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Detecting architectural integrity violation patterns using ..:

, In: Proceedings of the 35th Annual ACM Symposium on Applied Computing,

Zakurdaeva, Alla ; Weiss, Michael ; Muegge, Steven - p. 1480-1487 , 2020

Link: https://dl.acm.org/doi/10.1145/3341105.3374008

RT 
T1 <cTitle>Proceedings of the 35th Annual ACM Symposium on Applied Computing</cTitle>: T1 <Title>Detecting architectural integrity violation patterns using machine learning</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3374008&Exemplar=1&LAN=DE
				
A1 Zakurdaeva, Alla
A1 Weiss, Michael
A1 Muegge, Steven
PB ACM
YR 2020
K1 architectural flaws
K1 bug-proneness
K1 hotspot patterns
K1 machine learning
K1 software architecture
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Software and its engineering
K1 Software organization and properties
K1 Software system structures
K1 Software architectures
K1 Software creation and management
K1 Software post-development issues
K1 Software version control
K1 Software verification and validation
K1 Software defect analysis
K1 Software development process management
K1 Software development methods
K1 Design patterns
SP 1480
OP 1487
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3341105.3374008
DO https://dl.acm.org/doi/10.1145/3341105.3374008
SF ELIB - SuUB Bremen

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