Agent-based simulation for software project planning
WSC '05 Proceedings of the 37th conference on Winter simulation
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
A search-based approach for dynamically re-packaging downloadable applications
CASCON '07 Proceedings of the 2007 conference of the center for advanced studies on Collaborative research
Locating dependence structures using search-based slicing
Information and Software Technology
Special Issue on Search-Based Software Maintenance
Journal of Software Maintenance and Evolution: Research and Practice - Search Based Software Engineering [SBSE]
Information and Software Technology
Disruption-driven resource rescheduling in software development processes
ICSP'10 Proceedings of the 2010 international conference on New modeling concepts for today's software processes: software process
Using multi-objective metaheuristics to solve the software project scheduling problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Search based software engineering
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Dynamic resource scheduling in disruption-prone software development environments
FASE'10 Proceedings of the 13th international conference on Fundamental Approaches to Software Engineering
Robust solutions for the software project scheduling problem: a preliminary analysis
International Journal of Metaheuristics
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
Exact scalable sensitivity analysis for the next release problem
ACM Transactions on Software Engineering and Methodology (TOSEM)
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Managing a large software project involves initial estimates that may turn out to be erroneous or that might be expressed with some degree of uncertainty. Furthermore, as the project progresses, it often becomes necessary to rework some of the work packages that make up the overall project. Other work packages might have to be abandoned for a variety of reasons. In the presence of these difficulties, optimal allocation of staff to project teams and teams to work packages is far from trivial. This paper shows how genetic algorithms can be combined with a queuing simulation model to address these problems in a robust manner. A tandem genetic algorithm is used to search for the best sequence in which to process work packages and the best allocation of staff to project teams. The simulation model, that computes the project estimated completion date, guides the search. The possible impact of rework, abandonment and erroneous or uncertain initial estimates are characterised by separate error distributions. The paper presents results from the application of these techniques to data obtained from a large scale commercial software maintenance project.