ER '02 Proceedings of the 21st International Conference on Conceptual Modeling
Probabilistic fault diagnosis in communication systems through incremental hypothesis updating
Computer Networks: The International Journal of Computer and Telecommunications Networking
Real-world learning with Markov logic networks
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Failure Diagnosis Using Decision Trees
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Machine Learning
A Hybrid Rule-Based/Case-Based Reasoning Approach for Service Fault Diagnosis
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 02
Distributed Systems: Concepts and Design (4th Edition) (International Computer Science)
Distributed Systems: Concepts and Design (4th Edition) (International Computer Science)
Event Modeling and Recognition Using Markov Logic Networks
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Intrusion detection alarms reduction using root cause analysis and clustering
Computer Communications
Monitoring and diagnosing software requirements
Automated Software Engineering
Muse: Mapping Understanding and deSign by Example
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
From goals to high-variability software design
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Log filtering and interpretation for root cause analysis
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
A goal driven framework for software project data analytics
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
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Root cause analysis for software systems is a challenging diagnostic task, due to the complexity emanating from the interactions between system components and the sheer size of logged data. This diagnostic task is usually assisted by human experts who create mental models of the system-at-hand, in order to generate hypotheses and conduct the analysis. In this paper, we propose a root cause analysis framework based on requirement goal models. We consequently use these models to generate a Markov Logic Network that serves as a diagnostic knowledge repository. The network can be trained and used to provide inferences as to why and how a particular failure observation may be explained by collected logged data. The proposed framework improves over existing approaches by handling uncertainty in observations, using natively generated log data, and by providing ranked diagnoses. The framework is illustrated using a test environment based on commercial off-the-shelf software components.