TAMRI: A Tool for Supporting Task Distribution in Global Software Development Projects

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1 TAMRI: A Tool for Supporting Task Distribution in Global Software Development Projects REMIDI 2009, Limerick, Ireland Ansgar Lamersdorf a_lamers@informatik.uni-kl.de Jürgen Münch Juergen.Muench@iese.fraunhofer.de

2 Outline Motivation Model Overview Application Scenarios The TAMRI Tool Summary and Outlook Page 2/18

3 Motivation: Task Allocation Software development processes have to be allocated to distributed sites TAMRI: Tool for Supporting Task Distribution n tasks to m sites up to m n different assignments theoretically possible Task allocation has to consider abilities at sites and communication overhead In practice: often task allocation by cost and availability only High development risks Page 3/18

assignments theoretically possible Task allocation has to consider abilities at sites and

4 Challenges of Task Allocation Decisions Multiple goals Multiple influences Organization-specific contexts Uncertainty Cost reduction (assign to low-cost sites), development time (reduce communication overhead), development quality (assign to experts), closeness to markets E.g., expertise at sites, proximity to customer, time zone difference, cultural distance, communication need Different weight on goals, other influencing factors, differences in importance of influences Not all characteristics of remote sites are known; uncertainty in predicting human behavior Not handled by existing planning approaches Research goal: Build decision support model addressing these challenges Page 4/18

5 TAMRI Overview Identified in empirical study Organizational knowledge: Influencing factors Causal relationships Goal: Task allocation based on multiple criteria Based on specific project characteristics and general organizational experience, make suggestions for task assignment Task allocation has to consider multiple goals and criteria Decision model Input: Values for influencing factors (actual characteristics of project, tasks, sites), weighted goals Output: Assignment suggestions Page 5/18

suggestions for task assignment Task allocation has to consider multiple goals and criteria Decision model Input: Values for

6 Empirical Study: Influencing Factors TAMRI: Tool for Supporting Task Distribution Study goal Which factors influence project goals in GSD? Study design Literature study and interviews with 10 practitioners Language difference Cultural difference ++ Common experiences ++ Infrastructur e distance -- Time shift Problems in Communication, Coordination, Control - Coupling between tasks Labor Costs Productivity Expertise & Knowledge ++ Possibility of Followthe-sun Follow-thesun benefit Process maturity Proximity to customer --- Knowledge fit Needed Proximity Needed Expertise for tasks Model describes causal relations under uncertainty Bayesian Networks Result: Model for evaluating assignment Influences of task assignment on goals Costs Time Quality Page 6/18

Communication, Coordination, Control - Coupling between tasks Labor Costs ++ -- --- ++ Productivity Expertise & Knowledge ++ Possibility of Followthe-sun Follow-thesun benefit Process maturity +++

7 Modeling Impact with Bayesian Networks Bayesian Networks are used for modeling causal relationships under uncertainty Values for each causal relationship are determined by probabilistic tables Example: high Communication need medium Time zone distance Communication need Time zone distance OH Low Med High Low 0% 5% 10% 15% 20% Medium 0% 5% 10% 15% 20% High 0% 2% 5% 8 % 10% 35% 15% 30% 20% 25% Overhead TAMRI: Table values were determined through - study results - own estimations - and mathematical functions Probability 0% 5% 10% 15% 20% Overhead Page 7/18

Time zone distance OH Low Med High Low 0% 5% 10% 15% 20% Medium 0% 5% 10% 15% 20% High 0% 2% 5% 8 % 10% 35% 15% 30% 20% 25% Overhead TAMRI:

8 TAMRI Overview Results of empirical study Optimization algorithm Input: Values for influencing factors Evaluation model Distributed Systems Algorithm Output: Assignment suggestions Evaluation model Optimization algorithm Describes the results of concrete assignment on goals Bayesian Networks Uses assignment evaluation Identifies optimal assignment Based on algorithm of distributed systems (optimal assignment of tasks to processor nodes) Page 8/18

algorithm Describes the results of concrete assignment on goals Bayesian Networks Uses assignment evaluation

9 TAMRI Overview Results of empirical study Input: Values for influencing factors Bayesian Network (task execution) Bayesian Network (transmission) Output: Statistical distribution over impact Optimization algorithm Distributed Systems Algorithm Evaluation model uses Bayesian Networks (BN) BN for execution tasks at site: What is the impact on goals if task A is assigned to site 1? BN for transmission overhead: What is the impact of overhead for transmitting information between task A at site 1 and task B at site 2? BNs can be instantiated for every assignment of tasks to sites Result: not single values but probabilistic distribution Page 9/18 Output: Assignment suggestions

impact on goals if task A is assigned to site 1?

10 TAMRI Overview Results of empirical study Input: Values for influencing factors Bayesian Network (task execution) Bayesian Network (transmission) Output: Statistical distribution over impact Optimization algorithm: Randomization Pick values Distributed Systems Algorithm Input: Execution impact (site, task) Transmission impact (site, site, task, task) Output: Optimal assignment For n >> 1 (n=1000) - Pick random values for every impact function based on statistical distributions - Execute distributed system algorithm - Store optimal assignment Store in list Page 10/18 Output: Weighted list of assignment suggestions

Transmission impact (site, site, task, task) Output: Optimal assignment For n >> 1 (n=1000) - Pick random values for every impact function based on

11 Scenario Overview component_ requirements Tasks: component_ requirements generate_ test cases test_ cases white_box_test transferred input transferred input component_ design transferred input transferred output create_ component_ design component_ design ad_hoc_ design_ inspection component_development defect_ list perform_ tests executable_ code create_ component_ code test_ results transferred output transferred output executable_ code Sites: - low labor costs - small distance - no time shift A - near customer - high labor costs - large distance - large time shift - low labor costs B - large distance - large time shift C Page 11/18 Source: Goldmann, S., Münch, J., Holz, H. (2000): Distributed Process Planning Support with MILOS

component_ code test_ results transferred output transferred output executable_ code Sites: - low labor costs - small distance - no time shift A - near customer - high labor costs -

12 Task Allocation Scenario (1) TAMRI: Tool for Supporting Task Distribution Scenario 1 Component development & testing - Development is done at A, testing needs to be assigned to B or C - Follow-the-sun possible between A and C - Higher familiarity at B - Cost rate no criterion Testing Development Page 12/18

testing needs to be assigned to B or C - Follow-the-sun possible between A

13 Tool Implementation: Scenario 1 TAMRI: Tool for Supporting Task Distribution Factors are organizationspecific and can be changed easily in the implementation Weight Tasks Characteristics and placed Sites of on can development sites testing quality: be AB with added task development task respect Assignment to testing to time: familiar task follow-the-sun site with suggested C slightly prioritized Page 13/18

placed Sites of on can development sites testing quality: be AB with added task development task

14 Task Allocation Scenario (2) TAMRI: Tool for Supporting Task Distribution Scenario 2 Test 1 Dev 1 Development and testing of 3 components - All tasks can be assigned freely - Different coupling between components (1 2: high, 1 3, 2 3: low) - Expertise for development at A, testing at B - Low labor costs at C, possibility of follow-thesun Test 2 Dev 2 Dev 3 Test 3 Page 14/18

coupling between components (1 2: high, 1 3, 2 3: low) - Expertise for development at A,

15 Results Scenario 2 Priority: quality and cost Due to complexity, significance in results not so high (best assignment only optimal in 16% of runs) However, general suggestions can be derived from result list - Development should be done at A - It is generally better to do testing at B than at C - But single testing tasks could also be done at C Page 15/18

can be derived from result list - Development should be done at A - It is generally

16 Summary: Characteristics of TAMRI Model Multiple goals Multiple influences Organization-specific context Uncertainty Different goals can be weighted and aggregated into one impact function Large number of different influencing factors can be specified in Bayesian Networks Bayesian Network concept allows for easily exchanging networks without having to change underlying algorithms; Bayesian Networks can be adapted to organization-specific goals, influencing factors, weights Bayesian Networks can describe causal relationships under uncertainty; results can be produced without having to specify all input parameters; model output reflects uncertainty by providing multiple suggestions Page 16/18

having to change underlying algorithms; Bayesian Networks can be adapted to organization-specific goals, influencing factors, weights Bayesian Networks can describe causal

17 Future Research Adaptation of model to specific contexts Evaluation Eliciting organizational knowledge Process for decision support Bayesian Networks are based on empirical study with input from various types of GSD - Model needs to be adapted to specific environments / organization The model has only been evaluated in hypothetical scenarios - Future work: evaluation in real-world context - Will practitioners accept black-box model? Underlying assumption: knowledge about organization is very detailed (e.g., cultural differences between sites can be classified) - Methods for eliciting knowledge have to be found The decision model has to be integrated into an organizational task allocation process - Includes roles, responsibilities, sub-processes for eliciting knowledge, determining influencing factors Page 17/18

Will practitioners accept black-box model? Underlying

18 Thank you for your attention! Contact: Dr. Jürgen Münch Ansgar Lamersdorf Page 18/18

T task Distribution and Selection Based Algorithm

T task Distribution and Selection Based Algorithm 2009 Fourth IEEE International Conference on Global Software Engineering TAMRI: A Tool for Supporting Task Distribution in Global Software Development Projects Ansgar Lamersdorf University of Kaiserslautern