1. 1. Marcelo Cataldo, Robert Bosch LLC Presented By Bhagyashree Deokar Sources of Errors in Distributed Development Projects: Implications for Collaborative Tools
2. 2. Outline of Paper Introduction Research Setting Sources of Errors in Distributed Project Measure Results Limitation Implication For Collaborative Tools
3. 3. Introduction Success of Product Development Project: Market Performance Of The Product Project Cycle Time Efficiency of the Development Process Product Quality
4. 4. Discussion What are the barriers these people would have faced in order to conduct this meeting?
5. 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. 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. 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. 8. Sources of Errors in Distributed Project Types of experience Dimensions of geographic dispersion Technical properties of the product Projects time pressure
9. 9. Activity 1 Divide into four groups. Identify factors that impact product quality from the category of your group
10. 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. 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. 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. 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. 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. 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. 16. Measures Model Number of defects = count variable Negative Binomial Regression Model is appropriate in this research setting
17. 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. 18. Results Variance Inflation Factor Variance Inflation Factor above 10 -> High Multi-collinearity Variance Inflation Factor above 5 -> Need to be handled carefully
19. 19. Results : VIF
20. 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. 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. 22. Results : IRR
23. 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. 24. Results : IRR
25. 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. 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. 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. 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. 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. 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. 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. 32. Tool : World View
33. 33. Similar to Ensemble Salesforce Chatter https://www.youtube.com/watch?v=tv4hqseuD QA
34. 34. Thank You