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1 Project Planning CIS 375 Bruce R. Maxim UM-Dearborn.

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1 1 Project Planning CIS 375 Bruce R. Maxim UM-Dearborn

2 2 Planning Objectives To provide a framework that allows a software manager to make an estimate of resources, cost, and schedule. Project outcomes should be bounded by 'best case' and 'worst case' scenarios. Estimates should be updated as the project progresses.

3 3 Software Scope data to be processed or produced control parameters function performance constraints external interfaces reliability

4 4 Scope Definition Determine the customer's overall goals for the proposed system and any expected benefits. Determine the customer's perceptions concerning the nature of a good solution to the problem. Evaluate the effectiveness of the customer meeting.

5 5 Product Feasibility Technical feasibility is not a good enough reason to build a product. The product must meet the customer's needs and not be available as an off- the-shelf purchase.

6 6 Resource Estimation Human Resources –number of people required and skills needed to complete the development project Project Methods and Tools Reusable Software Resources –off-the-shelf components –modifiable components –new components Development Environment –hardware and software required during the development process

7 7 Estimation Options Delay estimation until late in the project. Base estimates on similar projects already completed. Use simple decomposition techniques to estimate project cost and effort. Use empirical models for software cost and effort estimation. Automated tools may assist with project decomposition and estimation.

8 8 Decomposition Techniques -1 Software sizing –fuzzy logic –function point calculation –standard component –%change Process-based estimation –decomposition based on tasks required to complete the software process framework

9 9 Decomposition Techniques - 2 Problem-based estimation –using lines of code (LOC) decomposition focuses on software functions –using function point (FP) decomposition focuses on information domain characteristics

10 10 Empirical Estimation Models Experiential Models –Typically derived from regression analysis of historical software project data with estimated person-months as the dependent variable Static Estimation Model –does not include time as an independent variable –Constructive Cost Model (COCOMO) Dynamic Estimation Models –usually takes time or development phase into account –Software Equation Model

11 11 Regression

12 12 Correlation and Regression Mean =  (x i )/n Variance  x =  (x i 2 )/n - (mean) 2 Covariance  xy =  (x i y i )/n – (mean x )(mean y ) Correlation r =  xy /  x  y Regression y = mx + b m =  xy / (  x ) 2 b = mean y – m * mean x

13 13 XYX^2X*YY^2 11111 32964 4416 64362416 85644025 97816349 1181218864 14919012681 5640524364256 Totals

14 14

15 15 Make Buy Decision It may be more cost effective to acquire a piece of software rather than develop it. Decision tree analysis provides a systematic way to sort through the make-buy decision. As a rule outsourcing software development requires more skillful management than does in-house development of the same product.

16 16 Decision Process 1.Develop specifications. 2.Estimate internal cost & delivery. 3.Select 3 or 4 candidate packages. 4.Select reasonable components. 5.Build a cost-benefit comparison matrix (key function performance) or use conduct benchmark tests of candidate software 6.Evaluate each software package or component based on history with the product or vendor. 7.Contact other users.

17 17 Decision Tree


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