Ordering of software modules based on the number of faults predicted by a software quality prediction model.

Complete step by step solution. Microsoft Excel is the required tool. This assignment involves ordering of software modules, based on the number of faults predicted by a software quality prediction model. The models to be used for this assignment are as follows: Requirements: needed | _____________________Order any type of Service We Provide We are the best assignment service that can satisfy student’s demands in different scientific fields. We perform tasks of any kind. Our specialists provide diverse custom assignment writing services to students from all over the world every day. You can contact us for assistance with: • Essays of any type (application, scholarship, argumentative, personal, informative, persuasive, compare and contrast, narrative, analytical, cause and effect, critical, process, descriptive, expository essays); • Homework, • Assignments, • Reviews of all types (for a book, an article, or a film), • Reports, • Annotated bibliography, • Projects, • Thesis, • Term papers, • Presentations, • Lab works, • Research papers, • Speeches, • Critical thinking, • Capstone projects; • plan; • Coursework’s; • Dissertations. The list is far from complete! Representatives of our student assignment service are connoisseurs of the peculiarities of presentation in regards to academics. 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 in at least 200 words. 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 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 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 in at least 200 words.