Abstract

In this paper, the modeling of developers' assignment to bugs (DAB) is studied. The problem is modeled both as a single objective (minimize bug fix time) and as a bi-objective (minimize bug fix time and cost) combinatorial optimization problem. Two models of developer assignment are considered where in the first model a single developer is assigned per bug (single developer model), while in the second model a single developer is assigned for each competency area of a bug (individual competency model). The latter model is proposed in this paper. For the single developer model, GA@DAB, an existing genetic algorithm-based approach, is extended to support precedence among bugs. For the individual competency model of DAB, one genetic algorithm-based approach (Competence@DAB) and one nondominated sorting genetic algorithm II-based approach (CompetenceMulti 2 @DAB ) are proposed to generate solutions minimizing time and minimizing both time and cost, respectively. The performance of the proposed approaches was evaluated for 2040 bugs of 19 open-source milestone projects from the ECLIPSE platform. Our results and analysis show that the proposed individual competency model is far better than the single developer model, with average bug fix time reduction of 39.7% across all projects.

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.