This assignment involves ondering of software modules, based on the nunmber of fulls predicted by a software quality prediction model. Use the predictions obuained with Linear Regression Mode we built in Small Project #1. You will use only two of the three models you nbtained in Small Project 1. The models to be used for this assignment are as follows: near Regression Model with M Method of Attribute Selection

FAULTS 0.0516 A NUMUORS 0.0341 NUMUANDS 0.0027 TOTOTORS 0.0372 VG 0.2119 NLOGIC 0.0018 LOC 05 ELOC 0.309

Linear Regression Model with Greed Method A Selecuion: FAULTS 0,0482 NUMUTORS 0,0336 NUMUANDS 0,0021 TOTOTORS 0,0337 VG 0.2088 IC 0,0019 LOC 0,3255 nudels. order Modeling for both and test data sets using buuh regression mudels Oblaita the predictions for both the dala sel atid the test data set using the abuve uwo Perform Mudule o Compare the performances of MOM for both the lincar on mod Usc Alberg Diagram and Pcformancc Curve for cach Model using fit and test data scts o Use tables tu summarize the results of MOM. Also provide analysis of your summary. NOTE You should NOT start working on this assignment uatil you bave read Reference 4 and You DO NOT nced to use Weka or any other software tool to do this homework. Any spreadsheet program like Microsoft Excel wil suffice

\fSmall Project 2: Module Order Models DUE DATE: March 29, '17 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 Small Project #1. You will use only two of the three models you obtained in Small Project #1. The models to be used for this assignment are as follows: Linear Regression Model with M5 Method of Attribute Selection: FAULTS = - 0.0516 * NUMUORS + 0.0341 * NUMUANDS - 0.0027 * TOTOTORS - 0.0372 * VG + 0.2119 * NLOGIC + 0.0018 * LOC + 0.005 * ELOC - 0.3091 Linear Regression Model with Greedy Method of Attribute Selection: FAULTS = - 0.0482 * NUMUORS + 0.0336 * NUMUANDS - 0.0021 * TOTOTORS - 0.0337 * VG + 0.2088 * NLOGIC + 0.0019 * LOC - 0.3255 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 the linear regression models. Use Alberg Diagram and Peformance 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 DO NOT need to use Weka or any other software tool to do this homework. Any spreadsheet program like Microsoft Excel will suffice. @relation FIT @attribute NUMUORS real @attribute NUMUANDS real @attribute TOTOTORS real @attribute TOTOPANDS real @attribute VG real @attribute NLOGIC real @attribute LOC real @attribute ELOC real @attribute FAULTS real @data 22,85,203,174,9,0,362,40,0 21,87,186,165,5,0,379,32,0 30,107,405,306,25,0,756,99,0 6,5,19,6,2,0,160,9,0 21,47,168,148,7,0,352,29,0 28,38,161,114,10,3,375,40,0 27,218,1522,1328,114,0,1026,310,0 21,78,156,135,5,0,300,27,0 6,13,55,38,1,0,291,21,0 7,6,19,8,2,0,135,9,0 22,83,168,145,6,0,317,30,0 5,3,14,6,1,0,144,7,0 22,37,115,95,8,0,164,21,0 9,9,32,13,3,0,201,14,0 26,26,90,64,10,0,166,24,0 24,35,120,83,6,4,151,25,0 26,82,313,275,12,0,293,44,0 14,50,130,108,8,0,291,27,0 6,5,19,6,2,0,144,9,0 4,9,34,27,1,0,237,13,0 31,172,1221,1104,35,4,1158,151,0 21,26,81,56,3,2,236,18,0 9,17,59,47,2,0,136,21,0 6,5,19,6,2,0,155,9,0 8,7,104,30,1,0,495,32,0 6,5,19,6,2,0,162,9,0 12,59,232,205,13,0,410,47,0 21,79,156,135,5,0,303,27,0 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