Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and...

Continuous Productivity Assessment and Effort Prediction

Based on Bayesian Analysis

Seok Jun Yun and Dick B. Simmons

Texas A&M University

College Station, TX 77843-3112

Email: {sjy3806, simmons}@cs.tamu.edu

Overview

• PAMPA 2 Knowledge Base (KB)• Productivity• Productivity Attributes• Gather Attributes from CASE Tools• Compute Productivity• Use Bayesian approach to adjust

Productivity Prediction• Use Expert System to advise Manager

Pampa IIKnowledge Base

Dick B. SimmonsTexas A&M University

College Station, TX 77843-3112

Organization

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ProjectList

Supplier SoftwareProduct

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SLCModelList

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View [Productivity, Organization, Process, Project Dominator, Plan and WBS Gannt, Plann and WBS Activity Network,Feature Status,Project Design, Testing, Documentation]

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Productivity

Software Productivity Model Before 2000

Customer andCorporate Needs

Complexity of Problem

Constraints of Environment

VALUE

Quality Quantity Reusability

Defects Size

Lines ofSource

Functions ObjectPoints

Difficulty

COST

People CalendarTime

(Opportunity)

Capital

EngineeringMonths

Software Productivity Model After 2000

Customer andCorporate Needs

Complexity of Problem

Constraints of Environment

VALUE

Quality Quantity Reusability

Defects Size

Lines ofSource

Functions

Difficulty

COST

People CalendarTime

(Opportunity)

Capital

$’sHLCs (High Level Chunks)

ObjectPoints

Estimate uncertainty

x

2x

4x

0.5x

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Feasibility Requirements Design CodeDelivery

Estimate uncertainty

x

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Feasibility Requirements Design CodeDelivery

Object PointsFunction Points

Source lines of Code

HLCs

ProductivityAttributes

Productivity Prediction

where a is the units of Volume, m is the number of the Volume estimating

model, and n is the number of the effort estimating model.

Productivitym,n is expression in a per person month.

For example if a = KNCSS, then the units of productivity would be KNCSS per person month.

Productivitym,n = Volumea,m

Effortn

Productivity Prediction

where a is the units of Volume, m is the number of the Volume estimating

model, and n is the number of the effort estimating model.Salary is expressed $’s per month

$Productivitym,n is expression in a per $.

For example if a = KNCSS, then the units of productivity would be KNCSS per person month.

$Productivitym,n = Volumea,m

Effortn x Salary

Gather Attributes

from CASE Tools

Organization

Project

ProjectList

Salary

Supplier

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SoftwareProduct

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CASE TOOLSJESSMetric CenterRational ClearCaseRational ClearQuestRational Test StudioCostXpertCrystal Report WriterMS SQL ServerRational RequisiteProSLIMSoDAMS ProjectRational Rose

DBMS

Attribute Gatherer

Design Tool


ComputeProductivity

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Effort

Salary

Volume

Use Bayesian approach to adjust

Productivity PredictionEquation

Use Expert System to Advise Manager

Organization

Project

ProjectList

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Supplier

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Facts

InferenceEngine

Knowledge Elicitation

from Manager

Rules and Facts Generator

Milestone & RiskCriteria

(Rules and Initial Facts)

Facts

Action Response

Data Collection Subsystem

Plan Tracking Intelligent Agent

Summary

• Continuous productivity measurement

• Continuous productivity model calibration

• Expert Advisor

• Optimize cost across a geographically distributed labor force

Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and...

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