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Nebro, Antonio J.; Durillo, Juan J.; Luna, Francisco; Dorronsoro, Bernabè; Alba, Enrique

doi:10.1002/int.v24:7

Cited by 30 publications

(1 citation statement)

References 97 publications

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“…We selected the six commonly used MOSAs in SBSE: NSGA-II [11], MoCell [29], SPEA2 [48], PAES [20], SMPSO [28], and CellDE [13]. CellDE was not applicable in the rule mining problem since it requires Integer-type solution encoding, whereas CellDE applies to Real/RealArray type solutions [12].…”

Section: Mosas and Quality Indicatorsmentioning

confidence: 99%

Quality Indicators in Search-based Software Engineering

Ali

Arcaini

Pradhan

et al. 2020

ACM Trans. Softw. Eng. Methodol.

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Search-Based Software Engineering (SBSE) researchers who apply multi-objective search algorithms (MOSAs) often assess the quality of solutions produced by MOSAs with one or more quality indicators (QIs). However, SBSE lacks evidence providing insights on commonly used QIs, especially about agreements among them and their relations with SBSE problems and applied MOSAs. Such evidence about QIs agreements is essential to understand relationships among QIs, identify redundant QIs, and consequently devise guidelines for SBSE researchers to select appropriate QIs for their specific contexts. To this end, we conducted an extensive empirical evaluation to provide insights on commonly used QIs in the context of SBSE, by studying agreements among QIs with and without considering differences of SBSE problems and MOSAs. In addition, by defining a systematic process based on three common ways of comparing MOSAs in SBSE, we present additional observations that were automatically produced based on the results of our empirical evaluation. These observations can be used by SBSE researchers to gain a better understanding of the commonly used QIs in SBSE, in particular, regarding their agreements. Finally, based on the results, we also provide a set of guidelines for SBSE researchers to select appropriate QIs for their particular context.

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