Pareto-optimal search-based software engineering (POSBSE): A literature survey

2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE) Pub Date : 2013-05-25 DOI:10.1109/RAISE.2013.6615200

Abdel Salam Sayyad, H. Ammar

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引用次数: 78

Abstract

The Search-Based Software Engineering (SBSE) community is increasingly recognizing the inherit “multiobjectiveness” in Software Engineering problems. The old ways of aggregating all objectives into one may very well be behind us. We perform a well-deserved literature survey of SBSE papers that used multiobjective search to find Pareto-optimal solutions, and we pay special attention to the chosen algorithms, tools, and quality indicators, if any. We conclude that the SBSE field has seen a trend of adopting the Multiobjective Evolutionary Optimization Algorithms (MEOAs) that are widely used in other fields (such as NSGA-II and SPEA2) without much scrutiny into the reason why one algorithm should be preferred over the others. We also find that the majority of published work only tackled two-objective problems (or formulations of problems), leaving much to be desired in terms of exploiting the power of MEOAs to discover solutions to intractable problems characterized by many trade-offs and complex constraints.

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基于pareto最优搜索的软件工程(POSBSE):文献综述

基于搜索的软件工程(SBSE)社区日益认识到软件工程问题中继承的“多目标性”。把所有目标集中为一个目标的旧方法很可能已经过时了。我们对使用多目标搜索找到帕累托最优解的SBSE论文进行了一项应得的文献调查，我们特别关注所选择的算法、工具和质量指标(如果有的话)。我们得出的结论是，SBSE领域已经看到了采用多目标进化优化算法(meoa)的趋势，这些算法在其他领域(如NSGA-II和SPEA2)中广泛使用，而没有仔细研究为什么一种算法应该优于其他算法。我们还发现，大多数已发表的工作只处理双目标问题(或问题的表述)，在利用meoa的力量来发现以许多权衡和复杂约束为特征的棘手问题的解决方案方面，还有很多需要改进的地方。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE)

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