49,573 research outputs found
Feature-Aware Verification
A software product line is a set of software products that are distinguished
in terms of features (i.e., end-user--visible units of behavior). Feature
interactions ---situations in which the combination of features leads to
emergent and possibly critical behavior--- are a major source of failures in
software product lines. We explore how feature-aware verification can improve
the automatic detection of feature interactions in software product lines.
Feature-aware verification uses product-line verification techniques and
supports the specification of feature properties along with the features in
separate and composable units. It integrates the technique of variability
encoding to verify a product line without generating and checking a possibly
exponential number of feature combinations. We developed the tool suite
SPLverifier for feature-aware verification, which is based on standard
model-checking technology. We applied it to an e-mail system that incorporates
domain knowledge of AT&T. We found that feature interactions can be detected
automatically based on specifications that have only feature-local knowledge,
and that variability encoding significantly improves the verification
performance when proving the absence of interactions.Comment: 12 pages, 9 figures, 1 tabl
Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search
This work proposes a process for efficiently searching over combinations of
individual object 6D pose hypotheses in cluttered scenes, especially in cases
involving occlusions and objects resting on each other. The initial set of
candidate object poses is generated from state-of-the-art object detection and
global point cloud registration techniques. The best-scored pose per object by
using these techniques may not be accurate due to overlaps and occlusions.
Nevertheless, experimental indications provided in this work show that object
poses with lower ranks may be closer to the real poses than ones with high
ranks according to registration techniques. This motivates a global
optimization process for improving these poses by taking into account
scene-level physical interactions between objects. It also implies that the
Cartesian product of candidate poses for interacting objects must be searched
so as to identify the best scene-level hypothesis. To perform the search
efficiently, the candidate poses for each object are clustered so as to reduce
their number but still keep a sufficient diversity. Then, searching over the
combinations of candidate object poses is performed through a Monte Carlo Tree
Search (MCTS) process that uses the similarity between the observed depth image
of the scene and a rendering of the scene given the hypothesized pose as a
score that guides the search procedure. MCTS handles in a principled way the
tradeoff between fine-tuning the most promising poses and exploring new ones,
by using the Upper Confidence Bound (UCB) technique. Experimental results
indicate that this process is able to quickly identify in cluttered scenes
physically-consistent object poses that are significantly closer to ground
truth compared to poses found by point cloud registration methods.Comment: 8 pages, 4 figure
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