Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Cost-Value Approach for Prioritizing Requirements
IEEE Software
Converging on the Optimal Attainment of Requirements
RE '02 Proceedings of the 10th Anniversary IEEE Joint International Conference on Requirements Engineering
Software Requirements Prioritizing
ICRE '96 Proceedings of the 2nd International Conference on Requirements Engineering (ICRE '96)
Quantitative Studies in Software Release Planning under Risk and Resource Constraints
ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
Module-Order Modeling using an Evolutionary Multi-Objective Optimization Approach
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
Improving network applications security: a new heuristic to generate stress testing data
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Search--based approaches to the component selection and prioritization problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
TimeAware test suite prioritization
Proceedings of the 2006 international symposium on Software testing and analysis
Search Based Approaches to Component Selection and Prioritization for the Next Release Problem
ICSM '06 Proceedings of the 22nd IEEE International Conference on Software Maintenance
Lightweight Replanning of Software Product Releases
IWSPM '06 Proceedings of the International Workshop on Software Product Management
Hybrid Intelligence in Software Release Planning
International Journal of Hybrid Intelligent Systems
Software project management with GAs
Information Sciences: an International Journal
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
A multi-objective approach to search-based test data generation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Pareto optimal search based refactoring at the design level
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
Bi-objective release planning for evolving software systems
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Automatic Generation of Floating-Point Test Data
IEEE Transactions on Software Engineering
An optimization framework for "build-or-buy" decisions in software architecture
Computers and Operations Research
Search Based Requirements Optimisation: Existing Work and Challenges
REFSQ '08 Proceedings of the 14th international conference on Requirements Engineering: Foundation for Software Quality
"Fairness Analysis in Requirements Assignments
RE '08 Proceedings of the 2008 16th IEEE International Requirements Engineering Conference
Cellular Genetic Algorithms
A systematic review of search-based testing for non-functional system properties
Information and Software Technology
MOCell: A cellular genetic algorithm for multiobjective optimization
International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
A Study of the Multi-objective Next Release Problem
SSBSE '09 Proceedings of the 2009 1st International Symposium on Search Based Software Engineering
Requirements Engineering - Special Issue on RE'08: Requirements Engineering for a Sustainable World; Guest Editor: T. Tamai
Design issues in a multiobjective cellular genetic algorithm
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Automated selection of software components based on cost/reliability tradeoff
EWSA'06 Proceedings of the Third European conference on Software Architecture
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
Multiobjective optimization of safety related systems: an application to short-term conflict alert
IEEE Transactions on Evolutionary Computation
Solving acquisition problems using model-driven engineering
ECMFA'12 Proceedings of the 8th European conference on Modelling Foundations and Applications
Dynamic adaptive search based software engineering
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Empirical evaluation of search based requirements interaction management
Information and Software Technology
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One important issue addressed by software companies is to determine which features should be included in the next release of their products, in such a way that the highest possible number of customers get satisfied while entailing the minimum cost for the company. This problem is known as the Next Release Problem (NRP). Since minimizing the total cost of including new features into a software package and maximizing the total satisfaction of customers are contradictory objectives, the problem has a multi-objective nature. In this work, we apply three state-of-the-art multi-objective metaheuristics (two genetic algorithms, NSGA-II and MOCell, and one evolutionary strategy, PAES) for solving NRP. Our goal is twofold: on the one hand, we are interested in analyzing the results obtained by these metaheuristics over a benchmark composed of six academic problems plus a real world data set provided by Motorola; on the other hand, we want to provide insight about the solution to the problem. The obtained results show three different kinds of conclusions: NSGA-II is the technique computing the highest number of optimal solutions, MOCell provides the product manager with the widest range of different solutions, and PAES is the fastest technique (but with the least accurate results). Furthermore, we have observed that the best solutions found so far are composed of a high percentage of low-cost requirements and of those requirements that produce the largest satisfaction on the customers as well.