Log Summarisation for Defect Evolution Analysis

TL;DR

This work suggests an online semantic-based clustering approach to error logs that dynamically updates the log clusters to enable monitoring code error life-cycles and introduces a novel metric to evaluate the performance of temporal log clusters.

Abstract

Log analysis and monitoring are essential aspects in software maintenance and identifying defects. In particular, the temporal nature and vast size of log data leads to an interesting and important research question: How can logs be summarised and monitored over time? While this has been a fundamental topic of research in the software engineering community, work has typically focused on heuristic-, syntax-, or static-based methods. In this work, we suggest an online semantic-based clustering approach to error logs that dynamically updates the log clusters to enable monitoring code error life-cycles. We also introduce a novel metric to evaluate the performance of temporal log clusters. We test our system and evaluation metric with an industrial dataset and find that our solution outperforms similar systems. We hope that our work encourages further temporal exploration in defect datasets.

Figures (1)

• Figure 1: Example of Error Log Evolution. The top row provides a visual aid of the clusters while the bottom row details the specific defects found at each point in time.