Applying Deep Learning For Imitating Adaptive Agent Behavior in Statistical Software Testing
| dc.contributor.author | Reichstaller, André | |
| dc.contributor.author | Eberhardinger, Benedikt | |
| dc.contributor.author | Seebach, Hella | |
| dc.contributor.author | Knapp, Alexander | |
| dc.contributor.author | Reif, Wolfgang | |
| dc.date.accessioned | 2023-03-02T10:35:46Z | |
| dc.date.available | 2023-03-02T10:35:46Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Statistical test generation builds on profiles which describe the estimated conditions of the system under test’s environment. Such environmental profiles, however, do not directly provide us with inputs for testing particular system components, as those mostly depend on the output of others. We thus a additionally need to estimate this output if we want to maintain statistical accuracy. Instantiating this task for the isolated testing of self-organization mechanisms between adaptive agents, this paper investigates the application of deep learning techniques for imitating the agents’ output. The proposed technique is evaluated on a simulated self-organizing grid of power plants. | en |
| dc.identifier.pissn | 0720-8928 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/40525 | |
| dc.language.iso | en | |
| dc.publisher | Geselllschaft für Informatik e.V. | |
| dc.relation.ispartof | Softwaretechnik-Trends Band 38, Heft 1 | |
| dc.title | Applying Deep Learning For Imitating Adaptive Agent Behavior in Statistical Software Testing | en |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 60 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 57 | |
| gi.conference.sessiontitle | FG TAV: Bericht und Beiträge vom Treffen der GI-Fachgruppe Test, Analyse und Verifikation von Software (TAV 41), 9. - 10. November 2017, Ratingen |
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