Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
An empirical study of operating systems errors
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Simplifying and Isolating Failure-Inducing Input
IEEE Transactions on Software Engineering
Machine Learning
Design recovery of interactive graphical applications
Proceedings of the 25th International Conference on Software Engineering
Bug isolation via remote program sampling
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Automatic detection and repair of errors in data structures
OOPSLA '03 Proceedings of the 18th annual ACM SIGPLAN conference on Object-oriented programing, systems, languages, and applications
Anomalies as Precursors of Field Failures
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
Ingimp: introducing instrumentation to an end-user open source application
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An analysis method for improving a bug modification process in open source software development
Proceedings of the joint international and annual ERCIM workshops on Principles of software evolution (IWPSE) and software evolution (Evol) workshops
MACs: Mining API code snippets for code reuse
Expert Systems with Applications: An International Journal
Finding errors in multithreaded GUI applications
Proceedings of the 2012 International Symposium on Software Testing and Analysis
Automatically repairing broken workflows for evolving GUI applications
Proceedings of the 2013 International Symposium on Software Testing and Analysis
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In this paper, we propose a method to help users avoid bugs in GUI applications. In particular, users would use the application normally and report bugs that they encounter to prevent anyone -- including themselves -- from encountering those bugs again. When a user attempts an action that has led to problems in the past, he/she will receive a warning and will be given the opportunity to abort the action -- thus avoiding the bug altogether and keeping the application stable. Of course, bugs should be fixed eventually by the application developers, but our approach allows application users to collaboratively help each other avoid bugs -- thus making the application more usable in the meantime. We demonstrate this approach using our "Stabilizer" prototype. We also include a preliminary evaluation of the Stabilizer's bug prediction.