Commonality and Variability in Software Engineering
IEEE Software
Feature-Oriented Project Line Engineering
IEEE Software
Software Product Line Engineering: Foundations, Principles and Techniques
Software Product Line Engineering: Foundations, Principles and Techniques
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
Pareto efficient multi-objective test case selection
Proceedings of the 2007 international symposium on Software testing and analysis
Optimal antenna placement using a new multi-objective chc algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A multi-objective approach to search-based test data generation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The multi-objective next release problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Decision-making coordination in collaborative product configuration
Proceedings of the 2008 ACM symposium on Applied computing
On the Effect of the Steady-State Selection Scheme in Multi-Objective Genetic Algorithms
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
MOCell: A cellular genetic algorithm for multiobjective optimization
International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Selecting highly optimal architectural feature sets with Filtered Cartesian Flattening
Journal of Systems and Software
S.P.L.O.T.: software product lines online tools
Proceedings of the 24th ACM SIGPLAN conference companion on Object oriented programming systems languages and applications
Automated reasoning for multi-step feature model configuration problems
Proceedings of the 13th International Software Product Line Conference
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Automated analysis of feature models 20 years later: A literature review
Information Systems
Variability modeling in the real: a perspective from the operating systems domain
Proceedings of the IEEE/ACM international conference on Automated software engineering
IEEE Transactions on Software Engineering
jMetal: A Java framework for multi-objective optimization
Advances in Engineering Software
Using knowledge-based systems to manage quality attributes in software product lines
Proceedings of the 15th International Software Product Line Conference, Volume 2
Journal of Systems and Software
Automated reasoning on feature models
CAiSE'05 Proceedings of the 17th international conference on Advanced Information Systems Engineering
Simulating and optimising design decisions in quantitative goal models
RE '11 Proceedings of the 2011 IEEE 19th International Requirements Engineering Conference
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
Automated generation of computationally hard feature models using evolutionary algorithms
Expert Systems with Applications: An International Journal
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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.