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: Siegmund:Norbert
Contributed to:
2015
2015
2014
2013
2013
2012201220112011200820152017201720182019Wrote 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.