Many software projects still fail in recent years, also agile projects. Improving estimation maturity in order to start with a realistic estimate instead of an optimistic one can really save billions of dollars in most local software industries. The Chinese government may now be moving towards an active software estimation maturity improvement strategy in its new 5-year plan. Functional size measurement and relevant historical data as well as parametric estimation tools are key to such a strategy. This presentation was the key-note speech at the China System and Software Process Improvement Association conference on software estimation, Beijing China, May 27 2016.

1. IMPROVE ESTIMATION MATURITY
using Functional Size Measurement and Industry Data

2. INTRODUCING ME
Drs. Harold van Heeringen, Senior Consultant ADM Benchmarking at METRI.
• Netherlands Software Metrics Association (NESMA) – board member and chairman of the
working groups ’Benchmarking’ and ‘FPA in contract(ing)’
• International Software Benchmarking Standards Group (ISBSG) – President
• Common Software Measurement International Consortium (COSMIC) - Dutch representative
in the International Advisory Council (IAC)
• Dutch Association for Cost Engineers (DACE) – working group parametric analysis
• ICEAA trainer of CEBoK chapter 12: Software Cost Estimation
• Speaker at many conferences on software measurement, estimation and benchmarking
Slideshare: haroldveendam
twitter: @haroldveendam
LinkedIn: www.linkedin.com/in/haroldvanheeringen
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3. OVERVIEW
• Industry estimation maturity
• Effect of low maturity
• Maturity levels
• FPA
• Estimate with FPA
• Historical data
• Use in the industry
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4. SOFTWARE PROJECT RESULTS
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5. IMPACT
• Deliver too late: losing business.
• Fail/stop: loss of time, money,
business and still no solution for
the problem that needed to be
solved.
• Waste of resources that could
have been deployed successfully
otherwise.
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Failing IT projects cost the Dutch
government 7 billion USD per year
Projects > 10 million USD only 7%
succeeds.
In total, only 30% of IT projects are
successful.
These are tax dollars and one of the
reasons the whole country was in
recession for years.

6. SOFTWARE ESTIMATION MATURITY
• Software project industry: low maturity
• Low estimation maturity;
• No or little formal estimation processes ‘expert estimates’
• No or little use of historical data  ‘experience’
• Customers chose suppliers based on price, not reality
• Immature project estimation techniques results in low estimates
• Unrealistic optimism results often in complete failure!
• Lots of schedule and cost overruns
• Standish Chaos reports: Most projects fail or are at least unsuccessful
• No learning of mistaken, failing over and over again
• Low customer satisfaction rates
• In Europe: only slightly higher than the financial sector
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7. RESULTS OF LOW ESTIMATION MATURITY
• Many projects are not estimated in a professional way
• Only expert estimates, no use of estimation models / historical data
• Underestimation results in bad planning
• Development team too small
• Duration too short
• Unrealistic milestones
• Project management with no grip on the project
• Extra management attention, more meetings
• Stress in the team  bad quality  more effort
• Bad software, low maintainability
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8. ESTIMATION MATURITY MODEL*
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Developed by Dan Galorath – www.galorath.com
95% of the industry
Estimation Bias Mitigation Begins
at Level 2, Solid at Level 3
Majority of software projects are not
mitigated for bias, resulting in
optimistic estimates.

9. REALISTIC ESTIMATES
A realistic estimate is one of the most important conditions for a successful
project.
The estimate is the basis for:
• Business case;
• Planning;
• Proposal (outsourcing: fixed price / date);
• Financial result of the project… and the organization;
• Claiming and releasing of resources;
• Alignment between IT and business / customer;
• Progress reports / dashboards;
• The feeling of the team and the stakeholder.
Without a realistic estimate, the project is likely to fail!
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10. LOW/HIGH ESTIMATES
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Non-linear extra costs
-Planning errors
-team enlargement more expensive, not faster
-Extra management attention / overhead
-Stress: More defects, lower maintainability !!
Linear extra costs
Extra hours will be used

11. LEVEL 1 AND 2 ESTIMATES
• Bottom-up , assign effort hours to work items, based on knowledge and experience
• Advantages:
• Always possible to do and relatively easy;
• Experts ‘see the bears’.
• Disadvantages:
• Forgotten activities (e.g. testscript reviews. …);
• No good foundation of the estimate, very subjective;
• The expert is not going to de all the work (who will ?);
• How expert is the expert? (projects are unique);
• Experts don’t take into account duration, team size, etc.;
• Experts don’t assess the reality value, no real use of history.
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Result: expert estimates are optimistic,
on average 30% underestimation.

12. OVERVIEW
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Expert
Estimates
Level
1 and 2

13. TWO WAYS TO ESTIMATE
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Objective
Size
Effort
Cost
Estimating &
Benchmarking
Level 1 and 2
Level 3, 4 and 5

14. OVERVIEW
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Parametric
Estimates
Level 3-5

15. FUNCTIONAL SIZE
 What the software should be able to do (functionality) Functional
expressed in a number Size
based on an objectively described method Measurement
 ISO/IEC 14143
 Something intangible like functionality becomes a physical number that can be used for
calculation
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ISO/IEC 24570:2005
nesma.org

16. FUNCTION POINT ANALYSIS (FPA)
• Can be used early in the project, when functional requirements are known
• Independent of technical implementation. 500 FP Mobile app = 500 FP Legacy Cobol
system
• Just as a 20 m2 glass wall = 20 m2 brick wall
• Effort to realize the software depends on productivity
• Independent of the systems requirements
• Objective, verifiable, repeatable, defensible measurement !!
• More function points means more functionality: value!
• Functional size is the basis for objective software metrics:
• Productivity (Hours spend per FP)
• Cost Efficiency (Money spend per FP)
• Time to Market (FP per calendar month)
• Quality (Defects per 1000 FP)
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17. BASIC ESTIMATION MODEL
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measures
risk analysis
risks
consequences
Functional size
FP
hours/cost
(provisional)
hours/cost
(attuned)
influences
Productivity
h/FP

18. TIME VS COST
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Paul Masson’s
Law
Parkinson’s
Law
Brooks’
Law
Minimal time
Optimal effort
Time
Effort/Cost
Realistic
Productivity

19. TEAM SIZE: IMPACT ON PRODUCTIVITY
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Larger team size means lower
overall productivity.
Adding people to a late
project only makes the project
later.

20. OVERVIEW
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EffortorCost
Duration
Impossible
zone
Impractical
zone
Minimal duration /
highest effort and cost
Optimal duration /
lowest effort and cost
Realistic
zone

21. OVERVIEW
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Price/FP
Duration (months)
Impossible
zone
Impractical
zone
Minimal duration /
highest effort and cost
Optimal duration /
lowest effort and cost
Realistic
zone
1000
500
6 12

22. HISTORICAL DATA
• Parametric estimation models need historical data to estimate
• Preferred for estimation: data of the company itself
• For new types of projects or no data available: Industry data can be used
• Sources of industry data:
• Data delivered with the parametric models, e.g.
• QSM SLIM: trendlines based on slocs or FP
• SEER-SEM: knowledge bases
• Data provided by Benchmarking suppliers (METRI, Gartner, DCG, etc.)
• Independent (International Software Benchmarking Standards Group)
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23. INTERNATIONAL SOFTWARE BENCHMARKING STANDARDS GROUP
• Independent and not-for-profit;
• Full Members are non-profit organizations, like China SPI, NESMA, IFPUG, JFPUG, GUFPI-
ISMA, FiSMA, QESP, DASMA and Swiss-ICT.
• Grows and exploits two open repositories of software data (.xls):
• New development projects and enhancements (> 7500 projects);
• Maintenance and support (> 1200 applications).
• Everybody can submit project data
• DCQ on the site / on request (.xls)
• Anonymous
• Free benchmark report in return
• ISBSG and China SPI start working on translating ISBSG materials and increase
data collection.
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24. OVERVIEW
7500 rows in Excel, Easy to analyze
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25. EXAMPLE
• Data Quality: A or B
• Year of Project > 2012
• Project Type: Enhancement
• Primary Programming language: Java
• Count approach: Nesma or IFPUG
• Further refinement, for instance:
• Size category
• Methodology
• Industry
• Application type
• Team size
• Time pressure (duration)
• …
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PDR (hours/FP)
Number of projects 166
Minimum 4,2
Percentile 10% 5,3
Percentile 25% 6,8
Median 7,8
Percentile 75% 9,4
Percentile 90% 10,2
Maximum 15,3
Average 7,9
Example: 500 FP Java project ROM Estimate
Reality Zone:
Low (P25): 500 * 6,8 = 3400 hours
Likely(Median): 500 * 7,8 = 3900 hours
High (P75): 500 * 9,4 = 4700 hours

26. PARAMETRIC ESTIMATION IN PRACTICE
• Parametric Estimation is carried out by a number of ‘more mature’
organizations:
• Global software service providers, e.g. HP, Capgemini, Sogeti, HCL, TechM, et cetera.
They need to be able to estimate fixed price/fixed duration projects accurately.
• ‘More mature’ companies that have implemented an ‘Estimate and Performance
Measurement’ process in order to understand their (and their suppliers’) capabilities in
order to improve, e.g. many banks, governments, insurance companies, telecom
providers.
• Next to estimating, performance measurement and benchmarking of
completed projects is another main advantage of measuring functional
size.
• Analysis of the historical data sometimes shows a lot!
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27. FPA INTERNATIONAL
• Brazil: FPA used intensively in software contracts: Price per FP
• Especially early methods (Nesma)
• Government, airlines, telco’s, banks, …
• 1 company claims to measure >60.000 FP per month with > 70 CFPA
• Netherlands, Korea, Malaysia, Finland and Italy:
• FPA recommended and often mandatory in government contracts
• Supplier performance measurement in Agile and traditional projects:
• Cost/FP
• Hours/FP
• Defects/FP
• FP/month
• USA: IFPUG used in the private sector, public sector low estimation maturity
(LOC based estimation)
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28. COUNTRY REPORT
• 12 countries
- >40 projects submitted
- Australia
- Brazil
- Canada
- Denmark
- Finland
- France
- India
- Italy
- Japan
- Netherlands
- UK
- United States
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29. PRODUCTIVITY PER COUNTRY (HOURS/FP)
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0
2
4
6
8
10
12
14
16
18
PDR
Mediaan PDR

30. SPEED (FP PER CALENDAR MONTH)
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0
10
20
30
40
50
60
70
Speed
Mediaan Speed

31. MANPOWER DELIVERY RATE (FP/PERSON PER MONTH)
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0
5
10
15
20
25
Manpower DR
Mediaan Manpower DR

32. QUALITY (DEFECTS PER 1000 FP)
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0
10
20
30
40
50
60
Defectdensity
Mediaan Defect
density
In the UK software is
developed fast, but with
many defects!

33. INTERNATIONAL COOPERATION
• China SPI / IQ Alliance starts working together with international parties to
improve estimation maturity on the Chinese market:
• Partnership with ISBSG (2016):
• Translate Special Reports into Chinese
• Collect Industry data from the Chinese market
• Publish reports for Chinese market
• Partnership with Nesma (2016):
• Translate documents into Chinese
• Facilitate Training of consultants in Nesma FPA and Software Estimation practices
• Organize Nesma certification exams
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34. THANK YOU!
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@haroldveendam
haroldveendam
www.linkedin.com/in/haroldvanheeringen
ISBSG: www.isbsg.org
Nesma: www.nesma.org
METRI: www.metrigroup.com
harold.van.heeringen@metrigroup.com