A Survey of Program Slicing Techniques.
A Survey of Program Slicing Techniques.
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Detecting large-scale system problems by mining console logs
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
A few billion lines of code later: using static analysis to find bugs in the real world
Communications of the ACM
SherLog: error diagnosis by connecting clues from run-time logs
Proceedings of the fifteenth edition of ASPLOS on Architectural support for programming languages and operating systems
Precise analysis of string expressions
SAS'03 Proceedings of the 10th international conference on Static analysis
R2: an application-level kernel for record and replay
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Improving software diagnosability via log enhancement
Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems
MoonBox: debugging with online slicing and dryrun
Proceedings of the Asia-Pacific Workshop on Systems
MoonBox: debugging with online slicing and dryrun
APSys'12 Proceedings of the Third ACM SIGOPS Asia-Pacific conference on Systems
Structured and Interoperable Logging for the Cloud Computing Era: The Pitfalls and Benefits
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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Logs are valuable for failure diagnosis and software debugging in practice. However, due to the ad-hoc style of inserting logging statements, the quality of logs can hardly be guaranteed. In case of a system failure, the log file may contain a large number of irrelevant logs, while crucial clues to the root cause may still be missing. In this paper, we present an automated approach to log improvement based on the combination of information from program source code and textual logs. It selects the most relevant ones from an ocean of logs to help developers focus and reason along the causality chain, and generates additional informative logs to help developers discover the root causes of failures. We have conducted a preliminary case study using an implementation prototype to demonstrate the usefulness of our approach.