Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamically discovering likely program invariants to support program evolution
Proceedings of the 21st international conference on Software engineering
The computer for the 21st century
ACM SIGMOBILE Mobile Computing and Communications Review - Special issue dedicated to Mark Weiser
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Testing Context-Sensitive Middleware-Based Software Applications
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Volume 01
Correlation exploitation in error ranking
Proceedings of the 12th ACM SIGSOFT twelfth international symposium on Foundations of software engineering
Toward an OSGi-Based Infrastructure for Context-Aware Applications
IEEE Pervasive Computing
Inconsistency detection and resolution for context-aware middleware support
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
EgoSpaces: Facilitating Rapid Development of Context-Aware Mobile Applications
IEEE Transactions on Software Engineering
Automated Generation of Context-Aware Tests
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Testing pervasive software in the presence of context inconsistency resolution services
Proceedings of the 30th international conference on Software engineering
DySy: dynamic symbolic execution for invariant inference
Proceedings of the 30th international conference on Software engineering
CARISMA: Context-Aware Reflective mIddleware System for Mobile Applications
IEEE Transactions on Software Engineering
Model-based fault detection in context-aware adaptive applications
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
A Model to Design and Verify Context-Aware Adaptive Service Composition
SCC '09 Proceedings of the 2009 IEEE International Conference on Services Computing
Partial constraint checking for context consistency in pervasive computing
ACM Transactions on Software Engineering and Methodology (TOSEM)
Z-ranking: using statistical analysis to counter the impact of static analysis approximations
SAS'03 Proceedings of the 10th international conference on Static analysis
Multi-layer faults in the architectures of mobile, context-aware adaptive applications
Journal of Systems and Software
Context-Aware Adaptive Applications: Fault Patterns and Their Automated Identification
IEEE Transactions on Software Engineering
Adam: Identifying defects in context-aware adaptation
Journal of Systems and Software
IDEA: improving dependability for self-adaptive applications
Proceedings of the 2013 Middleware Doctoral Symposium
Managing environment and adaptation risks for the internetware paradigm
Theories of Programming and Formal Methods
An approach to testing commercial embedded systems
Journal of Systems and Software
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Context-aware adaptive applications continually sense and adapt to their changing environments. A large body of such applications relies on user-configured adaptation rules to customize their behavior. We call them rule-based context-aware applications (or RBAs for short). Due to the complexity required for adequately modeling environmental dynamics, adaptation faults are common in these RBAs. One promising approach to detecting such faults is to build a state transition model for an RBA, and exhaustively explore the model's state space. However, it can suffer from numerous false positives. For example, 78.6% of 784 reported faults for one popular RBA - PhoneAdapter, turn out to be false in a real deployment. In this paper, we address this false positive problem by inferring a domain model and an environment model for an RBA. The two models capture the hidden features inside user-configured adaptation rules as well as the RBA's running environment. We formulate these features as deterministic constraints and probabilistic constraints to prune false positives and effectively prioritize remaining faults. Our experiments on two real RBAs report that this approach successfully removes 46.5% of false positives and ranks 86.2% of true positives to the top of the fault list.