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2019-06-21 16:41:49
2019-06-21
近日，我們的論文Going Deeper With Optimal Software Products Selection Using Many-objective Optimization and Satisfiability Solvers被軟件工程CCF-B類期刊Empirical Software Engineering正式錄用。
以下是論文摘要：
In search-based software engineering, one actively studied problem is the optimal
software product selection from a feature model using multiple (usually more than three)
optimization objectives simultaneously. This can be represented as a many-objective
optimization problem. The primary goal of solving this problem is to search for diverse
and high-quality valid products as rapidly as possible. Previous studies have shown that
combining search-based techniques with satisfiability (SAT) solvers was promising for
achieving this goal, but it remained open that how different solvers affect the performance
of a search algorithm, and that whether the ways to randomize solutions in the solvers
make a difference. Moreover, we may need further investigation on the necessity of mixing
different types of SAT solving techniques. In this paper, we address the above open research
questions by performing a series of empirical studies on 21 features models, most of which
are reverse-engineered from industrial software product lines. We examine four conflictdriven
clause learning solvers, two stochastic local search solvers, and two different ways
to randomize solutions. Experimental results suggest that the performance can be indeed
affected by different SAT solvers, and by the ways to randomize solutions in the solvers.
This study serves as a practical guideline for choosing and tuning SAT solvers for the manyobjective
optimal software product selection problem.
