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# Module Order Modeling This assignment involves ordering of software modules, based on the number of faults predicted by a software quality prediction model. Use the predictions obtained with Linear...

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Module Order Modeling
This assignment involves ordering of software modules, based on the number of faults predicted by a software quality prediction model. Use the predictions obtained with Linear Regression Models we built in Homework I-B. You will use only two of the three models you obtained in Homework I-B. You must use Weka to generate your own models that are used in this assignment.
These are an example of the model outputted by Weka (i.e. do not use these ve
atim):
Linear Regression Model with M5 Method of Attribute Selection:
FAULTS = XXXXXXXXXX * NUMUORS XXXXXXXXXX * NUMUANDS XXXXXXXXXX * TOTOTORS XXXXXXXXXX * VG XXXXXXXXXX * NLOGIC XXXXXXXXXX * LOC XXXXXXXXXX * ELOC XXXXXXXXXX
Linear Regression Model with Greedy Method of Attribute Selection:
FAULTS = XXXXXXXXXX * NUMUORS XXXXXXXXXX * NUMUANDS XXXXXXXXXX * TOTOTORS XXXXXXXXXX * VG XXXXXXXXXX * NLOGIC XXXXXXXXXX * LOC XXXXXXXXXX
Obtain the predictions for both the fit data set and the test data set using the above two models. Perform Module Order Modeling for both fit and test data sets using both regression models.
Compare the performances of MOM for both linear regression models. Use Alberg Diagram and Performance Curve for each Model using fit and test data sets.
Use tables to summarize the results of MOM. Also provide analysis of your summary.
NOTE:
You should NOT start working on this assignment until you have read Reference #5 and #9.
You DO NOT need to use Weka or any other software tool to do this homework after generating the model formula. Any spreadsheet program like Microsoft Excel will suffice.

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Answered 4 days AfterMar 10, 2022

## Solution

Mohd answered on Mar 15 2022
test set
Linear Regression-Greedy            NUMUORS    NUMUANDS    TOTOTORS    TOTOPANDS    VG    NLOGIC    LOC    ELOC    class    Linear Regression-Greedy    Linear Regression-M5        C-percentile    Linear Regression-Greedy    Linear Regression-M5
NUMUORS    -0.0438            6    12    127    45    10    0    641    55    nfp    0.3133    0.5449        0.95    10.90    11.07
NUMUANDS    0.0359            5    5    41    12    1    0    407    17    nfp    0.149    0.1921        0.90    6.51    6.41
TOTOPANDS    -0.0024            23    28    95    66    4    2    241    20    nfp    0.1243    0.0264        0.85    3.55    3.64
VG    -0.0376            5    5    35    20    1    0    254    14    nfp    -0.115    -0.0815        0.80    2.87    2.76
NLOGIC    0.223            6    10    43    26    1    0    264    17    nfp    0.0223    0.0678        0.75    1.99    1.86
LOC    0.0016            3    6    25    6    1    0    279    13    nfp    0.0821    0.1421        0.70    1.78    1.75
c    -0.3963            15    21    47    32    5    1    122    12    nfp    -0.146    -0.1987        0.65    1.52    1.49
-            6    11    155    96    1    0    915    58    nfp    0.9318    1.0698        0.60    1.33    1.35
Linear Regression-M5            36    159    1480    1275    41    1    1704    203    nfp    2.0828    1.8742        0.55    1.00    1.01
NUMUORS    -0.0532            17    62    121    108    5    0    200    21    nfp    0.9577    0.9086        0.50    0.81    0.73
NUMUANDS    0.0372            25    27    109    75    4    2    285    24    nfp    0.0496    -0.0534
TOTOTORS    0.0022            40    77    488    360    30    5    498    99    nfp    0.5358    0.5388
TOTOPANDS    -0.0054            6    5    41    24    1    0    303    16    nfp    -0.09    -0.0672
VG    -0.0446            24    18    172    100    11    2    422    52    nfp    -0.3337    -0.2947
NLOGIC    0.2202            13    16    40    33    5    0    136    9    nfp    -0.4409    -0.5194
LOC    0.0013            32    68    320    253    12    7    437    60    nfp    1.8451    1.7638
ELOC    0.0061            10    11    36    24    3    0    158    13    nfp    -0.357    -0.3638
c    -0.3415            14    29    52    42    2    0    123    9    nfp    0.0524    0.0057
15    43    91    72    3    0    355    21    nfp    0.7728    0.7273
6    5    41    24    1    0    303    16    nfp    -0.09    -0.0672
28    131    440    365    30    0    447    79    nfp    1.7914    1.7641
16    21    88    50    6    0    396    29    nfp    -0.0552    -0.0638
22    37    115    95    8    0    147    21    nfp    -0.3252    -0.4331
34    25    306    183    2    0    1170    96    nfp    0.3696    0.4821
17    65    126    113    5    0    182    21    nfp    1.0246    0.9808
12    13    54    34    6    0    336    20    nfp    -0.2248    -0.2699
23    96    189    163    7    0    284    31    nfp    1.8427    1.7878
18    50    118    103    5    0    195    21    nfp    0.4871    0.4229
13    20    76    53    3    0    300    23    nfp    -0.0077    -0.0116
21    80    159    138    5    0    297    28    nfp    1.5119    1.4558
4    9    15    12    1    0    128    6    nfp    -0.11    -0.0929
6    15    111    72    13    0    248    38    nfp    -0.3854    -0.2729
4    9    27    24    1    0    174    10    nfp    -0.0652    -0.0471
31    186    860    766    25    0    1072    129    nfp    3.8601    3.7496
26    104    328    268    18    0    403    65    nfp    1.5233    1.5361
5    2    10    4    1    0    127    5    nfp    -0.3875    -0.3817
4    3    14    4    1    0    140    7    nfp    -0.287    -0.2534
19    51    237    201    4    0    344    38    nfp    0.52    0.4815
35    138    800    590    35    0    1656    149    nfp    2.9425    3.0048
21    91    186    165    5    0    337    30    nfp    1.906    1.8428
27    103    356    307    37    0    445    75    nfp    0.7028    0.5649
18    95    238    217    18    0    343    42    nfp    1.577    1.486
24    95    357    275    38    1    443    70    nfp    0.806    0.7444
30    99    634    510    10    0    866    84    nfp    1.6294    1.5783
4    11    62    45    1    0    404    24    nfp    0.3242    0.3753
8    12    155    90    7    0    556    59    nfp    0.0945    0.3048
7    5    16    8    2    0    170    7    nfp    -0.3458    -0.3614
17    65    125    112    5    0    208    21    nfp    1.0686    1.0178
20    81    221    184    15    2    427    46    nfp    1.7592    1.7074
21    59    425    356    9    0    427    80    nfp    0.2924    0.3904
8    8    33    14    1    0    216    13    nfp    -0.1851    -0.157
17    71    345    283    19    0    812    87    nfp    1.3136    1.365
10    13    44    30    2    0    194    14    nfp    -0.2044    -0.2067
13    23    150    81    1    1    675    48    nfp    0.931    1.061
33    66    221    166    14    4    531    46    nfp    1.3445    1.1752
18    30    197    158    15    1    362    46    nfp    -0.2487    -0.3005
18    17    95    57    6    1    329    27    nfp    -0.1874    -0.2205
19    40    107    75    8    0    276    27    nfp    0.1683    0.1328
1    1    4    1    1    0    23    2    nfp    -0.4074    -0.3566
25    49    146    102    13    0    529    45    nfp    0.3806    0.3041
4    2    6    2    1    0    91    3    nfp    -0.3965    -0.3855
4    2    6    2    1    0    96    3    nfp    -0.3885    -0.379
6    11    66    42    8    0    177    23    nfp    -0.3826    -0.3195
23    86    173    151    7    0    293    31    nfp    1.5269    1.4571
21    123    595    437    85    0    719    125    nfp    0.0052    -0.0277
32    145    831    733    22    1    1023    150    nfp    2.681    2.704
28    173    632    581    86    0    763    135    nfp    1.1808    0.8373
37    188    1454    1300    71    0    1189    244    fp    0.8451    0.73
34    135    1116    889    52    4    2108    246    fp    3.137    3.3289
43    293    1413    1269    56    7    1425    217    fp    6.9288    6.7465
32    111    747    544    69    0    1103    199    fp    0.0518    0.3615
37    111    446    342    32    1    831    116    fp    1.4966    1.5346
42    142    793    635    53    1    1095    169    fp    1.3201    1.3329
25    39    349    278    16    0    674    67    fp    -0.2816    -0.3828
6    6    31    12    4    0    165    13    fp    -0.3589    -0.3187
30    149    627    469    28    0    1152    115    fp    3.3036    3.4024
35    149    675    527    34    0    894    140    fp    2.307    2.4783
32    166    819    704    69    4    1302    149    fp    2.8527    2.5364
40    179    1477    1285    50    1    1775    248    fp    2.3768    2.3102
21    87    240    218    7    0    296    32    fp    1.4944    1.3963
42    276    3205    2852    95    25    3302    339    fp    8.1139    6.97
48    172    821    622    70    12    874    174    fp    3.6257    3.6687
31    89    431    339    26    6    503    86    fp    1.7926    1.7778
31    408    2553    2281    31    0    2580    315    fp    10.3811    10.379
52    313    1170    1038    47    1    1171    211    fp    6.401    6.4379
88    450    2189    1703    127    23    2927    450    fp    12.8541    13.287
29    141    363    307    19    1    727    80    fp    3.3304    3.3076
44    241    1239    1044    69    2    1448    225    fp    3.9912    3.989
36    297    1234    1106    31    0    1066    137    fp    6.5748    6.373
49    375    1645    1496    57    21    1404    242    fp    12.1158    11.9257
52    208    1134    814    58    2    1540    204    fp    3.5689    3.8289
55    363    2378    1933    109    20    2680    437    fp    10.2368    10.7218
53    430    3063    2668    108    23    2954    429    fp    12.1107    11.8712
72    737    5163    4603    169    25    4530    665    fp    18.3298    17.66
79    755    5201    4712    175    28    4620    695    fp    18.9952    18.1452
Linear Regression-M5
0.95    0.9    0.85    0.8    0.75    0.7    0.65    0.6    0.55000000000000104    0.500000000000001    11.066619999999995    6.4119399999999995    3.6420699999999981    2.76416    1.8584999999999994    1.7525200000000001    1.4908599999999999    1.3457400000000002    1.0067000000000033    0.73000000000000131
Linear Regression-Greedy
0.95    0.9    0.85    0.8    0.75    0.7    0.65    0.6    0.55000000000000104    0.500000000000001    10.899979999999998    6.5052800000000017    3.545049999999998    2.8706600000000004    1.9944000000000002    1.7849600000000003    1.52366    1.3298600000000005    1.0045300000000064    0.80600000000000416
training set
Beta Coefficient    Linear Regression-Greedy            NUMUORS    NUMUANDS    TOTOTORS    TOTOPANDS    VG    NLOGIC    LOC    ELOC    class    Linear Regression-Greedy    Linear Regression-M5        C-percentile    Linear Regression-Greedy    Linear Regression-M5
NUMUORS    -0.0438            22    85    203    174    9    0    362    40    nfp    1.5148    1.4703        0.95    11.54    12.14
NUMUANDS    0.0359            21    87    186    165    5    0    379    32    nfp    1.8296    1.7608        0.90    7.24    7.27
TOTOPANDS    -0.0024            30    107    405    306    25    0    756    99    nfp    1.6662    1.7532        0.85    4.34    4.17
VG    -0.0376            6    5    19    6    2    0    160    9    nfp    -0.3132    -0.2916        0.80    3.35    3.30
NLOGIC    0.223            21    47    168    148    7    0    352    29    nfp    0.316    0.1824        0.75    2.47    2.43
LOC    0.0016            28    38    161    114    10    3    375    40    nfp    0.3609    0.2672        0.70    2.06    2.05
c    -0.3963            27    218    1522    1328    114    0    1026    310    nfp    0.4153    0.6493        0.65    1.84    1.75
-            21    78    156    135    5    0    300    27    nfp    1.4521    1.3888        0.60    1.62    1.56
Beta Coefficient    Linear Regression-M5            6    13    55    38    1    0    291    21    nfp    0.1444    0.2005        0.55    1.38    1.37
NUMUORS    -0.0532            7    6    19    8    2    0    135    9    nfp    -0.3659    -0.3509        0.50    0.85    0.83
NUMUANDS    0.0372            22    83    168    145    6    0    317    30    nfp    1.5534    1.4898
TOTOTORS    0.0022            5    3    14    6    1    0    144    7    nfp    -0.3292    -0.3122                        Linear...
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