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'''Search-based software engineering''' ('''SBSE''') applies [[metaheuristic]] search techniques such as [[genetic algorithms]], [[simulated annealing]] and [[tabu search]] to [[software engineering]] problems. Many activities in [[software engineering]] can be stated as [[Optimization (mathematics)|optimization]] problems. [[Optimization (mathematics)|Optimization]] techniques of [[operations research]] such as [[linear programming]] or [[dynamic programming]] are often impractical for large scale [[software engineering]] problems because of their [[Computational complexity theory|computational complexity]] or their assumptions on the problem structure. Researchers and practitioners use [[metaheuristic]] search techniques, which impose little assumptions on the problem structure, to find near-optimal or "good-enough" solutions.
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[[Requirements engineering]] is the process by which the needs of a software's users and environment are determined and managed. Search-based methods have been used for requirements selection and optimisation with the goal of finding the best possible subset of requirements that matches user requests amid constraints such as limited resources and interdependencies between requirements. This problem is often tackled as a [[MCDM|multiple-criteria decision-making]] problem and, generally involves presenting the decision maker with a set of good compromises between cost and user satisfaction as well as the requirements risk.<ref>
{{Cite thesis
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| publisher = University of London
| last = Zhang
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