the trouble ticket-development phase relevance evaluation unit 7, and the trouble classification inference correction unit 11, some functions may be realized by dedicated hardware and other functions may be realized by software or firmware.

[0082] Thus, the processing circuitry may implement the functions described above through hardware, software, firmware, or a combination of these.

[0083] Furthermore, within the scope of the present disclosure, the embodiments may be modified or omitted as appropriate. [Explanation of symbols]

[0084] 1 Fault classification inference correction device, 2 Fault information DB, 3 Element parameters, 4 Fault ticket-element parameter relevance evaluation unit, 5 Fault ticket-element parameter relevance evaluation value, 6 Development phase-element parameter relevance evaluation value, 7 Fault ticket-development phase relevance evaluation unit, 8 Fault ticket-development phase relevance evaluation value, 9 Fault information DB with classification information, 10 Input fault ticket, 11 Fault classification inference correction unit, 12 Fault classification inference unit, 13 Fault classification inference result, 14 Processing circuit, 15 Processor, 16 Memory, 201 Fault ticket, 202 Fault information, 203 Access history, 204 Person in charge history, 205 Comment history, 206 Citation history, 301 Related party, 302 Article, 401 Access count extraction unit, 402 Comment count extraction unit, 403 Related party URL attachment count extraction unit, 404 Person in charge period extraction unit, 405 Article URL attachment count extraction unit, 406 Immediately accessed article extraction unit, 501 related person-trouble ticket relevance, 502 article-trouble ticket relevance, 503 evaluation parameters, 504 access, 505 comment, 506 attaching URL to other article, 507 responsible person period, 508 attaching URL of trouble ticket to article, 509 accessing article immediately after trouble ticket, 601 related person-development phase relevance, 602 article-development phase relevance, 603 development phase, 901 development phase where trouble is caused, 1101 feature item addition unit, 1102 inference result correction unit, 1201 preprocessing, 1202 training data, 1203 input data, 1204 added training data, 1205 added input data, 1206 learning, 1207 classifier, 1208 inference, 1209 inference result, 1210 corrected inference result.

Claims

1. a first evaluation unit that evaluates a relevance between a trouble ticket including information about a trouble that has occurred in software development and an element parameter that is a parameter related to the trouble ticket; a second evaluation unit that evaluates a relevance between the trouble ticket and the development phase based on a first evaluation value that is an evaluation result of the first evaluation unit and a second evaluation value that indicates a relevance between the development phase of the software development and the element parameter; a correction unit that applies a correction based on a third evaluation value, which is an evaluation result of the second evaluation unit, to a fault classification inference unit that infers a development phase in which a fault has occurred for an input fault ticket that is a target for classification, based on fault information with classification information to which information on a development phase in which a fault has occurred for the fault ticket has been added; A fault classification inference correction device comprising:

2. The element parameters include a party associated with the trouble ticket; the trouble ticket includes a history of the parties' access to the trouble ticket; The fault classification inference correction device according to claim 1 , wherein the first evaluation unit counts the number of times the concerned person has accessed the fault ticket.

3. The element parameters include a party associated with the trouble ticket; The trouble ticket includes a history of comments posted by the related party to the trouble ticket; The fault classification inference correction device according to claim 1 , wherein the first evaluation unit counts the number of times the related person posts a comment on the fault ticket.

4. The element parameters include a party associated with the trouble ticket; the trouble ticket includes a history of when the participant has cited the trouble ticket; The fault classification inference correction device according to claim 1 , wherein the first evaluation unit counts the number of times the related person has cited the fault ticket.

5. The element parameters include a party associated with the trouble ticket; The trouble ticket includes a history of the person involved being an assignee of the trouble ticket; The fault classification inference correction device according to claim 1 , wherein the first evaluation unit counts up a period during which the person involved was in charge of the fault ticket.

6. The element parameters include an article associated with the trouble ticket; The trouble ticket includes a history of when the trouble ticket was cited in the article; The fault classification inference correction device according to claim 1 , wherein the first evaluation unit counts the number of times the fault ticket is cited in the article.

7. The element parameters include an article associated with the trouble ticket; the trouble ticket includes a history of accesses to the article immediately following the trouble ticket; The fault classification inference correction device according to claim 1 , wherein the first evaluation unit counts the number of times the article is accessed immediately after the trouble ticket.

8. The correction unit corrects pre-learning data used in the fault classification inference unit, The fault classification inference correction device according to claim 1 , wherein the fault classification inference unit performs learning on the pre-learning data corrected by the correction unit.

9. The fault classification inference correction device according to claim 1 , wherein the correction unit corrects an inference result obtained by the fault classification inference unit.

Images