Patent Publication Number: US-10318371-B2

Title: Apparatus and method to determine a setting item causing an incident based on action records therefor

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2016-117017, filed on Jun. 13, 2016, the entire contents of which are incorporated herein by reference. 
     FIELD 
     The embodiment discussed herein is related to apparatus and method to determine a setting item causing an incident based action records therefor. 
     BACKGROUND 
     Heretofore, in systems including multiple apparatuses, apparatuses are monitored so that a significant failure does not occur. In addition, it is known that many system failures are caused by setting errors in a system configuration. 
     For example, when a system implemented by using different middleware products provided by a wide variety of different vendors performs a task involving the different middleware products, integrity of setting values of parameters is to be maintained among the middleware products. Accordingly, the following technique has been disclosed: one sentence is read from a sentence table in which multiple sentences, each of which describes a relationship between two parameters, are recorded; a pattern table including multiple patterns obtained through classification of sentences is referred to; and the sentence that has been read is compared with each pattern that is obtained through classification and that is stored in the pattern table. In the technique, a pattern for which similarity to the sentence that has been read is highest is determined. Among the records in a summary table in which relationships between two parameters are summarized, in a record corresponding to the pattern for which the similarity is highest, the count value for the relationship between two parameters which is indicated by the pattern is incremented. Thus, an event having a high probability of resulting in occurrence of a failure is inferred. 
     Widespread use of technique such as cloud causes multiple techniques to be combined for use with one another in a layered manner. Therefore, a combination of multiple factors may cause a failure in a system such that, when such a failure occurs, it becomes difficult to determine the reason for the failure. 
     To address this problem, there has been used a method in which information about failures that have occurred in the past and information about actions taken in response to the failures are recorded and analyzed. 
     For example, the following technique has been proposed. When a range in operation history in which classification of operation history is to be performed is given, pieces of operation history which correspond to the range are obtained from an operation history database (DB) storing operation history for operation target apparatuses which has been written in the past. In this technique, feature values are extracted from each piece of operation history, and the pieces of operation history are classified, for each of units causing failures, based on similarity of the feature values. 
     Japanese Laid-open Patent Publication No. 2015-232757 and Japanese Laid-open Patent Publication No. 2015-153078 are examples of the related art. 
     SUMMARY 
     According to an aspect of the invention, an apparatus obtains, for each of setting files, from a plurality of first action records, an occurrence count value that is associated with the each first action record and indicates a number of occurrences of a setting-item name identifying each of setting items of the each setting file, in association with a sequence number assigned to the each first action record, each of the plurality of first action records recording an action that is taken in response to a first incident occurring in the apparatus. When there exists a first setting file for which an occurrence pattern generated based on a sequence of occurrence count values that are each associated with different one of the sequence numbers assigned to the plurality of first action records for the first incident is similar to a model pattern that is in advance obtained from a plurality of second action records for second incidents occurring due to a setting error of a setting value that is set to the first setting file, the apparatus stores the first setting file in association with identification information of the first incident. 
     The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention, as claimed. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating an example of a system configuration including a record analysis apparatus, according to an embodiment; 
         FIG. 2  is a diagram illustrating an example of a functional configuration of a record analysis apparatus, according to an embodiment; 
         FIG. 3  is a diagram illustrating an example of incident information, according to an embodiment; 
         FIG. 4  is a diagram illustrating an example of a setting-item list, according to an embodiment; 
         FIG. 5  is a diagram illustrating an example of an occurrence count list, according to an embodiment; 
         FIG. 6  is a diagram illustrating an example of an occurrence pattern list, according to an embodiment; 
         FIG. 7  is a diagram illustrating an example of a model pattern list, according to an embodiment; 
         FIG. 8  is a diagram illustrating an example of a causal-file list, according to an embodiment; 
         FIG. 9  is a diagram illustrating an example of a causal-item list, according to an embodiment; 
         FIG. 10  is a diagram illustrating an example of configuration information, according to an embodiment; 
         FIG. 11  is a diagram illustrating an example of a changed-item list, according to an embodiment; 
         FIG. 12  is a diagram illustrating an example of an evaluation list, according to an embodiment; 
         FIG. 13  is a diagram illustrating an example of a configuration of a computer functioning as a record analysis apparatus, according to an embodiment; 
         FIG. 14  is a diagram illustrating an example of an operational flowchart for an analysis process, according to an embodiment; 
         FIG. 15  is a diagram illustrating an example of an operational flowchart for a setting-item extraction process, according to an embodiment; 
         FIG. 16  is a diagram illustrating an example of an operational flowchart for an occurrence-pattern creation process, according to an embodiment; 
         FIG. 17  is a diagram illustrating an example of an operational flowchart for a pattern comparison process, according to an embodiment; 
         FIG. 18  is a diagram illustrating an example of an operational flowchart for a causal-item determination process, according to an embodiment; and 
         FIG. 19  is a diagram illustrating an example of an operational flowchart for an evaluation process, according to an embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENT 
     In change of a system configuration, the setting values of setting items in setting files which are set in the system are changed. At that time, setting errors of setting values cause an incident of the system. Typically, in incident information in which actions taken in response to an incident are recorded, specific setting items that have caused the incident are not recorded. Therefore, it is difficult to determine which change in settings may cause an incident occurring due to setting errors, from the recorded incident information. 
     It is desirable to associate an incident occurring due to setting errors with information about items causing the incident. 
     An exemplary embodiment according to the technique disclosed herein will be described in detail below with reference to the drawings. 
     As illustrated in  FIG. 1 , an action-record analysis apparatus  10  according to the embodiment is coupled to a processing system  14  including multiple processing apparatuses  16 , via a network  12  such as the Internet. 
       FIG. 2  is a functional block diagram of the action-record analysis apparatus  10 . The action-record analysis apparatus  10  includes an analysis unit  20  and an evaluation unit  30 . The analysis unit  20  receives incident information  41  as input, determines setting files and setting items that may cause the incident occurring due to setting errors, and stores the determined information in a causal-item list  44 . The evaluation unit  30  receives, as input, configuration information  45  before and after modification of setting items, and outputs an evaluation list  46  indicating an evaluation as to whether or not setting items for which setting values have been changed may cause incidents occurring due to setting errors. At that time, the evaluation unit  30  refers to the causal-item list  44  stored by the analysis unit  20 . The analysis unit  20  and the evaluation unit  30  will be described in detail below. 
     The analysis unit  20  will be described. The analysis unit  20  includes a setting-item extraction unit  22 , a pattern comparison unit  24 , and a causal-item determination unit  26 . The setting-item extraction unit  22  is an exemplary extraction unit provided in the technique disclosed herein. The pattern comparison unit  24  and the causal-item determination unit  26  are an exemplary analysis unit provided in the technique disclosed herein. 
     The setting-item extraction unit  22  extracts setting-item names described in a setting-item list  42 , from the incident information  41  received by the action-record analysis apparatus  10 , and counts occurrences of each of the setting-item names appearing in the incident information  41 . 
     The incident information  41  describes actions taken in response to an incident occurring in a processing apparatus  16 .  FIG. 3  illustrates exemplary incident information  41 . In the example in  FIG. 3 , the incident information  41  includes an incident identification (ID)  41 A that is identification information of an incident. The incident information  41  also includes a plurality of action records  41 B, in each of which information about an action in response to the incident is recorded for each of tasks. The plurality of action records  41 B are provided with sequence numbers  41 C that are determined chronologically according to when actions indicated by the plurality of action records  41 B are taken. Examples of a task include reception of an inquiry through mail or the like, transfer of the inquiry to a relevant department, a reply to the inquiry, and modification of settings according to the reply, each of which is handled as a single task. An action record  41 B contains information described in the mail or the like, information about a task performed by a person in charge, or the like. 
     The setting-item list  42  is a list for describing setting items included in all of the setting files that may be set in the processing apparatuses  16 .  FIG. 4  illustrates an exemplary setting-item list  42 . In the example in  FIG. 4 , each record (each row) corresponds to a single setting item. Each record includes an “id” that is identification information of the record, a “setting-item name”, and the “setting-file name” of a setting file including the setting-item indicated by the setting-item name. In the embodiment, a sequence number starting from 1 is given, as “id”, to each action record. The same is true for the other lists described below. In this example, it is assumed that configuration information including setting files describing settings for hardware and software included in the processing apparatuses  16  is data in a directory structure. A path from the root directory to a setting file is used as a setting-file name. 
     Specifically, from the incident information  41  including any of the setting-file names described in the setting-item list  42 , the setting-item extraction unit  22  extracts a setting-item name described in association with the setting-file name in the setting-item list  42 . In extraction of a setting-item name from the incident information  41 , for example, a known method of the related art, such as matching using a regular expression or the like which is performed on the incident information  41  described in a natural language, may be used. 
     The setting-item extraction unit  22  counts occurrences of the extracted setting-item name, for each sequence number  41 C of the plurality of action records  41 B, and stores the obtained count value in an occurrence count list.  FIG. 5  illustrates an exemplary occurrence count list  51 . In the example in  FIG. 5 , each record (each row) stores the occurrence counts of a single setting-item name, each of which is obtained for a corresponding one of the sequence numbers. Each record includes an “id” that is identification information of the record, a “setting-file name” and a “setting-item name”, the “incident ID” of the incident information  41  from which the setting-item name is extracted, and occurrence counts, each of which is obtained for a corresponding one of the sequence numbers. In the example in  FIG. 5 , a case in which an occurrence count for each sequence number is stored in a column corresponding to the sequence number is illustrated. For example, the occurrence counts, each of which is obtained for a corresponding one of the sequence numbers, may be stored in an array format such as {1, 0, . . . , 0, 4} in a single column. The setting-item extraction unit  22  outputs the stored occurrence count list  51  to the pattern comparison unit  24 . 
     The pattern comparison unit  24  creates an occurrence pattern for each setting file based on the occurrence count list  51  that has been output from the setting-item extraction unit  22 , and compares the created occurrence pattern with a model pattern that is created in advance from incident information for incidents occurring due to errors in setting of setting items. When the occurrence pattern is similar to the model pattern, the pattern comparison unit  24  determines the setting file corresponding to the occurrence pattern to be a setting file including setting items that may cause the incident occurring due to setting errors. 
     The reason why an occurrence pattern determined based on the occurrence count list  51 , that is, an occurrence pattern determined based on occurrence counts of setting-item names, each of which is obtained for a corresponding one of the sequence numbers, is used in determination of setting files that may cause the incident occurring due to setting errors will be described. 
     Attention is focused on a fact that, when an incident occurs due to setting errors, the occurrence pattern of occurrences of setting-item names in the action record  41 B for the incident has certain characteristics. For example, in many cases, the current setting values of the setting items in all of the setting files may be described as basic information in the first inquiry transmitted when an incident occurs. That is, regardless of whether or not setting items cause the incident, many setting-item names are highly likely to be included in an action record  41 B having a low sequence number. In an action in the final stage, in many cases, information about setting-item names for which the setting values are to be modified may be transmitted as a reply to the inquiry source. That is, setting-item names of the setting-items that may cause the incident are highly likely to be included in an action record  41 B having a high sequence number. 
     Therefore, based on characteristics of an occurrence pattern of setting-item names which is obtained for each setting file from a plurality of action records, it may be determined whether or not setting items included in the setting file may cause the incident occurring due to setting errors. The above-described characteristics of an occurrence pattern of setting-item names in the plurality of action records are merely an example. An occurrence pattern may have other characteristics in accordance with a description format of the action records, a troubleshooting manual, or the like. 
     For example, the pattern comparison unit  24  calculates an element value γ i,m  that corresponds to sequence number m within an occurrence pattern of a setting file i, based on the occurrence count list  51 . The pattern comparison unit  24  generates an occurrence pattern in which the mth element is γ i,m , for the setting file i. That is, a sequence in which values γ i,m  are arrayed in ascending order of the sequence number m is an occurrence pattern of the setting file i. 
     For example, the pattern comparison unit  24  obtains a mode value n, for the setting file i and sequence number m, of the occurrence counts of the setting-item names, and calculates, as γ i,m , a ratio of the number of setting-item names whose occurrence count is not n, to the number of all of the setting-item names. An example, in which a ratio (γ i,m ) for sequence number 10 and setting file i of “/etc/sysconfig/network-scripts/ifcfg-eth0” is calculated by using the occurrence count list  51  illustrated in  FIG. 5 , will be described. In this case, the pattern comparison unit  24  uses the occurrence counts enclosed with a dashed line in  FIG. 5 , so as to obtain the mode value n, which is equal to 2, of the occurrence counts for sequence number 10. There is only one setting-item name “GATEWAY” for which the occurrence count is not 2. The number of all of the setting-item names included in the setting file i is 5. Therefore, the pattern comparison unit  24  calculates γ i,10  as 1/5=0.2. For the other sequence numbers, γ i,m  may be similarly calculated. Accordingly, for example, an occurrence pattern of {0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2} is created. 
     As another method for calculating γ i,m , a value obtained by multiplying γ i,m  described above by the average of the occurrence counts for the sequence number m may be calculated as γ i,m . For example, in an example similar to the above-described case, the pattern comparison unit  24  uses the occurrence counts of the setting-item names enclosed with the dashed line in  FIG. 5 , so as to calculate an average of occurrence counts for sequence number 10 as (4+2+2+2+2)/5=2.4. The pattern comparison unit  24  multiplies the above-described ratio by the average to calculate γ i,10  as 0.2×2.4=0.48. 
     The value of each element in an occurrence pattern is calculated as described above, thereby generating an occurrence pattern, in which a sequence number for which, for example, all of the setting-items included in a setting file appear is regarded as a noise and is excluded, may be created. A method for generating an occurrence pattern is not limited to the above-described example. A generation method, in which an occurrence pattern of setting-item names appearing in the action record  41 B is taken into account, may be employed. 
     The pattern comparison unit  24  stores the generated occurrence pattern of each setting file in an occurrence pattern list  52 .  FIG. 6  illustrates an exemplary occurrence pattern list  52 . In the example in  FIG. 6 , each record (each row) corresponds to an occurrence pattern of a corresponding one of the setting files. Each record includes an “id” that is identification information of the record, a “setting-file name”, an “occurrence pattern” for the setting file, and the “incident ID” of the incident information  41  used in generation of the occurrence pattern. 
     A model pattern is generated by using a method similar to the above-described method, from incident information for the incidents known to occur due to setting errors.  FIG. 7  illustrates an exemplary model pattern list  43  in which a model pattern for each setting file is registered. 
     The pattern comparison unit  24  compares an occurrence pattern of each setting file which is stored in the occurrence pattern list  52 , with a model pattern that corresponds to a setting file that is registered in the model pattern list  43  and identical to the each setting file for the occurrence pattern, and calculates a similarity between them. As similarity, known similarity of the related art, such as the root mean square or the cosine similarity, may be used. For example, it is assumed that an occurrence pattern P i  of the setting file “/etc/sysconfig/network-scripts/ifcfg-eth0” is {0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2}. In addition, it is assumed that a model pattern P k  of the same setting file is {0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0.25}. When the root mean square is used as similarity, the similarity S i,k  between the occurrence pattern P i  and the model pattern P k =√((0.1−0) 2 +(0.25−0.2) 2 )=0.1118. 
     The pattern comparison unit  24  compares the similarity between an occurrence pattern and a model pattern, with a predetermined threshold. When the occurrence pattern is similar to the model pattern, the pattern comparison unit  24  determines the setting file corresponding to the occurrence pattern to be a setting file that may cause an incident occurring due to setting errors, and stores the setting-file name in a causal-file list. Hereinafter, a setting file that may cause an incident occurring due to setting errors is referred to as a “causal file”. In the above-described example, under the assumption that a threshold T S  with which the similarity S i,k  is compared is 0.2, S i,k &lt;T S  because S i,k =0.1118 and T S =0.2. Therefore, it is determined that the occurrence pattern P i  is similar to the model pattern P k . 
       FIG. 8  illustrates an exemplary causal-file list  53 . In the example in  FIG. 8 , each record (each row) corresponds to a single causal file. Each record includes an “id” that is identification information of the record, the “setting-file name” of a causal file, and the “incident ID” of the incident information  41  used in generation of the occurrence pattern of the causal file. The pattern comparison unit  24  outputs the stored causal-file list  53  to the causal-item determination unit  26 . 
     For each of the setting-file names stored in the causal-file list  53  that is output from the pattern comparison unit  24 , the causal-item determination unit  26  determines setting items that may cause the incident occurring due to setting errors, among the setting items included in the setting file indicated by the setting-file name. Hereinafter, a setting-item that may cause an incident occurring due to setting errors is referred to as a “causal item”. 
     Specifically, the causal-item determination unit  26  refers to the occurrence count list  51  that is output from the setting-item extraction unit  22 , and determines causal items for each setting file, based on occurrence counts of setting-item names appearing for a certain sequence number. The certain sequence number may be, for example, a sequence number for which the total of occurrence counts of the setting-item names is largest among the sequence numbers for which setting-item names are extracted, or the highest sequence number (the last sequence number for which the setting-item names appear) among the sequence numbers for which setting-item names are extracted. The causal-item determination unit  26  determines setting items indicated by setting-item names that characteristically appear for the certain sequence number, to be the causal items. 
     For example, the causal-item determination unit  26  obtains the mode value n of occurrence counts for the certain sequence number. When the number of setting items for which the occurrence counts are n is smaller than a predetermined threshold, the causal-item determination unit  26  determines all of the setting items of the setting file which are included in the occurrence count list  51 , to be causal items. When the number of setting items for which the occurrence counts are n is equal to or greater than the predetermined threshold, the causal-item determination unit  26  determines the setting items for which the occurrence counts are not n, to be causal items. 
     In the example in  FIG. 5 , an exemplary case in which causal items are determined from the setting items of the setting file “/etc/sysconfig/network” will be described. It is assumed that a threshold T v  which is compared with the number of setting items for which the occurrence counts are equal to the mode value n is 2, and that the certain sequence number is set at sequence number 10. In this case, the number of occurrences of the mode value n (n=1)&lt;the threshold T v  because the number of occurrences of the mode value=1 and T v =2. Therefore, the causal-item determination unit  26  determines all of the setting items of the setting file “/etc/sysconfig/network” which are included in the occurrence count list  51 , to be causal items. In this example, “HOSTNAME” is determined to be a causal item (a portion enclosed with a dot-dashed line in  FIG. 5 ). 
     Similarly, a case for the setting file “/etc/sysconfig/network-scripts/ifcfg-eth0” will be described by using the example in  FIG. 5 . In this case, the number of occurrences of the mode value n (n=2)&gt;the threshold T v  because the number of occurrences of the mode value=4 and T v =2. Therefore, the causal-item determination unit  26  determines the setting-item “GATEWAY” for which the occurrence count is not the mode value n (n=2), among the setting items of the setting file which are included in the occurrence count list  51 , to be a causal item (a portion enclosed with a dot-dot-dashed line in  FIG. 5 ). 
     The causal-item determination unit  26  stores the determined causal items in the causal-item list  44 .  FIG. 9  illustrates an exemplary causal-item list  44 . In the example in  FIG. 9 , each record (each row) corresponds to a single causal item. Each record includes an “id” that is identification information of the record, the “setting-item name” of a causal item, the “setting-file name” of the setting file including the setting-item indicated by the setting-item name, and the “incident ID” of the incident information  41  from which the causal item is extracted. 
     As another method for determining causal items, a determination method, in which setting items indicated by setting-item names included in an action record  41 B having a higher sequence number, that is, an action record  41 B that describes an action in the final stage are regarded as being more likely to be causal items, will be described. 
     Assume that the setting items of a setting file of interest are represented by P={p 1 , p 2 , . . . }, and that a score indicating probability that a setting-item p is a causal item is represented by S(p). Then, the score S(p) is expressed by using Expression (1) described below. 
     
       
         
           
             
               
                 
                   
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     In Expression (2), count(i, p) represents the number of occurrences, in the action record  41 B for the sequence number i, of the setting-item name indicating the setting-item p. 
     When the score S(p) of the setting-item p which is calculated by using Expression (1) described above is larger than a predetermined threshold, the causal-item determination unit  26  determines the setting-item p to be a causal item. For example, when the occurrence count list  51  in  FIG. 5  is used to calculate the score S(p) of the setting-item “GATEWAY” of the setting file “/etc/sysconfig/network-scripts/ifcfg-eth0”, S(p)=10/10=1. If the threshold is 0.5, since the score S(p) (=1)&gt;0.5, the setting-item “GATEWAY” is determined to be a causal item. 
     The evaluation unit  30  will be described. The evaluation unit  30  further includes a changed-item extraction unit  32  and an incident associating unit  34 . The incident associating unit  34  is an example of an output unit provided in the technique disclosed herein. 
     When a processing apparatus  16  changes the setting values of setting items of any setting file, the changed-item extraction unit  32  obtains the configuration information  45  before and after change of the setting values, and extracts the setting-file name and the setting-item names of the setting file and the setting items for which the setting values have been changed.  FIG. 10  illustrates exemplary configuration information  45 .  FIG. 10  illustrates an example of configuration information  45 A before change of setting values on the left side, and an example of configuration information  45 B after change of the setting values on the right side.  FIG. 10  schematically illustrates only parts of setting files in which the setting values have been changed, in the configuration information. 
     The changed-item extraction unit  32 , for example, obtains a difference between the configuration information  45 A before change and the configuration information  45 B after change, and extracts the setting-file names and the setting-item names of the setting files and the setting items for which setting values have been changed. Further, the changed-item extraction unit  32  extracts the setting values before and after the change. The changed-item extraction unit  32  stores, in a changed-item list  54 , the extracted setting-file names and setting-item names, and the setting values before and after change.  FIG. 11  illustrates an example of a changed-item list  54 . In the example in  FIG. 11 , each record (each row) corresponds to a single setting-item for which the setting value has been changed. Each record includes an “id” that is identification information of the record, extracted “setting-file name” and “setting-item name”, and “before change” and “after change” that are setting values before and after change. The changed-item list  54  also includes a “score” and a “relevant incident ID” that will be described in detail below. The initial value of the “score” field is 0, and the initial state of the “relevant incident ID” field is blank. 
     The incident associating unit  34  associates each record stored in the changed-item list  54 , with causal items stored in the causal-item list  44 . 
     For example, the incident associating unit  34  searches the causal-item list  44  for a record including a setting-file name and a setting-item name that match the setting-file name and the setting-item name included in each record in the changed-item list  54 . The incident associating unit  34  stores the incident ID in the retrieved record, in the “relevant incident ID” field of the record in the changed-item list  54 . When multiple records are retrieved, the incident associating unit  34  stores all of the incident IDs in the records. Further, the incident associating unit  34  adds the number of incident IDs stored in the “relevant incident ID” field, in the “score” field of the record in the changed-item list  54 . That is, the “score” of each record in the changed-item list  54  has a larger value when the number of associated incident IDs is larger. 
     When the incident associating unit  34  has performed an operation for association with causal items, on all of the records included in the changed-item list  54 , the incident associating unit  34  creates the evaluation list  46  obtained by sorting the records in the changed-item list  54  in descending order of the score, and outputs the created evaluation list  46 .  FIG. 12  illustrates an example of an evaluation list  46 . The evaluation list  46  in which changed items are described in accordance with the rankings determined by sorting the changed items based on the score is output so that it is possible to preferentially check setting items that have a high probability of causal incidents occurring due to setting errors. In particular, when there are many setting items for which the setting values have been changed, that is, for example, when there are a large number of records in the evaluation list  46 , it is possible to easily grasp and check setting items that have a high probability of causing incidents occurring due to setting errors. 
     When the changed-item list  54  includes many records having the same score, the incident associating unit  34  may set the ranking of a record, for which a troubleshooting time for the incident is longer, at a higher value in the evaluation list  46 . The reason is as follows. The longer the time for troubleshooting is, the more significant the adverse effect produced when the incident occurs is. Therefore, more attention is to be directed toward such a record in the evaluation list  46 . 
     For example, the incident information  41  includes a troubleshooting time from the beginning of the troubleshooting for the incident to the end. The incident associating unit  34  sorts records in accordance with the troubleshooting time that is included in the incident information  41  and that corresponds to the incident ID stored in the “relevant incident ID” field in the changed-item list  54 , and creates the evaluation list  46 . 
     When there are multiple records having the same score in the changed-item list  54 , the incident associating unit  34  may set the ranking of a record in such a manner that, when the record is associated with more incidents of the same type, the ranking of the record is set at a higher value in the evaluation list  46 . This is because, when the frequency of occurrences of incidents of the same type is high, more attention is to be directed toward such a record in the evaluation list  46 . 
     For example, the incident information  41  includes information about the type of the incident. The incident associating unit  34  classifies the incident IDs stored in the “relevant incident ID” in accordance with the incident types included in the pieces of incident information  41  corresponding to the incident IDs. The incident associating unit  34  sets, at a higher value, the ranking of a record that has a classification group including classified incident IDs, the number of which is larger than those in the other classification groups of the record and those in the classification groups of the other records. 
     For example, it is assumed that the types of incidents whose incident IDs are 001, 002, and 003 are the type A; the type of an incident whose incident ID is 104 is the type B; and the type of an incident whose incident ID is 205 is the type C. In the “relevant incident ID” field of a record X in the changed-item list  54 , 001, 002, and 003 are stored, and, in the “relevant incident ID” field of a record Y, 001, 104, and 205 are stored. In this case, a score corresponding to the number of relevant incident IDs is “3” for both of the record X and the record Y. In contrast, all of the relevant incident IDs in the record X are classified into the same type A, while the relevant incident IDs of the record Y are classified into the types A, B, and C. Therefore, the maximum number of classified incident IDs among the classification groups is 3 for the record X, and is 1 for the record Y. Therefore, the record X is given a higher ranking. 
     The action-record analysis apparatus  10  may be implemented, for example, by using a computer  60  illustrated in  FIG. 13 . The computer  60  includes a central processing unit (CPU)  61 , a memory  62  serving as a temporary storage area, and a nonvolatile storage device  63 . The computer  60  also includes an input/output device  64 , a read/write (R/W) unit  65  that controls reading and writing of data on a recording medium  69 , and a network I/F  66  that is coupled to the network  12  such as the Internet. The CPU  61 , the memory  62 , the storage device  63 , the input/output device  64 , the R/W unit  65 , and the network I/F  66  are coupled to one another via a bus  67 . 
     The storage device  63  may be implemented by using a hard disk drive (HDD), a solid state drive (SSD), a flash memory, or the like. The storage device  63  as a storage medium stores an action record analysis program  70  for causing the computer  60  to function as the action record analysis apparatus  10 . The action-record analysis program  70  includes a setting-item extraction process  72 , a pattern comparison process  74 , a causal-item determination process  76 , a changed-item extraction process  82 , and an incident associating process  84 . The storage device  63  includes an information storage area  90  in which information constituting various lists is stored. 
     The CPU  61  reads the action-record analysis program  70  from the storage device  63 , loads the action-record analysis program  70  on the memory  62 , and sequentially performs the processes included in the action-record analysis program  70 . The CPU  61  performs the setting-item extraction process  72  so as to act as the setting-item extraction unit  22  illustrated in  FIG. 2 . The CPU  61  performs the pattern comparison process  74  so as to act as the pattern comparison unit  24  illustrated in  FIG. 2 . The CPU  61  performs the causal-item determination process  76  so as to act as the causal-item determination unit  26  illustrated in  FIG. 2 . The CPU  61  performs the changed-item extraction process  82  so as to act as the changed-item extraction unit  32  illustrated in  FIG. 2 . The CPU  61  performs the incident associating process  84  so as to act as the incident associating unit  34  illustrated in  FIG. 2 . The CPU  61  reads information stored in the information storage area  90 , and loads the information as the various lists on the memory  62 . Thus, the computer  60  that executes the action-record analysis program  70  functions as the action-record analysis apparatus  10 . 
     The functions implemented by the action-record analysis program  70  may be implemented, for example, by using a semiconductor integrated circuit, more specifically, an application specific integrated circuit (ASIC) or the like. 
     Operations performed by the action-record analysis apparatus  10  according to the embodiment will be described. When the action-record analysis apparatus  10  receives the incident information  41  stored when an incident has occurred, the action-record analysis apparatus  10  performs an analysis process illustrated in  FIG. 14 . In a state in which the causal-item list  44  is stored in a predetermined storage area through the analysis process, when the action-record analysis apparatus  10  receives the configuration information  45  before and after change of setting values, the action-record analysis apparatus  10  performs an evaluation process illustrated in  FIG. 19 , and outputs the evaluation list  46 . The analysis process and the evaluation process performed by the action-record analysis apparatus  10  are examples of an action-record analysis method provided in the technique disclosed herein. Each of the processes will be described below. 
     The analysis process illustrated in  FIG. 14  will be described. In step S 10  in the analysis process, a setting-item extraction process of which the details are illustrated in  FIG. 15  is performed. 
     In step S 11  of the setting-item extraction process illustrated in  FIG. 15 , the setting-item extraction unit  22  performs loop process  1 . In loop process  1 , the setting-item extraction unit  22  sets, at 1, the initial value of a variable i corresponding to the id of the setting-item list  42 . While incrementing i by 1, the setting-item extraction unit  22  performs the processes in steps S 12  to S 14 . 
     In step S 12 , the setting-item extraction unit  22  determines whether or not the “setting-file name” included in a record with id i within the setting-item list  42  is present in the received incident information  41 . In the description below, an “item Y” included in a record with id i which is included in the various lists is denoted as an item Yi. When the setting-file name i of the setting-item list  42  is present in the incident information  41 , the process proceeds to step S 13 . When the setting-file name i is not present, the process proceeds to step S 15 . 
     In step S 13 , the setting-item extraction unit  22  extracts the setting-item name i from the incident information  41 , and counts occurrences of the extracted setting-item name which are obtained for each sequence number  41 C of the action record  41 B. In step S 14 , the setting-item extraction unit  22  stores the occurrence count obtained in step S 13  described above in the occurrence count list  51 , for example, as illustrated in  FIG. 5 . 
     In step S 15 , the setting-item extraction unit  22  determines whether or not the record whose id is i is the last record in the setting-item list  42 , as determination of the end of loop process  1 . When the record whose id is i is not the last record in the setting-item list  42 , loop process  1  is repeated. When the record is the last record, loop process  1  is ended, and the process proceeds to step S 16 . 
     In step S 16 , the setting-item extraction unit  22  outputs the occurrence count list  51  obtained in the storing operation in step S 14  described above, to the pattern comparison unit  24 , and the process returns back to the analysis process illustrated in  FIG. 14 . 
     Then, in step S 20  in the analysis process, an occurrence-pattern creation process of which the details are illustrated in  FIG. 16  is performed. 
     In step S 21  in the occurrence-pattern creation process illustrated in  FIG. 16 , the pattern comparison unit  24  performs loop process  2 . In loop process  2 , the pattern comparison unit  24  sets the setting-file names included in the occurrence count list  51 , to i one by one, and performs the process in step S 22  and loop process  3  in step S 23 . 
     In step S 22 , the pattern comparison unit  24  adds a record for the setting-file name i to the occurrence pattern list  52 , for example, as illustrated in  FIG. 6 . The pattern comparison unit  24  sets the record so that the record includes the setting-file name i and the incident ID corresponding to the setting file i of the occurrence count list  51 , and provides an id to the record. The pattern comparison unit  24  sets, in the “occurrence pattern” field, an empty array which may store elements, the number of which is equal to the number of sequence numbers for the action records  41 B in the incident information  41 . 
     In step S 23 , the pattern comparison unit  24  performs loop process  3 . In loop process  3 , the pattern comparison unit  24  sets, at 1, the initial value of a variable m corresponding to the sequence number  41 C of the action record  41 B. While incrementing m by 1, the pattern comparison unit  24  performs the processes in steps S 24  to S 25 . 
     In step S 24 , the pattern comparison unit  24  calculates the element value γ i,m  corresponding to the sequence number m of the occurrence pattern for the setting file i, based on the occurrence count list  51 . As described above, for example, the pattern comparison unit  24  obtains the mode value n of the occurrence counts of the setting-item names of the setting file i for the sequence number m, and calculates, as γ i,m , a ratio of the number of setting-item names for which the occurrence count is not n, to the number of all of the setting-item names. The pattern comparison unit  24  may calculate, as γ i,m , a value obtained by multiplying the average of the occurrence counts for each sequence number by the above-described ratio. 
     In step S 25 , the pattern comparison unit  24  stores γ i,m  calculated in step S 24  described above, in the mth element of the occurrence pattern of the setting file i, which is set in the “occurrence pattern” field in the record, of the occurrence pattern list  52 , including the setting-file name i. 
     In step S 26 , the pattern comparison unit  24  determines whether or not the sequence number m is the last sequence number (the highest sequence number), as determination of the end of loop process  3 . When the sequence number m is not the last sequence number, loop process  3  is repeated. When the sequence number m is the last sequence number, loop process  3  is ended. In the stage in which loop process  3  for the setting-file name i is ended, an occurrence pattern such as {0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2} has been created for the setting-file name i. 
     In step S 27 , the pattern comparison unit  24  determines whether or not all of the setting-file names included in the occurrence count list  51  have been set to i so as to be subjected to loop process  2 , as determination of the end of loop process  2 . When a setting-file name that has not been subjected to loop process  2  is present, the setting-file name is set to i, and loop process  2  is repeated. When all of the setting-file names have been subjected to loop process  2 , loop process  2  is ended, and the process proceeds to step S 28  which is the next step. 
     In step S 28 , the pattern comparison unit  24  outputs the occurrence pattern list  52  storing the occurrence pattern for each of the setting-file names included in the occurrence count list  51 , and the process returns back to the analysis process illustrated in  FIG. 14 . 
     In step S 30  in the analysis process, a pattern comparison process of which the details are illustrated in  FIG. 17  is performed. 
     In step S 31  of the pattern comparison process illustrated in  FIG. 17 , the pattern comparison unit  24  performs loop process  4 . In loop process  4 , the pattern comparison unit  24  sets 1 to the initial value of a variable i corresponding to the id of the occurrence pattern list  52 . While incrementing i by 1, the pattern comparison unit  24  performs the processes in steps S 32  to S 35 . 
     In step S 32 , the pattern comparison unit  24  determines whether or not a model pattern associated with a setting-file name matching the setting-file name i of the occurrence pattern list  52  is present in the model pattern list  43 . When a model pattern corresponding to the setting-file name i is present, the process proceeds to step S 33 . When such a model pattern is not present, the process proceeds to step S 36 . 
     In step S 33 , the pattern comparison unit  24  calculates a similarity S i,k  between the occurrence pattern P i  of the occurrence pattern list  52  and the model pattern P k , where P k  is the model pattern corresponding to the setting-file name i. The root mean square is used as similarity S i,k . 
     In step S 34 , the pattern comparison unit  24  compares the similarity S i,k  calculated in step S 33  described above with a predetermined threshold T S  so as to determine whether or not the occurrence pattern P i  is similar to the model pattern P k . In this example, the root mean square is used as similarity S i,k . Therefore, when the similarity S i,k  is less than the threshold T S , it is determined that the occurrence pattern P i  is similar to the model pattern P k . The process proceeds to step S 35  when S i,k ≥T S , and the process proceeds to step S 36  when S i,k &lt;T S . 
     In step S 35 , the pattern comparison unit  24  adds the incident ID i and the setting-file name i of the occurrence pattern list  52  to the causal-file list  53 , for example, as illustrated in  FIG. 8 , and provides an id to the added information. 
     In step S 36 , the pattern comparison unit  24  determines whether or not the record whose id is i is the last record in the occurrence pattern list  52 , as determination of the end of loop process  4 . When the record whose id is i is not the last record in the occurrence pattern list  52 , loop process  4  is repeated. When the record is the last record, loop process  4  is ended, and the process proceeds to step S 37  which is the next step. 
     In step S 37 , the pattern comparison unit  24  outputs, to the causal-item determination unit  26 , the causal-file list  53  obtained in the storing operation in step S 35  described above, and the process returns back to the analysis process illustrated in  FIG. 14 . 
     In step S 40  in the analysis process, a causal-item determination process of which the details are illustrated in  FIG. 18  is performed. 
     In step S 41  of the causal-item determination process of which the details are illustrated in  FIG. 18 , the causal-item determination unit  26  performs loop process  5 . In loop process  5 , the causal-item determination unit  26  sets 1 to the initial value of a variable i corresponding to the id of the causal-file list  53 . While incrementing i by 1, the causal-item determination unit  26  performs the processes in steps S 42  to S 47 . 
     In step S 42 , the causal-item determination unit  26  extracts records, each of which includes the same setting-file name as the setting-file name i of the causal-file list  53 , among the records in the occurrence count list  51  that is output from the setting-item extraction unit  22 . The extracted records are denoted as a list L. 
     In step S 43 , the causal-item determination unit  26  determines a certain sequence number to be used for determination of causal items. For example, a sum of occurrence counts of the setting-item names for each sequence number is calculated in the list L, and the sequence number for which the sum is the largest is determined to be the certain sequence number m max . In step S 44 , the causal-item determination unit  26  obtains the mode value n of the occurrence counts for the sequence number m max  in the list L, and determines the number of setting items for which the occurrence count is n to be n i . 
     In step S 45 , the causal-item determination unit  26  determines whether or not n i  is larger than the predetermined threshold T v . The process proceeds to step S 46  when n i &gt;T v , and the process proceeds to step S 47  when n i ≤T v . 
     In step S 46 , the causal-item determination unit  26  determines, to be causal items, setting items which are included in the list L for the sequence number m max  and for which the occurrence counts are not the mode value n. The causal-item determination unit  26  stores, in the causal-item list  44 , the incident ID i and the setting-file name i of the causal-file list  53 , and the setting-item names determined to be causal items, for example, as illustrated in  FIG. 9 , and provides an id to the stored information. 
     In contrast, in step S 47 , the causal-item determination unit  26  determines, to be causal items, all of the setting items included in the list L, and, similarly to step S 46  described above, stores the determined setting items in the causal-item list  44 . 
     In step S 48 , the causal-item determination unit  26  determines whether or not the record whose id is i is the last record in the causal-file list  53 , as determination of the end of loop process  5 . When the record whose id is i is not the last record in the causal-file list  53 , loop process  5  is repeated. When the record is the last record, loop process  5  is ended, and the process proceeds to step S 49  that is the next step. 
     In step S 49 , the causal-item determination unit  26  outputs the causal-item list  44  obtained in the storing operation in step S 46  or S 47  described above, and the process returns back to the analysis process illustrated in  FIG. 14 . Then, the analysis process ends. 
     The analysis process may be performed every time the incident information  41  is created after actions taken in response to an incident are completed. Alternatively, when actions taken in response to multiple incidents are completed, the analysis process for the incidents may be performed at a time. In either case, the above-described analysis process is performed on each piece of incident information  41 . 
     The evaluation process illustrated in  FIG. 19  will be described. In step S 51 , the changed-item extraction unit  32  obtains the configuration information  45  before and after change of setting values, extracts the setting-file names and the setting-item names of the setting files and the setting items for which setting values have been changed, and also extracts the setting values before and after setting. 
     In step S 52 , the changed-item extraction unit  32  stores records including the extracted setting-file names and setting-item names and the setting values before and after change in the changed-item list  54 , for example, as illustrated in  FIG. 11 . In the records, 0 is set in the “score” field, and a blank is set in the “relevant incident ID” field. 
     In step S 53 , the incident associating unit  34  performs loop process  6 . In loop process  6 , the incident associating unit  34  sets 1 to the initial value of a variable i corresponding to the id of the changed-item list  54 . While incrementing i by 1, the incident associating unit  34  performs the processes in steps S 54  to S 56 . 
     In step S 54 , the incident associating unit  34  determines whether or not a record including a setting-file name and a setting-item name matching the setting-file name i and the setting-item name i of the changed-item list  54  is present in the causal-item list  44 . When such a record is present, the process proceeds to step S 55 . When such a record is not present, the process proceeds to step S 57 . 
     In step S 55 , the incident associating unit  34  extracts the incident IDs of the records that are present in the causal-item list  44  and that include a setting-file name and a setting-item name matching the setting-file name i and the setting-item name i of the changed-item list  54 . The incident associating unit  34  stores all of the extracted incident IDs in the “relevant incident ID” field of the record with id i, within the changed-item list  54 . 
     In step S 56 , the incident associating unit  34  adds the number of incident IDs stored in the “relevant incident ID” field in step S 55  described above, in the “score” field of the record with id i, within the changed-item list  54 . 
     In step S 57 , it is determined whether or not the record with id i is the last record in the changed-item list  54 , as determination of the end of loop process  6 . When the record with id i is not the last record in the changed-item list  54 , loop process  6  is repeated. When the record is the last record, loop process  6  is ended, and the process proceeds to step S 58  that is the next step. 
     In step S 58 , the incident associating unit  34  creates the evaluation list  46  obtained by sorting the records in the changed-item list  54  in descending order of the score, and outputs the created evaluation list. Then, the evaluation process ends. 
     As described above, according to the embodiment, attention is focused on the fact that an occurrence pattern of setting-item names appearing in the action records for incident information has certain characteristics. Thus, an occurrence pattern based on the occurrence counts of the setting-item names which are obtained for each sequence number of an action record is created. The created occurrence pattern is compared with a model pattern created from incident information caused by setting errors so that setting-file names and setting-item names that may cause the incident occurring due to setting errors are determined. 
     In a case where setting values are changed in a processing apparatus, the determined setting-file names and setting-item names may be used to evaluate whether or not each changed item may cause an incident occurring due to setting errors. 
     In the above-described embodiment, a case, in which setting items that may cause an incident occurring due to setting errors are determined and in which evaluation is made also for each setting-item, has been described, but cases are not limited to this. Determination may be made only on the setting files corresponding to occurrence patterns similar to model patterns, that is, the causal files. In this case, when a changed item is to be evaluated, incident IDs may be associated in accordance with whether or not the setting file for which setting values have been changed matches a setting file stored in the causal-file list. 
     In the above-described embodiment, a case in which configuration information before and after change of setting values is obtained and in which changed items are extracted from the difference has been described. This is not limiting. For example, a list of setting-file names and setting-item names for which setting values have been changed may be obtained, or only configuration information after change in which flags are added to changed items may be obtained. 
     In the description above, the embodiment in which the action-record analysis program  70  which is an example of an action-record analysis program provided in the technique disclosed herein is stored (installed) in advance in the storage device  63  has been described. This is not limiting. The action-record analysis program provided in the technique disclosed herein may be provided in such a manner as to be stored in a recording medium, such as a compact disc-read-only memory (CD-ROM), a digital versatile disc-read-only memory (DVD-ROM), and a Universal Serial Bus (USB) memory. 
     All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although the embodiment of the present invention has been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.