Abstract-Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature maps) using various search-based software engineering methods. As we increase the number of optimization objectives, we find that methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0% violations of domain constraints. Our conclusion is that we need to change our methods for search-based software engineering, particularly when studying complex decision spaces.

Abstract-Software product lines are hard to configure. Techniques that work for medium sized product lines fail for much larger product lines such as the Linux kernel with 6000+ features. This paper presents simple heuristics that help the Indicator-Based Evolutionary Algorithm (IBEA) in finding sound and optimum configurations of very large variability models in the presence of competing objectives. We employ a combination of static and evolutionary learning of model structure, in addition to utilizing a pre-computed solution used as a "seed" in the midst of a randomly-generated initial population. The seed solution works like a single straw that is enough to break the camel's back -given that it is a feature-rich seed. We show promising results where we can find 30 sound solutions for configuring upward of 6000 features within 30 minutes.

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Abstract-In Search-Based Software Engineering, well-known metaheuristic search algorithms are utilized to find solutions to common software engineering problems. The algorithms are usually taken "off the shelf" and applied with trust, i.e. software engineers are not concerned with the inner workings of algorithms, only with the results. While this may be sufficient is some domains, we argue against this approach, particularly where the complexity of the models and the variety of user preferences pose greater challenges to the metaheuristic search algorithms. We build on our previous investigation which uncovered the power of Indicator-Based Evolutionary Algorithm (IBEA) over traditionally-used algorithms (such as NSGA-II), and in this work we scrutinize the time behavior of user objectives subject to optimization. This analysis brings out the business perspective, previously veiled under Pareto-collective gauges such as Hypervolume and Spread. In addition, we show how slowing down the rates of crossover and mutation can help IBEA converge faster, as opposed to following the higher rates used in many other studies as "rules of thumb".

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