TY - GEN
T1 - A multi-objective and cost-aware optimization of requirements assignment for review
AU - Li, Yan
AU - Yue, Tao
AU - Ali, Shaukat
AU - Zhang, Li
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/5
Y1 - 2017/7/5
N2 - A typical way to improve the quality of requirements is to assign them to suitable stakeholders for reviewing. Due to different characteristics of requirements and diverse background of stakeholders, it is needed to find an optimal solution for requirements assignment. Existing search-based requirements assignment solutions focus on maximizing stakeholders' familiarities to assigned requirements and balancing the overall workload of each stakeholder. However, a cost-effective requirements assignment solution should also take into account another two optimization objectives: 1) minimizing required time for reviewing requirements, and 2) minimizing the monetary cost required for performing reviewing tasks. We formulated the requirements assignment problem as a search problem and defined a fitness function considering all the five optimization objectives. We conducted an empirical evaluation to assess the fitness function together with six search algorithms using a real-world case study and 120 artificial problems to assess the scalability of the proposed fitness function. Results show that overall, our optimization problem is complex and further justifies the use for multi-objective search algorithms, and the Speed-constrained Multi-Objective Particle Swarm Optimization (SMPSO) algorithm performed the best among all the search algorithms.
AB - A typical way to improve the quality of requirements is to assign them to suitable stakeholders for reviewing. Due to different characteristics of requirements and diverse background of stakeholders, it is needed to find an optimal solution for requirements assignment. Existing search-based requirements assignment solutions focus on maximizing stakeholders' familiarities to assigned requirements and balancing the overall workload of each stakeholder. However, a cost-effective requirements assignment solution should also take into account another two optimization objectives: 1) minimizing required time for reviewing requirements, and 2) minimizing the monetary cost required for performing reviewing tasks. We formulated the requirements assignment problem as a search problem and defined a fitness function considering all the five optimization objectives. We conducted an empirical evaluation to assess the fitness function together with six search algorithms using a real-world case study and 120 artificial problems to assess the scalability of the proposed fitness function. Results show that overall, our optimization problem is complex and further justifies the use for multi-objective search algorithms, and the Speed-constrained Multi-Objective Particle Swarm Optimization (SMPSO) algorithm performed the best among all the search algorithms.
KW - Muti-objectives search algorithms
KW - Requirements assignment
KW - Search-based software engineering
UR - https://www.scopus.com/pages/publications/85027892914
U2 - 10.1109/CEC.2017.7969300
DO - 10.1109/CEC.2017.7969300
M3 - 会议稿件
AN - SCOPUS:85027892914
T3 - 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
SP - 89
EP - 96
BT - 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Congress on Evolutionary Computation, CEC 2017
Y2 - 5 June 2017 through 8 June 2017
ER -