Requirements review is an effective technique to ensure the quality of requirements in practice, especially in safety-critical domains (e.g., avionics systems, automotive systems). In such contexts, a typical requirements review process often prioritizes requirements, due to limited time and monetary budget, by, for instance, prioritizing requirements with higher implementation cost earlier in the review process. However, such a requirement implementation cost is typically estimated by stakeholders who often lack knowledge about (future) requirements implementation scenarios, which leads to uncertainty in cost overrun. In this article, we explicitly consider such uncertainty (quantified as cost overrun probability) when prioritizing requirements based on the assumption that a requirement with higher importance, a higher number of dependencies to other requirements, and higher implementation cost will be reviewed with the higher priority. Motivated by this, we formulate four objectives for uncertainty-wise requirements prioritization: maximizing the importance of requirements, requirements dependencies, the implementation cost of requirements, and cost overrun probability. These four objectives are integrated as part of our search-based uncertainty-wise requirements prioritization approach with tool support, named as URP. We evaluated six Multi-Objective Search Algorithms (MOSAs) (i.e., NSGA-II, NSGA-III, MOCell, SPEA2, IBEA, and PAES ) together with Random Search ( RS ) using three real-world datasets (i.e., the RALIC, Word, and ReleasePlanner datasets) and 19 synthetic optimization problems. Results show that all the selected MOSAs can solve the requirements prioritization problem with significantly better performance than RS . Among them, IBEA was over 40% better than RS in terms of permutation effectiveness for the first 10% of prioritized requirements in the prioritization sequence of all three datasets. In addition, IBEA achieved the best performance in terms of the convergence of solutions, and NSGA-III performed the best when considering both the convergence and diversity of nondominated solutions.

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.

To ensure the quality of requirements, a common practice, especially in critical domains, is to review requirements within a limited time and monetary budgets. A requirement with higher importance, larger number of dependencies with other requirements, and higher implementation cost should be reviewed with the highest priority. However, requirements are inherently uncertain in terms of their impact on the requirements implementation cost. Such cost is typically estimated by stakeholders as an interval, though an exact value is often used in the literature for requirements optimization (e.g., prioritization). Such a practice, therefore, ignores uncertainty inherent in the estimation of requirements implementation cost. This paper explicitly takes into account such uncertainty for requirement prioritization and formulates four objectives for uncertainty-wise requirements prioritization with the aim of maximizing 1) the importance of requirements, 2) requirements dependencies, 3) the implementation cost of requirements, and 4) cost over-run probability. We evaluated the multi-objective search algorithm NSGA-II together with Random Search (RS) using the RALIC dataset and 19 artificial problems. Results show that NSGA-II can solve the requirements prioritization problem with a significantly better performance than RS. Moreover, NSGA-II can prioritize requirements with higher priority earlier in the prioritization sequence. For example, in the case of the RALIC dataset, the first 10% of prioritized requirements in the prioritization sequence are on average 50% better than RS in terms of prioritization effectiveness.

Many software engineering problems are multi-objective in nature, which has been largely recognized by the Search-based Software Engineering (SBSE) community. In this regard, Pareto-based search algorithms, e.g., Non-dominated Sorting Genetic Algorithm II, have already shown good performance for solving multi-objective optimization problems. These algorithms produce Pareto fronts, where each Pareto front consists of a set of non-dominated solutions. Eventually, a user selects one or more of the solutions from a Pareto front for their specific problems. A key challenge of applying Pareto-based search algorithms is to select appropriate quality indicators, e.g., hypervolume, to assess the quality of Pareto fronts. Based on the results of an extended literature review, we found that the current literature and practice in SBSE lacks a practical guide for selecting quality indicators despite a large number of published SBSE works. In this direction, the paper presents a practical guide for the SBSE community to select quality indicators for assessing Pareto-based search algorithms in different software engineering contexts. The practical guide is derived from the following complementary theoretical and empirical methods: 1) key theoretical foundations of quality indicators; 2) evidence from an extended literature review; and 3) evidence collected from an extensive experiment that was conducted to evaluate eight quality indicators from four different categories with six Pareto-based search algorithms using three real industrial problems from two diverse domains.