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    Accepted author manuscript, 378 KB, PDF-document

DOI

Latent Dirichlet Allocation (LDA) has been used to support many software engineering tasks. Previous studies showed that default settings lead to sub-optimal topic modeling with a dramatic impact on the performance of such approaches in terms of precision and recall. For this reason, researchers used search algorithms (e.g., genetic algorithms) to automatically configure topic models in an unsupervised fashion. While previous work showed the ability of individual search algorithms in finding near-optimal configurations, it is not clear to what extent the choice of the meta-heuristic matters for SE tasks. In this paper, we present a systematic comparison of five different meta-heuristics to configure LDA in the context of duplicate bug reports identification. The results show that (1) no master algorithm outperforms the others for all software projects, (2) random search and PSO are the least effective meta-heuristics. Finally, the running time strongly depends on the computational complexity of LDA while the internal complexity of the search algorithms plays a negligible role.

Original languageEnglish
Title of host publicationSearch-Based Software Engineering - 11th International Symposium, SSBSE 2019, Proceedings
EditorsShiva Nejati, Gregory Gay
PublisherSpringer Verlag
Pages11-26
Number of pages16
ISBN (Print)9783030274542
DOIs
Publication statusPublished - 1 Jan 2019
Event11th International Symposium on Search-Based Software Engineering, SSBSE 2019 - Tallinn, Estonia
Duration: 31 Aug 20191 Sep 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11664 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Symposium on Search-Based Software Engineering, SSBSE 2019
CountryEstonia
CityTallinn
Period31/08/191/09/19

    Research areas

  • Duplicate Bug Report, Evolutionary Algorithms, Latent Dirichlet Allocation, Search-based Software Engineering, Topic modeling

ID: 62455533