fig1 is a diagram showing a general format of a test data used in the present invention . in fig1 the test data is made up of a collection of cell data , and each cell data is formed by a delimiter 10 , a cell number 12 , a cell data length 14 , a data pattern 16 , and a pattern data 18 . the delimiter 10 has a fixed pattern which indicates a boundary of two adjacent cell data . the cell number 12 is a sequential number which indicates an order of the cell data , in an ascending or descending order . the cell data length 14 indicates a data length of the entire cell data , by a number of bits , bytes or the like . this cell data length 14 may be omitted if the cell data has a fixed length . the data pattern 16 indicates the kind of pattern of the following pattern data 18 , and may be omitted . for example , the data pattern 16 indicates that the following pattern data 18 has a pattern with all “ 0 ” s , all “ 1 ” s , or the like . the pattern data 18 forms a main body of the test data , and is made up of a repetition of a predetermined number of bits having a fixed pattern or a variable pattern . fig2 is a diagram showing an embodiment of the test data . in fig2 each row corresponds to the cell data , and the values are indicated in hexadecimal . in each cell data , the delimiter 10 has a fixed pattern “ fefe ”. the cell number 12 of the cell data is a sequential number which starts from “ 0001 ” in an ascending order . the cell data length 14 of each cell data is 16 bytes . in this embodiment , the illustration of the data pattern 16 is omitted . the pattern data 18 of each cell data has a sequential numerical value in units of 2 bytes , starting from a numerical value following the cell number 12 . fig3 is a system block diagram showing an information processing apparatus applied with an embodiment of a data inspection method according to the present invention , and forms an embodiment of a data inspection apparatus according to the present invention . in fig3 a cpu 20 is connected to a semiconductor memory 24 via an internal bus 22 , and a cache 11 is provided within the cpu 20 . a channel input / output ( i / o ) controller 26 is connected to the cpu 20 . the channel i / o controller 26 is connected to an external bus 28 , and a hard disk drive 32 is connected to the external bus 28 via an input / output ( i / o ) controller 30 . when the information processing apparatus shown in fig3 makes a write access to the hard disk drive 32 , data are read from the memory 24 by the operating system ( os ) within the cpu 20 , and stored in the cache 11 within the cpu 20 via the internal bus 22 . the data stored in the cache 11 are written into one or more disks within the hard disk drive 32 , via the channel i / o controller 26 , the external bus 28 and the i / o controller 30 . during the write access described above , the data transfer on the internal bus 22 is made in units of 32 bits , for example . in addition , the data are stored in the cache 11 in units of 512 bytes , and the data transfer between the channel i / o controller 26 and the i / o controller 30 is made in units of 256 bytes , for example . furthermore , in the hard disk drive 32 , records are formed in units of 1024 bytes , tracks are formed in units of 6 kbytes , and cylinders are formed in units of 60 kbytes . in this embodiment , the cell data length 14 of the cell data forming the test data is variably set depending on the device which is to be tested and the location of the intermediate path which is to be inspected . for example , the cell data length 14 is set to 512 bytes when inspecting the cache 11 , and the cell data length 14 is set to 256 bytes when inspecting the intermediate path between the channel i / o controller 26 and the i / o controller 30 . the cell data length 14 can freely be changed within each test data . fig4 is a flow chart for explaining a main process of this embodiment of the data inspection method . in fig4 a step s 2 generates and stores a test data , such as that shown in fig2 depending on the device which is to be tested and the location of the intermediate path which is to be inspected . a step s 4 writes the test data to the device which is to be tested , such as the hard disk drive 32 . then , a step s 6 reads the written test data from the device which is to be tested . a step s 8 carries out a compare and inspect process , and the process ends . the compare and inspect process inspects the read test data , by comparing the read test data and the test data which is stored in the step s 2 . fig5 and 6 are flow charts for explaining the compare and inspect process carried out in the step s 8 shown in fig4 . in addition , fig7 is a diagram for generally explaining the compare and inspect process . in the following description , a device which is to be tested will simply be referred to as a “ testing device ”. in fig5 a step s 10 sets a variable i to 1 , and a step s 12 decides whether or not the delimiter 10 of the ith cell data is detectable from the test data which is read from the testing device . if the decision result in the step s 12 is yes , a step s 14 decides whether or not the cell number 12 of the ith cell data has the same value as the variable i and the value of the cell number 12 is correct . if the decision result in the step s 14 is yes , a step s 16 decides whether or not the actual data length of the ith cell data matches the cell data length 14 of the ith cell data . if the decision result in the step s 16 is yes , a step s 18 decides whether or not the pattern data 18 of the ith cell data in the test data read from the testing device matches the pattern data 18 of the ith cell data in the test data which is stored in the step s 2 described above . a step s 20 recognizes that the ith cell data is normal , if the decision result in the step s 18 is yes . on the other hand , a step s 22 recognizes that the pattern data 18 of the ith cell data is abnormal , if the decision result in the step s 18 is no . if the decision result in the step s 16 is no , a step s 24 decides whether or not the pattern data 18 of the ith cell data in the test data read from the testing device matches the pattern data 18 of the ith cell data in the test data which is stored in the step s 2 described above . a step s 26 recognizes that the cell data length 14 of the ith cell data is abnormal , if the decision result in the step s 24 is yes . on the other hand , a step s 28 recognizes that the cell data length 14 and the pattern data 18 of the ith cell data are abnormal , if the decision result in the step s 24 is no . the process advances to a step s 30 if the decision result in the step s 14 is no . the step s 30 decides whether or not the cell number 12 is greater than the variable i . if the decision result in the step s 30 is yes , a step s 32 decides whether the actual data length of the ith cell data matches the cell data length 14 of the ith cell data , and at the same time , a subsequent cell data has the cell number 12 with the same value as the variable i . a step s 34 recognizes that a cell switching abnormality exists if the decision result in the step s 32 is yes . in addition , a step s 36 recognizes that a cell dropout abnormality exists if the decision result in the step s 32 is no . on the other hand , if the decision result in the step s 30 is no , a step s 38 decides whether or not the cell number 12 of the ith cell data is smaller than the variable i . if the decision result in the step s 38 is yes , a step s 40 decides whether or not the actual data length of the ith cell data matches the cell data length 14 of the ith cell data , and at the same time , a previously inspected cell data has the cell number 12 with the same value as the variable i . a step s 42 recognizes a cell overlap abnormality if the decision result in the step s 40 is yes . further , a step s 44 recognizes a cell switching abnormality if the decision result in the step s 40 is no . if the decision result in the step s 38 is no , the cell number 12 has an impossible value . hence , a step s 46 recognizes a program abnormality if the decision result in the step s 38 is no . a step s 11 increments the variable i by 1 after any of the steps s 20 , s 22 , s 26 , s 28 , s 34 , s 36 , s 42 , s 44 and s 46 , and the process thereafter advances to the step s 12 . on the other hand , if the decision result in the step s 12 is no , the process advances to a step s 54 shown in fig6 . the step s 54 decides whether or not the cell number 12 of the ith cell data is equal to the variable i and the cell number 12 is correct . if the decision result in the step s 54 is yes , a step s 56 decides whether or not the actual data length of the ith cell data matches the cell data length 14 of the ith cell data . if the decision result in the step s 56 is yes , a step s 58 decides whether or not the pattern data 18 of the ith cell data in the test data which is read from the testing device matches the pattern data 18 of the ith cell data in the test data which is stored in the step s 2 described above . a step s 60 recognizes that the delimiter 10 of the ith cell data is abnormal , if the decision result in the step s 58 is yes . on the other hand , a step s 62 recognizes that the delimiter 10 and the pattern data 18 of the ith cell data are abnormal , if the decision result in the step s 58 is no . if the decision result in the step s 56 is no , a step s 64 decides whether or not the pattern data 18 of the ith cell data in the test data which is read from the testing device matches the pattern data 18 of the ith cell data in the test data which is stored in the step s 2 described - above . a step s 66 recognizes that the delimiter 10 and the cell data length 14 of the ith cell data are abnormal , if the decision result in the step s 64 is yes . on the other hand , a step s 68 recognizes that the delimiter 10 , the cell data length 14 and the pattern data 18 of the ith cell data are abnormal , if the decision result in the step s 64 is no . in addition , the process advances to a step s 70 if the decision result in the step s 54 is no . the step s 70 decides whether or not the actual data length of the ith cell data matches the cell data length 14 of the ith cell data . if the decision result in the step s 70 is yes , a step s 72 decides whether or not the pattern data 18 of the ith cell data in the test data which is read from the testing device matches the pattern data 18 of the ith cell data in the test data which is stored in the step s 2 described above . a step s 74 recognizes that the delimiter 10 and the cell number 12 of the ith cell data are abnormal , if the decision result in the step s 72 is yes . a step s 76 recognizes that the delimiter 10 , the cell number 12 and the pattern data 18 of the ith cell data are abnormal , if the decision result in the step s 72 is no . if the decision result in the step s 70 is no , a step s 78 decides whether or not the pattern data 18 of the ith cell data in the test data which is read from the testing device matches the pattern data 18 of the ith cell data in the test data which is stored in the step s 2 described above . a step s 80 recognizes that the delimiter 10 , the cell number 12 and the cell data length 14 of the ith cell data are abnormal , if the decision result in the step s 78 is yes . the abnormalities recognized in the steps s 74 , s 76 and s 80 are caused by data transformation . on the other hand , if the decision result in the step s 78 is no , a step s 82 decides whether or not a previously inspected cell data has the cell number 12 with the same value as the variable i . a step s 84 recognizes a data dropout abnormality if the decision result in the step s 82 is yes . because the inspection is made in an order starting from the smallest cell number , it is impossible for the decision result of the step s 82 to be yes unless a data dropout occurs . further , a step s 86 recognizes an unrecognizable abnormality , if the decision result in the step s 82 is no . after the step s 84 or s 86 , a step s 88 decides whether or not a subsequent cell data has the cell number 12 with the same value as the variable i . a step s 90 recognizes a data mixing abnormality up to the next cell data position , if the decision result in the step s 88 is yes . on the other hand , if the decision result in the step s 88 is no , a step s 92 recognizes that the subsequent test data is . abnormal , and a step s 94 outputs a recognition result together with the cell number 12 . the process ends after the step s 94 . the step s 11 shown in fig5 increments the variable i by 1 after any of the steps s 60 , s 62 , s 66 , s 68 , s 74 , s 76 , s 80 and s 90 shown in fig6 and the process thereafter advances to the step s 12 . fig7 shows the decision results of the compare and inspect process shown in fig5 and 6 , with respect to the item , the delimiter 10 , the cell number 12 , the cell data length 14 , the data comparison , other conditions , and the recognition result . in fig7 “ ok ” indicates satisfactory , and “ ng ” indicates unsatisfactory or no good . when the step s 22 shown in fig5 recognizes that the pattern data 18 of the ith cell data is abnormal , a routine shown in fig8 is carried out to determine in detail the exact location in the pattern data 18 where the abnormality is generated . in fig8 a step s 100 sets the cell number 12 of the ith cell data to a variable j , and a step s 102 calculates a sequential number jmax which is included in the pattern data 18 , by dividing the number of bytes of the pattern data 18 by 2 bytes . then , a step s 104 increments the variable j by 1 . a step s 106 decides whether or not the variable j is less than or equal to the sequential number jmax . the process ends if the decision result in the step s 106 is no . on the other hand , if the decision result in the step s 106 is yes , a step s 108 decides whether or not the values of the jth and ( j + 1 ) th bytes from the start of the pattern data 18 match the value of the variable j . if the decision result in the step s 108 is yes , the process returns to the step s 104 , so as to repeat the above described process to the end of the pattern data 18 . on the other hand , a s 110 recognizes value of the variable j as being the location of the abnormality , if the decision result in the step s 108 is no . the process returns to the step s 104 after the step s 110 . accordingly , it is possible to recognize the exact location in the pattern data 18 where the abnormality is generated . in addition , it is also possible to recognize in detail the amount of data including the abnormality . the step s 2 described above forms a test data generating means . the step s 36 forms a data dropout detecting means . the step s 42 forms a data overlap detecting means . the steps s 34 and s 44 form a data switching detecting means . moreover , the steps s 100 through s 110 form an abnormality location specifying means . further , the present invention is not limited to these embodiments , but various variations and modifications may be made without departing from the scope of the invention .