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Travelled to:
1 × Switzerland
2 × Germany
3 × Italy
4 × USA
Collaborated with:
S.Apel C.Kästner M.Rosenmüller G.Saake A.v.Rhein T.Thüm J.Feigenspan A.Grebhahn P.Jamshidi M.Velez J.Guo K.Czarnecki D.S.Batory J.Siegmund S.Sobernig S.Mühlbauer S.S.Kolesnikov R.Schröter M.Pukall S.Erdweg V.Nair T.Menzies J.Oh M.Myers A.Wasowski A.Sarkar D.Beyer T.Berger P.G.Giarrusso A.Wölfl H.Kosch J.Krautlager G.Weber-Urbina A.Patel Y.Agarwal
Talks about:
perform (8) configur (7) softwar (7) system (6) model (6) product (5) line (5) predict (4) featur (4) learn (4)

Person: Norbert Siegmund

DBLP DBLP: Siegmund:Norbert

Contributed to:

ESEC/FSE 20152015
ICSE 20152015
SPLC 20142014
ASE 20132013
GPCE 20132013
ICPC 20122012
ICSE 20122012
GPCE 20112011
SPLC 20112011
GPCE 20082008
ASE 20152015
ASE 20172017
ESEC/FSE 20172017
ESEC/FSE 20182018
ASE 20192019

Wrote 20 papers:

ESEC-FSE-2015-SiegmundGAK #configuration management #modelling
Performance-influence models for highly configurable systems (NS, AG, SA, CK), pp. 284–294.
ICSE-v1-2015-RheinGAS0B #configuration management
Presence-Condition Simplification in Highly Configurable Systems (AvR, AG, SA, NS, DB, TB), pp. 178–188.
ICSE-v1-2015-SiegmundSA #empirical #re-engineering
Views on Internal and External Validity in Empirical Software Engineering (JS, NS, SA), pp. 9–19.
SPLC-2014-SchroterSTS #interface #product line #programming
Feature-context interfaces: tailored programming interfaces for software product lines (RS, NS, TT, GS), pp. 102–111.
ASE-2013-GuoCASW #approach #learning #performance #predict #statistics #variability
Variability-aware performance prediction: A statistical learning approach (JG, KC, SA, NS, AW), pp. 301–311.
GPCE-2013-SiegmundRA #metric #performance
Family-based performance measurement (NS, AvR, SA), pp. 95–104.
ICPC-2012-FeigenspanS #comprehension
Supporting comprehension experiments with human subjects (JF, NS), pp. 244–246.
ICSE-2012-SiegmundKKABRS #automation #detection #performance #predict
Predicting performance via automated feature-interaction detection (NS, SSK, CK, SA, DSB, MR, GS), pp. 167–177.
GPCE-2011-RosenmullerSPA #product line
Tailoring dynamic software product lines (MR, NS, MP, SA), pp. 3–12.
SPLC-2011-SiegmundRKGAK #non-functional #predict #product line #scalability
Scalable Prediction of Non-functional Properties in Software Product Lines (NS, MR, CK, PGG, SA, SSK), pp. 160–169.
SPLC-2011-ThumKES #feature model #modelling
Abstract Features in Feature Modeling (TT, CK, SE, NS), pp. 191–200.
GPCE-2008-RosenmullerSSA #code generation #composition #product line
Code generation to support static and dynamic composition of software product lines (MR, NS, GS, SA), pp. 3–12.
ASE-2015-SarkarGSAC #configuration management #low cost #performance #predict
Cost-Efficient Sampling for Performance Prediction of Configurable Systems (T) (AS, JG, NS, SA, KC), pp. 342–352.
ASE-2015-WolflSAKKW #case study #experience #generative
Generating Qualifiable Avionics Software: An Experience Report (E) (AW, NS, SA, HK, JK, GWU), pp. 726–736.
ASE-2017-JamshidiSVKPA #analysis #configuration management #learning #modelling #performance
Transfer learning for performance modeling of configurable systems: an exploratory analysis (PJ, NS, MV, CK, AP, YA), pp. 497–508.
ESEC-FSE-2017-NairMSA #using
Using bad learners to find good configurations (VN, TM, NS, SA), pp. 257–267.
ESEC-FSE-2017-OhBMS #product line #random
Finding near-optimal configurations in product lines by random sampling (JO, DSB, MM, NS), pp. 61–71.
ESEC-FSE-2017-SiegmundSA #modelling #variability
Attributed variability models: outside the comfort zone (NS, SS, SA), pp. 268–278.
ESEC-FSE-2018-JamshidiVKS #configuration management #learning #modelling #performance
Learning to sample: exploiting similarities across environments to learn performance models for configurable systems (PJ, MV, CK, NS), pp. 71–82.
ASE-2019-MuhlbauerAS #evolution #modelling #performance
Accurate Modeling of Performance Histories for Evolving Software Systems (SM, SA, NS), pp. 640–652.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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