The multi-objective next release problem

TLDR

The paper is concerned with the Multi‑Objective Next Release Problem (MONRP), a problem in search‑based requirements engineering that extends previous single‑objective work to a realistic multi‑objective formulation balancing conflicting demands. The study investigates the suitability of weighted and Pareto optimal genetic algorithms, including NSGA‑II, for solving the MONRP. An empirical comparison of these algorithms was conducted, focusing on weighted, Pareto optimal, and NSGA‑II approaches. The results demonstrate that NSGA‑II is well suited to the MONRP and provide benchmark data indicating the size above which the problem becomes non‑trivial.

Abstract

This paper is concerned with the Multi-Objective Next Release Problem (MONRP), a problem in search-based requirements engineering. Previous work has considered only single objective formulations. In the multi-objective formulation, there are at least two (possibly conflicting) objectives that the software engineer wishes to optimize. It is argued that the multi-objective formulation is more realistic, since requirements engineering is characterised by the presence of many complex and conflicting demands, for which the software engineer must find a suitable balance. The paper presents the results of an empirical study into the suitability of weighted and Pareto optimal genetic algorithms, together with the NSGA-II algorithm, presenting evidence to support the claim that NSGA-II is well suited to the MONRP. The paper also provides benchmark data to indicate the size above which the MONRP becomes non--trivial.