A parallel portfolio approach to configuration optimization for large software product lines

Abstract

Summary Software product line (SPL) engineering demands for optimal or near‐optimal products that balance multiple often competing and conflicting objectives. A major challenge for large SPLs is to efficiently explore a huge space of various products and satisfy a large number of predefined constraints simultaneously. To improve the optimality and convergence speed, we propose a parallel portfolio approach, called IBEAPORT, which designs three algorithm variants by incorporating constraint solving into the indicator‐based evolutionary algorithm in different ways and performs these variants by utilizing parallelization techniques. Our approach utilizes the exploration capabilities of different algorithms and improves optimality as far as possible within a limited time budget. We evaluate our approach on five large‐scale real‐world SPLs. Empirical results demonstrate that our approach significantly outperforms the state of the art for all five SPLs on a quality indicator and a diversity indicator. Moreover, IBEAPORT quickly converges to a relatively stable hypervolume value even for the largest SPL with 6888 features.

Performance Evaluation Metrics for Multi-Objective Evolutionary Algorithms in Search-Based Software Engineering: Systematic Literature Review

“…As above-mentioned there are MOPs with no more than three objectives (2 and 3) and MaOPs with more than three objectives. As shown in Figure 12, two and three objectives are the most re-formulated problems, while there are increasing interest in the community in formulating MaOPs compared to previous studies' review [19], although there are number of studies that formulated a different number of objectives in a single study, such as References [75,76,86,91,95,96,102,108,109,117,[122][123][124][125]133,138,146,147]. Table 5 references of these objectives are stated.…”

“…This depends on the objective of the paper, some papers intend to formulate new problems while others only propose a new algorithm or compare existed algorithms by either applying existed formulated problems or considering new problem formulation. However, this does not indicate the practitioners are relaying the existed formulated problems, since the majority of them are formulating new problems with several objective functions, while we have seen studies employing a different number of objectives in a single study [75,76,86,91,95,96,102,108,109,117,[122][123][124][125]133,138,146,147]. Such practice of formulating a limited number of objectives shows the practitioners are either facing difficulties in re-formulating more objective functions that normally need a mathematical definition or defining a small number of objectives that are less expensive and easy to perform.…”

A Method for Feature Subset Selection in Software Product Lines

“…Shi et al introduced a parallel portfolio algorithm, IBEAPORT, which designs three algorithm variants by incorporating constraint solving into the indicator-based evolutionary algorithm in different ways and performs these variants by utilizing parallelization techniques (Shi et al, 2018). Their approach utilized the exploration capabilities of different algorithms and improved optimality as far as possible within a limited time budget.…”