PPT - Best Projects/Worst Projects: the Role of Size in ...
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Best Projects/Worst Projects: the Role of Size in Productivity
Best Projects/Worst Projects: the Role of Best Projects/Worst Projects: the Role of Size in ProductivitySize in Productivity
Debra L. Maschino4300 S Saginaw St.Flint, MI 48557Mail Stop: 485-302-000
September 2004
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AgendaAgenda
• Organizational and Process Background• The Best Project Process • Key Characteristics of Best Projects• Worst Project Analysis• Analysis by Size• Conclusions
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Organizational and Process BackgroundOrganizational and Process Background
• EDS• 100,000+ Employees divided into geographies
• Common software development process
• Corporate software metrics repository (web based) in place since end of 1999
• Projects estimated at more than 500 effort hours required to record metrics in repository
• Our focus for this analysis is on new development and enhancement projects. The vast majority of these are smaller than 1000 UFP.
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Best Project Process – Statement of ProblemBest Project Process – Statement of Problem
• Statement of Problem:• Projects are under pressure to shorten delivery time
and increase technical productivity
• Frequently, one of these is optimized at the expense of the other: especially when the schedule is compressed
• Some projects excel at both technical productivity and time to market. What characteristics do they have in common?
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Best Project Process StepsBest Project Process Steps
• Process Steps:• Use ISBSG data to determine medians for technical
productivity (FP/Staff Month) and schedule productivity (FP/Calendar Month)
• Calculate the technical and schedule productivity of each project for the time frame and organization(s) you want to analyze
• Look for common characteristics in the projects that are above average in both technical productivity and schedule productivity
• Conduct interviews with the project teams to uncover additional “soft” contributors to productivity
• Analyze and publish the results
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Best Projects ProcessBest Projects Process
Based on a project’s technical and schedule productivity it was placed in one of the following categories:
• High technical/High schedule productivity
• High technical/Low schedule productivity
• Low technical/High schedule productivity
• Low technical/Low schedule productivity
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Best Projects Benchmarking
EDS SC Development + Projects
020406080
100120140160180200220
0 10 20 30 40 50 60 70 80 90FP/ Person Month
FP \
Dur
atio
n
Projects
MedianFP / Duration
MedianFP / Person Month
# Projects
EDS SC Development + Projects
020406080
100120140160180200220
0 10 20 30 40 50 60 70 80 90FP/ Person Month
FP \
Dur
atio
n
Projects
MedianFP / Duration
MedianFP / Person Month
# Projects
Technical Plotted With Schedule Productivity
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Common Characteristics of Best Projects
• Short to medium project duration – between 4 & 8 months
• Medium project size – 231-732 Function Points
• Small to medium team size – Peak staff between 4 and 15
• Tracked defects
• Stable requirements
• Reuse
• Experienced team leadership - > 1 year experience
• Process Maturity (CMM level 2 or 3 processes)
• Client involvement
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Worst Project Analysis ProcessWorst Project Analysis Process
• Select all software development and enhancement projects that closed between 2001-2003.
• Determine the technical and schedule productivity for each project and assign them to a category
• Analyze and compare key environmental and project characteristics for Best and Worst Projects in order to identify key differentiators
• Compile and publish the results
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Worst Project Analysis HypothesisWorst Project Analysis Hypothesis
Hypothesis: Best projects will score significantly higher on key environmental characteristics. (a + indicates the predicted results)
+Proj. Mgt. Experience+Client Participation+Computer Experience+Language Experience+Info Tech Experience+Tool Experience+System Experience+Team complexity
Worst ProjectsBest Projects
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Environmental Characteristic ScoringEnvironmental Characteristic Scoring
Team Complexity1 - Single person2 - Single team3 - Multiple teams at single site4 - Multiple sites5 - Multiple sites – different cities6 - Multiple time zones7 - Multiple countries
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Environmental Characteristic ScoringEnvironmental Characteristic Scoring
• Environmental characteristics that evaluate the team’s level of experience use a 3 point scale
• 1 – Less than one year
• 2 – From one to three years
• 3 – More than three years
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Worst Project Analysis Hypothesis ResultsWorst Project Analysis Hypothesis Results
Hypothesis disproven. Worst projects averaged higher on all experience based characteristics and had only slightly more complex team structure
+Proj. Mgt Experience+Client Participation+Computer Experience+Language Experience+Info Tech Experience+Tool Experience+System Experience
+Team complexity
Worst ProjectsBest Projects
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Worst Project Analysis Environmental CharacteristicsWorst Project Analysis Environmental Characteristics
Environmental Characteristics
77.0456Size in UFP (Median)14.715.5Effort months (Median)8.07.0Peak Staff (Median)7.16.6Duration
Worst ProjectsBest Projects
The difference in size is noteworthy: the median for the Best Projects is 5.9 times larger than the Worst Projects
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Analysis by size – process stepsAnalysis by size – process steps
• Sort projects by size and divide into ranges
• For each size range compute the percentage of each project type
• Look for trends as project size increases
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Analysis by Size – Projects <51 UFPAnalysis by Size – Projects <51 UFP
The overwhelming majority of projects smaller than 51 UFP are “worst projects”. None is a best project.
Projects smaller than 51 FP 0%
6%
0%
94%
High Tech & SchedHigh Tech Low SchedLow Tech/High SchedLow Tech Low Sched
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Analysis by Size – Projects 51–99 UFPAnalysis by Size – Projects 51–99 UFP
As project size increases the percentage of worst projects decreases although it is still 77%
Projects 51 - 99 FP
4%
19%
0%
77%
High Tech & SchedHigh Tech Low SchedLow Tech/High SchedLow Tech Low Sched
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Analysis by Size – Projects 100–199 UFPAnalysis by Size – Projects 100–199 UFP
The percentage of worst projects continues to decrease as size grows. While still a majority, all four productivity categories are now present.
Projects 100-199 FP
9%
25%
10%
56%
High Tech & SchedHigh Tech Low SchedLow Tech/High SchedLow Tech Low Sched
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Analysis by Size – Projects 200–299 UFPAnalysis by Size – Projects 200–299 UFP
The percentage of worst projects continues to decrease as size increases although they are still the largest category
Projects 200-299 FP
27%
16%
16%
41% High Tech & SchedHigh Tech Low SchedLow Tech/High SchedLow Tech Low Sched
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Analysis by Size – Projects 300–399 UFPAnalysis by Size – Projects 300–399 UFP
Percentage wise there are twice as many Best projects than Worst projects: a dramatic reversal as project size increases
Projects 300-399 FP
42%
2%35%
21%
High Tech & SchedHigh Tech Low SchedLow Tech/High SchedLow Tech Low Sched
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Analysis by Size – Projects 400–499 UFPAnalysis by Size – Projects 400–499 UFP
There are very few worst projects in this range. Over half of the projects are Best projects
Projects 400-499 FP
52%
3%
42%
3%
High Tech & SchedHigh Tech Low SchedLow Tech/High SchedLow Tech Low Sched
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Analysis by Size – Projects > 500 UFPAnalysis by Size – Projects > 500 UFP
A clear majority of these projects are Best projects. Most of the remainder are schedule optimized. Few are Worst projects.
Projects 500 + FP
63%
0%
30%
7%
High Tech & SchedHigh Tech Low SchedLow Tech/High SchedLow Tech Low Sched
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ConclusionsConclusions
• Best projects do not enjoy any advantages over the worst projects in the project characteristics and experience categories we track
• Platform does not appear to be a crucial factor. Although a higher percentage of small (less than 51 FP) projects are mainframe, all platforms are well represented in both best and worst projects
• Small projects do not require more project management or overhead effort
• While larger size does not guarantee increased productivity, it is a characteristic of successful projects
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ConclusionsConclusions
• Project size strongly influences schedule and technical productivity
• Larger projects (over 300 function points in size) are more likely to be Best Projects
• The median size of Best Projects is 5.9 times that of the Worst Projects. The technical productivity of the Best Projects is 6.9 times that of the Worst
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Questions?Questions?