A Multi-objective and Cost-Aware Optimization of Requirements Assignment

Yan Li1, Tao Yue2,3, Shaukat Ali2, Li Zhang1

1Beihang University, Beijing, China; 2Simula Research Laboratory, Oslo, Norway; 3Department of Informatics, University of Oslo, Oslo, Norway;
Contact: yanll@buaa.edu.cn, {tao, shaukat}@simula.no, lily@buaa.edu.cn

Abstract

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 re-viewing 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-objectives search algorithms, and Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) performed the best among all the search algorithms.

For implementation:

As mentioned in the paper, all the selected search algorithms and quality indicators are implemented based on jMetal [1].

For the experiment data: Two types of data are made available: 1) We provide the real world case (Subsea)experiment data, including subsea requirements file (reqif file),subsea requirements property and related stakeholders' property, and the data for objective function values,corresponding solutions and time taken by running each algorithm. 2) We provide the artificial problems experiment data, including requirements property and related stakeholders' property, and the data for objective function values,corresponding solutions and time taken by running each algorithm.

Data related with the experiment

Download The data related with the real world case (subsea)

Download The data related with artificial problems(120 problems)