evaluation of optimized staffing for feature development and bug fixing
Skill level and productivity varies substantially between developers. In current staffing practices, however, developers are largely treated as the same. In this paper, an empirical analysis of the tow formulations of assignment of developers to tasks and bug fixing activities is studied. Two related problems are considered: (i) Assignment of developers to bug fixing with the objective to achieve best match between requested skill profile and assigned developer's skill profile. (ii) Assignment of developers to feature-related tasks in iterative development process. Two optimization approaches have been customized to determine qualified staffing plans. They are based on greedy optimization respectively genetic algorithm (GA). Empirical analysis is done for nine milestones of the open source Eclipse JDT project and two industrial case study projects. The main conclusion drawn from the analysis is that substantial savings can be achieved from optimized staffing policies when compared to the manual plans formerly applied. More specifically, the GA results are mostly the best, and the (lightweight) Greedy search becomes the better the bigger the look-ahead time L. Overall, the results are considered as decision support in finding better staffing policies in shorter time.
staffing empirical analysis greedy optimization genetic algorithm case study