1. Data Collection
Process and Integrity of Data Collection for
Later Software Cost Estimation Calibrations
Gerrit Klaschke

2. What is covered?
 Data Collection
 What is it?
 Process
 Best Practices
 Data Integrity
 Checklist
 Additional Tips

3. Data Collection Process
 A data collection process should cover several important parts:
 Ensure high quality data (see Data Collection Integrity)
 How to collect data from the sources
 How to store the data for later retrieval (analyses and calibration)
 The process itself must be refined to the point where data received
has some confidence to it - Not just taking what someone wrote on
the form at face value!
 The reason for collecting data and what data needs to be collected
is manifold.
 Goal-Question-Metric approach can help defining what metrics you need
to answer certain questions and reach goals. Goals can range from
quality improvement to schedule decrease.

4. Data Collection Process
 When to Collect Data
 When scoping a new project
 During development for management and to
identify issues and progress
 Post Mortem to improve corporate history
repository (database of completed projects)
 During maintenance to continue improving

5. Data Collection Process
 Suggested Central Repository
Requirements
 Database must be extensible so new fields can be
added easily
 Must be open, not a proprietary database
 Approach allows hosting on standalone laptop for
traveling users etc
 Additional speed over browser based versions
 Read information into excel or access via ODBC. Not
limited to provided functionality like many browser
based applications.

6. Data Collection Process
 Basic Flow
 Individuals or organizations will send their data of completed
projects to the metrics analyst or person responsible for
collection/analysis.
 All incoming data must be stored (which should includes
versioning in case updates come in from the same source) and
then reviewed for integrity and completeness. If there are
uncertainties, the metrics analyst has to clarify the points.
Having a RDBMS system makes tracking and updates very easy.
 If a normalization process is required, save both versions of the
data.
 Once a completed project passes the QA, it will be available in
the database for retrieval. This includes retrieval for the purpose
of ‘estimate by analogy’, more analysis (GQM or finding new
correlations) and calibration of estimation models.

7. Data Collection Process – Lessons
Learned
 Identify business goals. Use GQM. Setting goals enables a metric
program to enhance business results, reduce cost by keeping a
program well-defined and focused, and ensure a basis for improving
a business’ return of investment for IT.
 Clear definitions are essential but people will not always follow
them. Personally talk to them and interview to capture data. Do not
just take a form as face value. Doing this will improve the quality of
data as the interviewer can ask questions to clarify.
 People don’t read instructions. They might provide ‘just a
number’ off the top of their head. Some people might misinterpret
the data on purpose to make them look better on by mistake.
Personally talk to them.
 Sensitive data: if people/departments/companies don’t want to
share sensitive data or have concerns, try to sanitize the data.

8. Data Collection Process – Lessons
Learned
 Cost of data collection: some will claim that data collection cost
too much. Go through the list of benefits and back it up by data
showing that estimation/project success increases when using a
historical database/calibrated models.
 E.g. tell your manager for instance “software metrics will help us reduce
the number of faults reported in newly developed software by 25%
without increasing project schedules. The resulting savings in support
costs should drive a 150% ROI in the first year”.
 Cost of data collection 2: some developers will claim they are not
paid to collect data. Determine their claimed CMM/CMMI rating. If it
is 3 or higher, collecting data is required. Ask for that data in their
format and offer to fill in the forms yourself.

9. Data Collection Process – Lessons
Learned
 Use a good code counter. See the list of code
counters on the QSM.com site. The ‘understand’ code
counter is also used quite often in companies.
 Be sure to discriminate auto-generated code from
hand generated code. Auto-generated code does not
have the same correlation to effort as hand generated.
 Collect completed project actuals first: Start by
collecting data from completed projects first and THEN
collect from projects that are still underway.

10. Data Collection Process – Lessons
Learned
 Qualify the data quality: Some data collected will be
nonsensical. There are 2 approaches to handle this:
 Eliminate this data altogether. (not really recommended as data
is lost)
 Include a qualifier on the data rating it ‘a’ to ‘f’. The ISBSG
database has a rating similar to this.
 Capture both total size and amount of reuse:
Reuse is an essential part of software size. Just
collecting total size will skew the size/effort correlation.
 Don’t eliminate data points just because of the
programming language: size can be converted from one
language to another!

11. Data Collection Process – Lessons
Learned
 Have a normalization process and keep the data
both in raw and normalized forms.
 Data will be collected in varying phases, labor categories, size
definitions etc. Keep the raw data. And have a standard, well
documented normalization process that is rigorously followed to
normalize to a standard set of activities, phases etc.
 Have a structure for data storage: An excel sheet
can be used but will become unworkable as the
database grows. Get the data into an open database
asap.
 Offer them something in return: this could be a
sanitized copy of the database or at least a benchmark
showing how data fits with the rest of the database!

12. Data Integrity
 Good quality data is paramount to ensure
good calibration results.

13. Data Integrity - Checklist
 Review the goal of the data collection
 What is the data being used for? E.g. project type
calibration, later use for estimation by analogy etc.
This drives the data being collected.
 Ensure the integrity of the data collection
process
 Have the groups providing data been trained with
regard to the required data?
 Definitions
 Are different projects providing data using the same
data definitions?

14. Data Integrity - Checklist
 Approval of Inputs
 Has at least one designated individual approved the
inputs for each project?
 Missing Data
 Has any missing data been identified?
 Estimates/Actuals
 Are estimates of data items used in place of missing
actual data?
 Rationale
 Provide written rationale for any estimates used in
the calibration

15. Data Integrity - Checklist
 Sensitivity Analysis
 If estimates are used in lieu of actuals, has a
sensitivity analysis been done to evaluate the impact
on the calibration of varying assumptions with respect
to the estimates?
 Extra Data
 Has any extra data or different definitions been used?
 Changes
 Describe any changes made and the rationale for
them.

16. Data Integrity - Checklist
 Additional Data
 Has any additional data been collected that
can be used for later purposes?
 Identify the extra data and how it might be
used. Examples include effort and schedule
portions for detailed phases and activities.
 Size Conversion
 Have all size measures been converted to
eSLOC or another base unit?

17. Data Integrity - Checklist
 Counting Conventions
 What SLOC counting conventions were followed
(logical SLOC, physical SLOC etc)?
 If SLOC is not used, what definitions were followed
(such as IFPUG 4.2 standard, use cases 2.0)
 Reuse
 Are all reuse parameters provided for reused,
modified and COTS software portions?
 Has all reuse and modification been accounted for
and converted into equivalent SLOC?

18. Data Integrity - Checklist
 Reused/Modified
 Does the total equivalent size include all new software and the
equivalent sizes of reused and modified software?
 Evolution
 Has Requirements Evolution been reported?
 Input Ranges
 Make sure that there are no ranges in the volume input, as that
would indicate previously estimated values.
 Factors
 Has the environment and scaling factors been updated?
 Hours per Month
 Has the correct HPM been applied?

19. Data Integrity – Additional Tips
 Actual Phase Information: all activities may NOT be included. E.g.
system concept and integration is excluded.
 Actual Labor Information: all activities may NOT be included. E.g.
configuration and quality assurance is excluded.
 Was the schedule ‘stop and start’?
 Resources: where there hard-hitting resource constraints?
 Volatility: did requirements undergo extraordinary evolution?
 Manager’s objectives: was the project to complete in ‘minimum
time’ or ‘least cost’?
 Effort: are effort figures actually derived from cost figures?
 Always run sanity checks on data. E.g. one million lines of
code cannot be developed in 3 months.