continuous productivity assessment and effort prediction based on bayesian analysis seok jun yun and...
TRANSCRIPT
![Page 1: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/1.jpg)
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
![Page 2: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/2.jpg)
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
![Page 3: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/3.jpg)
Pampa IIKnowledge Base
Dick B. SimmonsTexas A&M University
College Station, TX 77843-3112
![Page 4: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/4.jpg)
Organization
Project
ProjectList
Supplier SoftwareProduct
*
1
ProjectVersion*
1
1.. ** *
Plan Customer*
SLCModelList
SLCModel*
1
View [Productivity, Organization, Process, Project Dominator, Plan and WBS Gannt, Plann and WBS Activity Network,Feature Status,Project Design, Testing, Documentation]
![Page 5: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/5.jpg)
Plan
Process
Activity
* *
*
InitialMilestone FinalMilestone
Criteria
*
*
*
*
Risk
![Page 6: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/6.jpg)
Supplier
COTSRunFile
ReusableSourceFile*
*
![Page 7: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/7.jpg)
Organization
Salary
Individual
*
**
1.. * member
{subset}
1.. *
Process
Activity
*
*
InitialMilestone FinalMilestone
*
WorkBreakdownStructure
Criteria
*
*
*
*
Risk
1 manager
![Page 8: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/8.jpg)
Feature
SoftwareProduct
Version
VAndVTest UsabilityTestSubsystem
Artifact Usability
Chunk
Volume
Defect
*
*
*
*
* * *
***
*
*
Structure
Rework
Problem
Change*
*
![Page 9: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/9.jpg)
Customer
![Page 10: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/10.jpg)
Organization
Project
ProjectList
Salary
Supplier
Feature
SoftwareProduct
COTSRunFile
ReusableSourceFile
Version
VAndVTest UsabilityTestSubsystem
Artifact Usability
authorsruns
Chunk
Individual
Volume
is located in Defect
is related
to
*
1
ProjectVersion*
1
owns
*
*
*
*
*
*
1.. *
*
1.. * member 1 manager
{subset}
*
*
*
*
*
*
*
* *
******
* * *
*
1.. *
PlanCustomer
*
Structure
Process
Activity
* *
*
InitialMilestone FinalMilestone
*
WorkBreakdownStructure
Rework
Criteria
*
*
*
*
* authors
*
* * *
*
*
SLCModelList
SLCModel*
Risk
1
Problem
Change*
*
View [Productivity, Organization, Process, Project Dominator, Plan and WBS Gannt, Plann and WBS Activity Network,Feature Status,Project Design, Testing, Documentation]
![Page 11: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/11.jpg)
Productivity
![Page 12: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/12.jpg)
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
![Page 13: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/13.jpg)
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
![Page 14: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/14.jpg)
Estimate uncertainty
x
2x
4x
0.5x
0.25x
Feasibility Requirements Design CodeDelivery
![Page 15: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/15.jpg)
Estimate uncertainty
x
2x
4x
0.5x
0.25x
Feasibility Requirements Design CodeDelivery
Object PointsFunction Points
Source lines of Code
HLCs
![Page 16: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/16.jpg)
ProductivityAttributes
![Page 17: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/17.jpg)
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
![Page 18: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/18.jpg)
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
![Page 19: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/19.jpg)
Gather Attributes
from CASE Tools
![Page 20: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/20.jpg)
Organization
Project
ProjectList
Salary
Supplier
Feature
SoftwareProduct
COTSRunFile
ReusableSourceFile
Version
VAndVTest UsabilityTestSubsystem
Artifact Usability
authorsruns
Chunk
Individual
Volume
is located in Defect
is related
to
*
1
ProjectVersion*
1
owns
*
*
*
*
*
*
1.. *
*
1.. * member 1 manager
{subset}
*
*
*
*
*
*
*
* *
******
* * *
*
1.. *
PlanCustomer
*
Structure
Process
Activity
* *
*
InitialMilestone FinalMilestone
*
WorkBreakdownStructure
Rework
Criteria
*
*
*
*
* authors
*
* * *
*
*
SLCModelList
SLCModel*
Risk
1
Problem
Change*
*
CASE TOOLSJESSMetric CenterRational ClearCaseRational ClearQuestRational Test StudioCostXpertCrystal Report WriterMS SQL ServerRational RequisiteProSLIMSoDAMS ProjectRational Rose
DBMS
Attribute Gatherer
Design Tool
View [Productivity, Organization, Process, Project Dominator, Plan and WBS Gannt, Plann and WBS Activity Network,Feature Status,Project Design, Testing, Documentation]
![Page 21: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/21.jpg)
ComputeProductivity
![Page 22: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/22.jpg)
Organization
Project
ProjectList
Salary
Supplier
Feature
SoftwareProduct
COTSRunFile
ReusableSourceFile
Version
VAndVTest UsabilityTestSubsystem
Artifact Usability
authorsruns
Chunk
Individual
Volume
is located in Defect
is related
to
*
1
ProjectVersion*
1
owns
*
*
*
*
*
*
1.. *
*
1.. * member 1 manager
{subset}
*
*
*
*
*
*
*
* *
******
* * *
*
1.. *
PlanCustomer
*
Structure
Process
Activity
* *
*
InitialMilestone FinalMilestone
*
WorkBreakdownStructure
Rework
Criteria
*
*
*
*
* authors
*
* * *
*
*
SLCModelList
SLCModel*
Risk
1
Problem
Change*
*
View [Productivity, Organization, Process, Project Dominator, Plan and WBS Gannt, Plann and WBS Activity Network,Feature Status,Project Design, Testing, Documentation]
Effort
Salary
Volume
![Page 23: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/23.jpg)
Use Bayesian approach to adjust
Productivity PredictionEquation
![Page 24: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/24.jpg)
Use Expert System to Advise Manager
![Page 25: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/25.jpg)
Organization
Project
ProjectList
Salary
Supplier
Feature
SoftwareProduct
COTSRunFile
ReusableSourceFile
Version
VAndVTest UsabilityTestSubsystem
Artifact Usability
authorsruns
Chunk
Individual
Volume
is located in Defect
is related
to
*
1
ProjectVersion*
1
owns
*
*
*
*
*
*
1.. *
*
1.. * member 1 manager
{subset}
*
*
*
*
*
*
*
* *
******
* * *
*
1.. *
PlanCustomer
*
Structure
Process
Activity
* *
*
InitialMilestone FinalMilestone
*
WorkBreakdownStructure
Rework
Criteria
*
*
*
*
* authors
*
* * *
*
*
SLCModelList
SLCModel*
Risk
1
Problem
Change*
*
View [Productivity, Organization, Process, Project Dominator, Plan and WBS Gannt, Plann and WBS Activity Network,Feature Status,Project Design, Testing, Documentation]
Facts
![Page 26: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/26.jpg)
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
![Page 27: Continuous Productivity Assessment and Effort Prediction Based on Bayesian Analysis Seok Jun Yun and Dick B. Simmons Texas A&M University College Station,](https://reader035.vdocument.in/reader035/viewer/2022062517/56649e7e5503460f94b82736/html5/thumbnails/27.jpg)
Summary
• Continuous productivity measurement
• Continuous productivity model calibration
• Expert Advisor
• Optimize cost across a geographically distributed labor force