Multi-objective software performance optimisation at the architecture level using randomised search rules

Ni，Youcong1,3; Du，Xin1,3; Ye，Peng2; Minku，Leandro L.4; Yao，Xin5; Harman，Mark6; Xiao，Ruliang1

2021-07-01

10.1016/j.infsof.2021.106565

INFORMATION AND SOFTWARE TECHNOLOGY   影响因子和分区

0950-5849

Architecture-based software performance optimisation can help to find potential performance problems and mitigate their negative effects at an early stage. To automate this optimisation process, rule-based and metaheuristic-based performance optimisation methods have been proposed. However, existing rule-based methods explore a limited search space, potentially excluding optimal or near-optimal solutions. Most of current metaheuristic-based methods ignore existing practical knowledge of performance improvement, and lead to solutions that are not easily explicable to humans. To address these problems, we propose a novel approach for performance optimisation at the software architecture level named Multiobjective performance Optimisation based on Randomised search rulEs (MORE). First, we design randomised search rules (MORE-R) to provide explanation without parameters while benefiting from existing practical knowledge of performance improvement. Second, based on all possible composite applications of MORE-R, an explicable multi-objective optimisation problem (MORE-P) is defined to enlarge search space and enable solutions explicable to architectural stakeholder. Third, a multi-objective evolutionary algorithm (MORE-EA) with an introduced do-nothing rule, innovative encoding and repair mechanism is designed to effectively solve MORE-P. The experiments show that MORE is able to achieve more explicable and higher quality solutions than two state-of-the-art techniques. They also demonstrate the benefits of integrating search-based software engineering approaches with practical knowledge.

Multi-objective evolutionary optimisation Performance optimisation Randomised search rule Software architecture

SCI ; EI

英语

其他

WOS:000642489200004

20211210125924

Evolutionary algorithms ; Software engineering

Computer Programming:723.1 ; Optimization Techniques:921.5

COMPUTER SCIENCE

2-s2.0-85102897223

Scopus

1.College of Mathematics and Informatics,Fujian Normal University,Fuzhou,350117,China
2.College of Mathematics and Computer,Wuhan Textile University,Wuhan,430200,China
3.Fujian Provincial Engineering Technology Research Center for Public Service Big Data Mining and Application,Fujian Normal University,FuzhouFujian,350007,China
4.School of Computer Science,University of Birmingham,Birmingham,B15 2TT,United Kingdom
5.Department of computer science and engineering,Southern University of Science and Technology,Shenzhen,518055,China
6.Department of Computer Science,University College London,London,WC1E 6BT,United Kingdom

Ni，Youcong,Du，Xin,Ye，Peng,et al. Multi-objective software performance optimisation at the architecture level using randomised search rules[J]. INFORMATION AND SOFTWARE TECHNOLOGY,2021,135.

Ni，Youcong.,Du，Xin.,Ye，Peng.,Minku，Leandro L..,Yao，Xin.,...&Xiao，Ruliang.(2021).Multi-objective software performance optimisation at the architecture level using randomised search rules.INFORMATION AND SOFTWARE TECHNOLOGY,135.

Ni，Youcong,et al."Multi-objective software performance optimisation at the architecture level using randomised search rules".INFORMATION AND SOFTWARE TECHNOLOGY 135(2021).