Baital is a sample generator for configurable systems that generates a set of testing samples for large configurable systems with high t-wise coverage. This release includes i) Scalable estimation of t-wise coverage ii) New scalable sampling strategie
International audienceCharacterizing performance is essential to optimize programs and architectures...
INST: L_042In data streaming we work with large data from multiple sources. We observe overloaded pa...
In the context of adaptive Monte Carlo algorithms, we cannot directly generate independent samples f...
The diversity of software application scenarios has led the evolution towards highly configurable sy...
The rise of highly configurable complex software and its widespread usage requires design of efficie...
This release features updated code for model parameter calculation, sensitivity analysis, and contam...
Behavior-oriented Adaptation in Testing (BAiT) is a toolset, which supports test generation and exec...
Objective: Understand impacts of adaptive sampling and get to know more about pbrt Developing enviro...
easily implementable sampling procedure for certain fractal and other non-band limited signal
The concept of sampling is central to the operation of all systems in which analog information is t...
The networks were used as input for fast greedy modularity optimization and Louvain community detect...
SamplingCA: Effective and Efficient Sampling-based Pairwise Testing for Highly Configurable Software...
All in-text references underlined in blue are linked to publications on ResearchGate, letting you ac...
The problem of uniform sampling is, given a formula F, sample solutions of F uniformly at random fro...
All in-text references underlined in blue are linked to publications on ResearchGate, letting you ac...
International audienceCharacterizing performance is essential to optimize programs and architectures...
INST: L_042In data streaming we work with large data from multiple sources. We observe overloaded pa...
In the context of adaptive Monte Carlo algorithms, we cannot directly generate independent samples f...
The diversity of software application scenarios has led the evolution towards highly configurable sy...
The rise of highly configurable complex software and its widespread usage requires design of efficie...
This release features updated code for model parameter calculation, sensitivity analysis, and contam...
Behavior-oriented Adaptation in Testing (BAiT) is a toolset, which supports test generation and exec...
Objective: Understand impacts of adaptive sampling and get to know more about pbrt Developing enviro...
easily implementable sampling procedure for certain fractal and other non-band limited signal
The concept of sampling is central to the operation of all systems in which analog information is t...
The networks were used as input for fast greedy modularity optimization and Louvain community detect...
SamplingCA: Effective and Efficient Sampling-based Pairwise Testing for Highly Configurable Software...
All in-text references underlined in blue are linked to publications on ResearchGate, letting you ac...
The problem of uniform sampling is, given a formula F, sample solutions of F uniformly at random fro...
All in-text references underlined in blue are linked to publications on ResearchGate, letting you ac...
International audienceCharacterizing performance is essential to optimize programs and architectures...
INST: L_042In data streaming we work with large data from multiple sources. We observe overloaded pa...
In the context of adaptive Monte Carlo algorithms, we cannot directly generate independent samples f...
DOI
Add to ORKG