近日，我们的论文Going Deeper With Optimal Software Products Selection Using Many-objective Optimization and Satisfiability Solvers被软件工程CCF-B类期刊Empirical Software Engineering正式录用。

以下是论文摘要：

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.