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

Ramírez, Aurora; Romero, José Raúl; Ventura, Sebastián

doi:10.1016/j.jss.2018.12.015

Cited by 77 publications

(29 citation statements)

References 68 publications

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“…Some of them focus on or are relevant to Pareto-based multi-objective optimization. For example, Sayyad et al [117] performed a brief literature review of SBSE studies that used Paretobased evolutionary algorithms for multi-objective optimization problems; Boussaïd et al [8] conducted a comprehensive survey on search-based model-driven engineering and classified relevant search algorithms into single-and multiobjective ones; Ramírez et al [110] reviewed SBSE studies on a subarea of multi-objective optimization, many-objective optimization, where the number of objectives is larger than 3. In general, these papers concentrate on the development of search algorithms for Pareto-based multi-objective optimization problems; very few touch on the quality evaluation of the results obtained by search algorithms until recently.…”

Section: Related Workmentioning

confidence: 99%

How to Evaluate Solutions in Pareto-Based Search-Based Software Engineering: A Critical Review and Methodological Guidance

Chen

Yao

2022

IIEEE Trans. Software Eng.

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Section: Related Workmentioning

confidence: 99%

How to Evaluate Solutions in Pareto-Based Search-Based Software Engineering: A Critical Review and Methodological Guidance

Chen

Yao

2022

IIEEE Trans. Software Eng.

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“…The clustering algorithm used in this study is a multiobjective genetic algorithm called NSGA-II (nondominated sorting genetic algorithm). 38 As cohesion and coupling objectives conflict with each other, solving SI problem in light of only one of them is naturally unreasonable and may lead to unreasonable results. A logical solution for a multiobjective problem is to analyze a set of solutions, that meet the objectives to a reasonable extent and is not affected by other solutions.…”

Section: B2: the Proposed Clustering Methods For Simentioning

confidence: 99%

“…A logical solution for a multiobjective problem is to analyze a set of solutions, that meet the objectives to a reasonable extent and is not affected by other solutions. 38 Although weighting the objectives by a genetic nondominated algorithm is an alternative solution, determining these weights would be very difficult for the designer who does not know exactly how much coupling should be sacrificed for cohesion or vice versa. 36 For the clustering algorithm, six objectives are considered simultaneously:…”

Section: B2: the Proposed Clustering Methods For Simentioning

confidence: 99%

A model driven and clustering method for service identification directed by metrics

Daghaghzadeh

Babamir

2020

Softw Pract Exp

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Service identification (SI) in the life cycle of service‐oriented architecture is a critical phase. Business models consisting of business process (BP) model and business entity (BE) model are the useful models that may be used for SI. To this end, SI is carried out by partitioning activities in BP based on the activities' use of the entities in BE. However, a proper partitioning activities to services, which is called a service design, is a challenge. This article aims to present a semiautomatized clustering method for partitioning the activities to services, which is directed by new proposed metrics cohesion, coupling, and granularity. With regard to the conflict of the metrics, a multiobjective evolutionary algorithm (MOEA) is used to clustering activities where the metrics are considered as objectives should be optimized. The MOEA produces a set of optimal solutions as proper identified services of a service design. Finally, we used three case studies to show the effectiveness of the proposed method and then evaluated the results.

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“…Sarro et al [89] used NSGA-II with CoGEE since it is a widely used Multi-objective Evolutionary Algorithm (MOEA) [26], [89]. However, there are many variants of MOEAs used in search-based software engineering [84], each designed to improve a different aspect of the Pareto Front quality. Therefore, we are interested to know if CoGEE achieves different results with other variants of MOEAs:…”

Section: Research Questionsmentioning

confidence: 99%

Multi-Objective Software Effort Estimation: A Replication Study

Tawosi

Sarro

Petrozziello

et al. 2022

IIEEE Trans. Software Eng.

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Replication studies increase our confidence in previous results when the findings are similar each time, and help mature our knowledge by addressing both internal and external validity aspects. However, these studies are still rare in certain software engineering fields. In this paper, we replicate and extend a previous study, which denotes the current state-of-the-art for multi-objective software effort estimation, namely CoGEE. We investigate the original research questions with an independent implementation and the inclusion of a more robust baseline (LP4EE), carried out by the first author, who was not involved in the original study. Through this replication, we strengthen both the internal and external validity of the original study. We also answer two new research questions investigating the effectiveness of CoGEE by using four additional evolutionary algorithms (i.e., IBEA, MOCell, NSGA-III, SPEA2) and a well-known Java framework for evolutionary computation, namely JMetal (rather than the previously used R software), which allows us to strengthen the external validity of the original study. The results of our replication confirm that: (1) CoGEE outperforms both baseline and state-of-the-art benchmarks statistically significantly (p < 0.001); (2) CoGEE's multi-objective nature makes it able to reach such a good performance; (3) CoGEE's estimation errors lie within claimed industrial human-expert-based thresholds. Moreover, our new results show that the effectiveness of CoGEE is generally not limited to nor dependent on the choice of the multi-objective algorithm. Using CoGEE with either NSGA-II, NSGA-III, or MOCell produces human competitive results in less than a minute. The Java version of CoGEE has decreased the running time by over 99.8% with respect to its R counterpart. We have made publicly available the Java code of CoGEE to ease its adoption, as well as, the data used in this study in order to allow for future replication and extension of our work.

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

Cited by 77 publications

References 68 publications

How to Evaluate Solutions in Pareto-Based Search-Based Software Engineering: A Critical Review and Methodological Guidance

How to Evaluate Solutions in Pareto-Based Search-Based Software Engineering: A Critical Review and Methodological Guidance

A model driven and clustering method for service identification directed by metrics

Multi-Objective Software Effort Estimation: A Replication Study

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