-
Scalable Inference of System-level Models from Component Logs
Behavioral software models play a key role in many software engineering ...
read it
-
Mining Periodic Patterns with a MDL Criterion
The quantity of event logs available is increasing rapidly, be they prod...
read it
-
A Directed Acyclic Graph Approach to Online Log Parsing
Logs are widely used in modern software system management because they a...
read it
-
ConfInLog: Leveraging Software Logs to Infer Configuration Constraints
Misconfigurations have become the dominant causes of software failures i...
read it
-
Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression
System logs record detailed runtime information of software systems and ...
read it
-
Length Matters: Clustering System Log Messages using Length of Words
The analysis techniques of system log messages (syslog messages) have a ...
read it
-
Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction
Goal: We consider the problem of automatically grouping logs of runs tha...
read it
Effective Removal of Operational Log Messages: an Application to Model Inference
Model inference aims to extract accurate models from the execution logs of software systems. However, in reality, logs may contain some "noise" that could deteriorate the performance of model inference. One form of noise can commonly be found in system logs that contain not only transactional messages—logging the functional behavior of the system—but also operational messages—recording the operational state of the system (e.g., a periodic heartbeat to keep track of the memory usage). In low-quality logs, transactional and operational messages are randomly interleaved, leading to the erroneous inclusion of operational behaviors into a system model, that ideally should only reflect the functional behavior of the system. It is therefore important to remove operational messages in the logs before inferring models. In this paper, we propose LogCleaner, a novel technique for removing operational logs messages. LogCleaner first performs a periodicity analysis to filter out periodic messages, and then it performs a dependency analysis to calculate the degree of dependency for all log messages and to remove operational messages based on their dependencies. The experimental results on two proprietary and 11 publicly available log datasets show that LogCleaner, on average, can accurately remove 98 operational messages and preserve 81 Furthermore, using logs pre-processed with LogCleaner decreases the execution time of model inference (with a speed-up ranging from 1.5 to 946.7 depending on the characteristics of the system) and significantly improves the accuracy of the inferred models, by increasing their ability to accept correct system behaviors (+43.8 pp on average, with pp=percentage points) and to reject incorrect system behaviors (+15.0 pp on average).
READ FULL TEXT
Comments
There are no comments yet.