Paper presentation by Prof. A. K. Verma Mr. Piyush Mehta Prof. A. Srividya

1. A Framework for Design Phase Prediction using Integrated Product and Process Attribute approach Indian Institute of Technology, Mumbai Paper presentation by Prof. A. K. Verma Mr. Piyush Mehta Prof. A. Srividya November 2005

2. Model Premise

3. Measures: Requirements Analysis End

4. Design Effort Prediction Models Multiple Regression Model I: Dependent Variable = Design Effort Independent Variables = Specification Size, Specification Defects, Specification Effort Factor Variable = Complexity Design Effort = f (Specification Size, Specification Complexity, Specification Defects, Specification Effort) Multiple Regression Model II: Dependent Variable = Design Effort Independent Variables = Specification Size, Specification Defects, Specification Effort) Design Effort = f (Specification Size, Specification Defects, Specification Effort) Assumption: Team competency is roughly same and is taken into account while calculating Specification Size Artificial Neural Network Model for I and II

5. Design Duration Prediction Models Multiple Regression Model for design duration prediction: Design Duration = f (Specification Size, Specification Effort, Specification Duration, Specification Team Size, Potential Design Team Size) Assumption: Design Team Size is constraint in the organization and depends on the availability of the resources in the organization Neural Network Model for design duration prediction: Design Duration = f (Specification Size, Specification Effort, Specification Duration, Specification Team Size, Design Team Size)

6. Design Size Prediction Models Multiple Regression Model for design size prediction: Design Size = f (Specification Size, Specification Complexity, Specification Effort) Assumption: The model will be fitted on the existing project database with the given specification size, specification complexity, specification effort. Specification size has strong relationship with the design size. The complexity drives the design size; highly complex requirements will correspond to higher design size. Neural Network Model for design size prediction: Design Size = f (Specification Size, Specification Complexity, Specification Effort)

7. Defect Prediction Models Multiple regression model for design defect prediction: Design Defect = f (Design Size, Design Complexity, Design Effort, Specification Defects Density, Team Competency Index, Review Team Size, Review Team Competency, Review Effort) Assumption: It can be observed to see if the team size and team competency are impacting each other. Parameter pruning can be enabled to find out the critical parameters important for predication of design defects. Design size and review effort represents the review pace, instead of review effort the review pace can be used. Artificial Neural Network model for design defect prediction: Design Defect = f (Design Size, Design Complexity, Design Effort, Specification Defects Density, Team Competency Index, Review Team Size, Review Team Competency, Review Effort)

8. Integrated Product and Process Attribute –Quantitative Model

9. Benefits of IPPA-QM Model • Early prediction leads to better project planning • Better project management and early corrective action (in case of deviation) • Better decision making capability • Understanding of organizational delivery capability • Development of project at lower cost, better quality, at an agreed time and schedule with higher customer satisfaction.