Runtime Prediction of Failure Modes from System Error Logs

Shalan, Atef; Zulkernine, Mohammad

doi:10.1109/iceccs.2013.41

Cited by 4 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the prediction of the failure, this approach also forecasts the occurrence of the modes in the software at the runtime. Pitakrat [19] also presented an online failure prediction approach called Hora. Hora is an online failure prediction approach based on the components of large-scale systems.…”

Section: Related Workmentioning

confidence: 99%

Classification and Prediction of Software Incidents Using Machine Learning Techniques

Ali

Adeel

Johar

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

An incident, in the perception of information technology, is an event that is not part of a normal process and disrupts operational procedure. This research work particularly focuses on software failure incidents. In any operational environment, software failure can put the quality and performance of services at risk. Many efforts are made to overcome this incident of software failure and to restore normal service as soon as possible. The main contribution of this study is software failure incidents classification and prediction using machine learning. In this study, an active learning approach is used to selectively label those data which is considered to be more informative to build models. Firstly, the sample with the highest randomness (entropy) is selected for labeling. Secondly, to classify the labeled observation into either failure or no failure classes, a binary classifier is used that predicts the target class label as failure or not. For classification, Support Vector Machine is used as a main classifier to classify the data. We derived our prediction models from the failure log files collected from the ECLIPSE software repository.

show abstract

Section: Related Workmentioning

confidence: 99%

Classification and Prediction of Software Incidents Using Machine Learning Techniques

Ali

Adeel

Johar

et al. 2021

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“… Khatuya et al (2018) propose ADELE, a machine learning-based technique to predict functional and performance issues. Shalan & Zulkernine (2013) utilize system logs to predict failure occurrences by means of regression analysis and support vector machines. Fu et al (2012) also utilize system logs to predict failures by mining recurring event sequences that are correlated.…”

Section: Resultsmentioning

confidence: 99%

“… Russo, Succi & Pedrycz (2015) use defective and non defective sequences of events as features. Shalan & Zulkernine (2013) ’s paper, although not completely explicit about which features they used, mention CPU, memory utilization, read/write instructions, error counter, error messages, error types, and error state parameters as examples of features.…”

Section: Resultsmentioning

confidence: 99%

Log-based software monitoring: a systematic mapping study

Cândido

Aniche

Deursen

2021

PeerJ Computer Science

View full text Add to dashboard Cite

Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve system reliability. However, despite the rich ecosystem around industry-ready log solutions, monitoring complex systems and getting insights from log data remains a challenge. Researchers and practitioners have been actively working to address several challenges related to logs, e.g., how to effectively provide better tooling support for logging decisions to developers, how to effectively process and store log data, and how to extract insights from log data. A holistic view of the research effort on logging practices and automated log analysis is key to provide directions and disseminate the state-of-the-art for technology transfer. In this paper, we study 108 papers (72 research track papers, 24 journals, and 12 industry track papers) from different communities (e.g., machine learning, software engineering, and systems) and structure the research field in light of the life-cycle of log data. Our analysis shows that (1) logging is challenging not only in open-source projects but also in industry, (2) machine learning is a promising approach to enable a contextual analysis of source code for log recommendation but further investigation is required to assess the usability of those tools in practice, (3) few studies approached efficient persistence of log data, and (4) there are open opportunities to analyze application logs and to evaluate state-of-the-art log analysis techniques in a DevOps context.

show abstract