1. Marcelo Cataldo, Robert Bosch LLC Presented By Bhagyashree
Deokar Sources of Errors in Distributed Development Projects:
Implications for Collaborative Tools
2. Outline of Paper Introduction Research Setting Sources of
Errors in Distributed Project Measure Results Limitation
Implication For Collaborative Tools
3. Introduction Success of Product Development Project: Market
Performance Of The Product Project Cycle Time Efficiency of the
Development Process Product Quality
4. Discussion What are the barriers these people would have
faced in order to conduct this meeting?
5. Introduction Past research focuses on: Process improvement
activities Experience Dimensions of geographic dispersion Technical
dependencies Time pressure Does not consider relative significance
of each of the factors
6. Research Setting Data from a multi-national company in the
area of embedded systems Products consist of physical elements
which are managed by a large and complex software Access to
modification request repository and version control system 209
software development projects between 2003 and 2008
7. Discussion Do you agree with below statement considering
todays advance technology Distributed development organizations
face significant challenges in terms of information sharing and
integration because of the detrimental effects of distance.
Moreover, the various dimensions of geographic dispersion have a
differentiating and additive effect on the ability of the
distributed development organizations to effectively communicate,
coordinate and share information, and consequently, on the quality
of the developed products
8. Sources of Errors in Distributed Project Types of experience
Dimensions of geographic dispersion Technical properties of the
product Projects time pressure
9. Activity 1 Divide into four groups. Identify factors that
impact product quality from the category of your group
10. Measures Outcome Variables Dependent on the defects derived
from system testing and integration testing Development process two
phase : Implementation phase : requirements engineering, design,
implementation, module-level testing, fixing of module-level
defects Integration phase : integration and system testing Number
of defects identified in the second phase is a good indicator of
product quality
11. Measures Experience Modification request (MR) Average MR
Experience : average number of MRs that the project members worked
on prior to the focal project Average Component Experience :
average number of times that the project members modified the
components that need to be changed in the focal project prior to
the beginning of the focal project Average Shared Experience
12. Measures Geographic Dispersion Spatial distribution :
Euclidean distance between each pair of location Temporal
distribution : difference between time zone Configuration
dispersion : People dispersion Number of Locations and number of
Regional Units Number of Regional Units is more precise
13. Measures Technical Dependencies Interface among components
is a major source of errors In-flow technical dependencies : Number
of interfaces that components modified in the project use from
components that are not modified in the project but that are part
of the final system Out-flow technical dependencies : Interfaces
exported by components that are modified in the project and used by
components not changed in the project but are part of the final
system
14. Measures Project Time Pressure Planned delivery dates =
Customer requested delivery dates Number of overlapping activities
Tasks Temporal Execution : standard deviation of the number of
tasks completed in each month High values associated with uneven
distributions indicate time pressure in particular duration with a
high number of tasks to be completed
15. Measures Control Measure Size: sum of the number of lines
of source code added, deleted or modified Process Maturity: level
of discipline and sophistication of the development organization
and the supporting processes Complexity = Additional Factors:
Number of modification requests, number of developers
16. Measures Model Number of defects = count variable Negative
Binomial Regression Model is appropriate in this research
setting
17. Discussion Did you come across any of the measures which we
discussed today in your past industry or academic level project
experience? Did you use any tools available in market to reduce the
errors due to those measures ? Describe the functionality,
experience using the tool from your experience.
18. Results Variance Inflation Factor Variance Inflation Factor
above 10 -> High Multi-collinearity Variance Inflation Factor
above 5 -> Need to be handled carefully
19. Results : VIF
20. Results VIF based models Model 1 included all factors Model
2 - average component experience, number of modification requests,
spatial distribution Model 3 - number of new features, number of
developers, number of regional units
21. Results Incident Rate Ratio (IRR) Indicate the change in
the estimated counts of the outcome variable for a unit increase in
the independent variable holding the other variables constant
Greater than or equal to 1 indicates High Value : positive relation
between dependent and independent variables Less than 1 indicates
increase in independent variable with decrease in dependent
variable
22. Results : IRR
23. Results IRR Based models Model 1 Baseline model consist of
control factors Model 2 Increase in Average MR experience and
Average Shared Experience decreases errors Model 3 Higher number of
outflow technical dependencies indicates poorer quality Model 4
Higher number of locations and uneven people make dispersion higher
Uneven people dispersion has more impact than higher number of
locations
24. Results : IRR
25. Results IRR is dependent on the scale of independent
measure impact of each particular factor by understanding the
changes in quality for the full range of variation of each
independent factor Independent Measures % of Defects Task Temporal
Execution 47.1 % People Dispersion 45.2 % Number of Locations 35.7
% Flow of Technical Dependencies 28.3% Temporal Distribution 19.2%
Out- Shared Experience - 1.4% MR Experience - 29.2%
26. Discussion Is this statement valid in agile methodology
& why? Our analyses of 209 software development projects in a
large multination organization showed that two factors, time
pressure (measured as concurrent execution of tasks) and uneven
distribution of engineers across locations, were the two most
significant sources of errors
27. Results Factors improving the awareness and co-ordination
capabilities of collaborative tools: Project time pressure
Technical dependencies that cross project boundaries Dimensions of
distribution
28. Limitation Not able to collect interaction and
co-ordination data Not able to access data repositories from the
previous generations Did not include 31 projects which has
developers working on multiple projects
29. Implication for Collaborative Tools Supporting Coordination
and Awareness in Large- Scale Development Organizations: Supply the
pattern information to tool that will provide co-ordination and
awareness capabilities specific to context Awareness beyond
Traditional Boundaries: Use social computing tools to build social
ties among the members of the distributed teams
30. Relevance To Previous Paper Presentation Lets Go to the
Whiteboard: How and Why Software Developers Use Drawings
Distributed projects cannot take advantage of whiteboards for
understanding problem through visualization and creation of
drawings collaboratively
31. Discussion From this research study and considering current
technology trends, what are the things that should be included in
collaborative tools in order to reduce errors and improve product
quality?
32. Tool : World View
33. Similar to Ensemble Salesforce Chatter
https://www.youtube.com/watch?v=tv4hqseuD QA