DDMin versus QuickXplain - An Experimental Comparison of two Algorithms for Minimizing Collections

Oliver A. Tazl, Christopher Tafeit, Franz Wotawa, Alexander Felfernig

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

About two decades ago, two algorithms, i.e., DDMin and QuickXPlain, for minimizing collections, were independently proposed and gained attention in the two research areas of Software Engineering and Artificial Intelligence, respectively. Whereas DDMin was developed for reducing a given test case, QuickXPlain was intended to be used for obtaining minimal conflicts efficiently. In this paper, we compare the performance of both algorithms with respect to their capabilities of minimizing collections. We found out that one algorithm outperforms the other under given prerequisites and vice versa. These findings help to select the suitable algorithm for a given task.

Originalspracheenglisch
TitelSEKE 2022 - Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering
Herausgeber (Verlag)Knowledge Systems Institute Graduate School
Seiten481-486
Seitenumfang6
ISBN (elektronisch)1891706543, 9781891706547
DOIs
PublikationsstatusVeröffentlicht - 2022
Veranstaltung34th International Conference on Software Engineering and Knowledge Engineering: SEKE 2022 - Pittsburgh, USA / Vereinigte Staaten
Dauer: 1 Juli 202210 Juli 2022

Konferenz

Konferenz34th International Conference on Software Engineering and Knowledge Engineering
KurztitelSEKE 2022
Land/GebietUSA / Vereinigte Staaten
OrtPittsburgh
Zeitraum1/07/2210/07/22

ASJC Scopus subject areas

  • Software

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