2022
DOI: 10.1109/tse.2020.3036108
|View full text |Cite
|
Sign up to set email alerts
|

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

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

1
35
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 45 publications
(36 citation statements)
references
References 146 publications
(328 reference statements)
1
35
0
Order By: Relevance
“…In this paper, we replicate and extend their work [89] by further assessing CoGEE's effectiveness using the most recent best practice in search-based software engineering [6], [45], [67], [83] and predictive models for software engineering [88], [97], [109].…”
Section: Introductionmentioning
confidence: 85%
See 4 more Smart Citations
“…In this paper, we replicate and extend their work [89] by further assessing CoGEE's effectiveness using the most recent best practice in search-based software engineering [6], [45], [67], [83] and predictive models for software engineering [88], [97], [109].…”
Section: Introductionmentioning
confidence: 85%
“…On the other hand, the Pareto Front's quality indicators allow us to quantify the overall quality of prediction models by measuring the trade-off between multiple competing objective values (in our case, SAE and CI). These indicators are well-known in the multi-objective optimisation literature [14], [19], [113] and have been extensively used in pre-vious software engineering work to evaluate and compare multi-and single-objective algorithms performance as an alternative to using the average values (see e.g., [35], [41], [42], [43], [67], [85]).…”
Section: Validation and Evaluation Criteriamentioning
confidence: 99%
See 3 more Smart Citations