A Survey on Automated Log Analysis for Reliability EngineeringShilin He, Pinjia He, Zhuangbin Chen et al.
Logs are semi-structured text generated by logging statements in software source code. In recent decades, software logs have become imperative in the reliability assurance mechanism of many software systems because they are often the only data available that record software runtime information. As modern software is evolving into a large scale, the volume of logs has increased rapidly. To enable effective and efficient usage of modern software logs in reliability engineering, a number of studies have been conducted on automated log analysis. This survey presents a detailed overview of automated log analysis research, including how to automate and assist the writing of logging statements, how to compress logs, how to parse logs into structured event templates, and how to employ logs to detect anomalies, predict failures, and facilitate diagnosis. Additionally, we survey work that releases open-source toolkits and datasets. Based on the discussion of the recent advances, we present several promising future directions toward real-world and next-generation automated log analysis.
TOUR: Dynamic Topic and Sentiment Analysis of User Reviews for Assisting App ReleaseTianyi Yang, Cuiyun Gao, Jingya Zang et al.
App reviews deliver user opinions and emerging issues (e.g., new bugs) about the app releases. Due to the dynamic nature of app reviews, topics and sentiment of the reviews would change along with app release versions. Although several studies have focused on summarizing user opinions by analyzing user sentiment towards app features, no practical tool is released. The large quantity of reviews and noise words also necessitates an automated tool for monitoring user reviews. In this paper, we introduce TOUR for dynamic TOpic and sentiment analysis of User Reviews. TOUR is able to (i) detect and summarize emerging app issues over app versions, (ii) identify user sentiment towards app features, and (iii) prioritize important user reviews for facilitating developers' examination. The core techniques of TOUR include the online topic modeling approach and sentiment prediction strategy. TOUR provides entries for developers to customize the hyper-parameters and the results are presented in an interactive way. We evaluate TOUR by conducting a developer survey that involves 15 developers, and all of them confirm the practical usefulness of the recommended feature changes by TOUR.