A survey of many-objective optimisation in search-based software engineering

被引:58
|
作者
Ramirez, Aurora [1 ]
Raul Romero, Jose [1 ]
Ventura, Sebastian [1 ]
机构
[1] Univ Cordoba, Dept Comp Sci & Numer Anal, E-14071 Cordoba, Spain
关键词
Search-based software engineering; Many-objective optimisation; Multi-objective optimisation; Evolutionary algorithms; Literature survey; EVOLUTIONARY ALGORITHMS; FEATURE-SELECTION; !text type='JAVA']JAVA[!/text] FRAMEWORK; MODEL; INTEGRATION; DIVERSITY;
D O I
10.1016/j.jss.2018.12.015
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Search-based software engineering (SBSE) is changing the way traditional software engineering (SE) activities are carried out by reformulating them as optimisation problems. The natural evolution of SBSE is bringing new challenges, such as the need of a large number of objectives to formally represent the many decision criteria involved in the resolution of SE tasks. This suggests that SBSE is moving towards many-objective optimisation, an emerging area that provides advanced techniques to cope with high dimensional optimisation problems. To analyse this phenomenon, this paper surveys relevant SBSE literature focused on the resolution of many-objective problems. From the gathered knowledge, current limitations regarding problem formulation, algorithm selection, experimental design and industrial applicability are discussed. Through the analysis of observed trends, this survey provides a historical perspective and future lines of research concerning the adoption of many-objective optimisation within SBSE. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码:382 / 395
页数:14
相关论文
共 50 条
  • [41] Beyond evolutionary algorithms for search-based software engineering
    Chen, Jianfeng
    Nair, Vivek
    Menzies, Tim
    [J]. INFORMATION AND SOFTWARE TECHNOLOGY, 2018, 95 : 281 - 294
  • [42] Implementing Search-Based Software Engineering Approaches with Nautilus
    Ferreira, Thiago Do Nascimento
    Vergilio, Silvia Regina
    Kessentini, Marouane
    [J]. ACM International Conference Proceeding Series, 2021, : 303 - 308
  • [43] Search-based software engineering for constructing covering arrays
    Torres-Jimenez, Jose
    Izquierdo-Marquez, Idelfonso
    Avila-George, Himer
    [J]. IET SOFTWARE, 2018, 12 (04) : 324 - 332
  • [44] Data-Driven Search-based Software Engineering
    Nair, Vivek
    Agrawal, Amritanshu
    Chen, Jianfeng
    Fu, Wei
    Mathew, George
    Menzies, Tim
    Minku, Leandro
    Wagner, Markus
    Yu, Zhe
    [J]. 2018 IEEE/ACM 15TH INTERNATIONAL CONFERENCE ON MINING SOFTWARE REPOSITORIES (MSR), 2018, : 341 - 352
  • [45] Search-Based Software Engineering: Trends, Techniques and Applications
    Harman, Mark
    Mansouri, S. Afshin
    Zhang, Yuanyuan
    [J]. ACM COMPUTING SURVEYS, 2012, 45 (01)
  • [46] Search-based software test data generation: a survey
    McMinn, P
    [J]. SOFTWARE TESTING VERIFICATION & RELIABILITY, 2004, 14 (02): : 105 - 156
  • [47] A Diversity Management Operator for Evolutionary Many-Objective Optimisation
    Adra, Salem F.
    Feming, Peter J.
    [J]. EVOLUTIONARY MULTI-CRITERION OPTIMIZATION: 5TH INTERNATIONAL CONFERENCE, EMO 2009, 2009, 5467 : 81 - +
  • [48] Many-Objective Directed Evolutionary Line Search
    Hughes, Evan J.
    [J]. GECCO-2011: PROCEEDINGS OF THE 13TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2011, : 761 - 768
  • [49] Radar waveform optimisation as a many-objective application benchmark
    Hughes, Evan J.
    [J]. EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PROCEEDINGS, 2007, 4403 : 700 - 714
  • [50] A hybrid many-objective cuckoo search algorithm
    Cui, Zhihua
    Zhang, Maoqing
    Wang, Hui
    Cai, Xingjuan
    Zhang, Wensheng
    [J]. SOFT COMPUTING, 2019, 23 (21) : 10681 - 10697