12,000

We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK

93%

93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > Towards anomaly comprehension
View graph of relations

« Back

Towards anomaly comprehension: using structural compression to navigate profiling call-trees

Research output: Contribution in Book/Report/ProceedingsConference contribution

Published

Publication date2010
Host publicationSOFTVIS '10 Proceedings of the 5th international symposium on Software visualization
Place of publicationNEW YORK
PublisherASSOC COMPUTING MACHINERY
Pages103-112
Number of pages10
ISBN (Print)978-1-4503-0494-8
Original languageEnglish

Conference

Conference5th ACM Symposium on Software Visualization
CitySalt Lake City
Period25/10/1026/10/10

Conference

Conference5th ACM Symposium on Software Visualization
CitySalt Lake City
Period25/10/1026/10/10

Abstract

Developers must often diagnose anomalies in programs they only have a partial knowledge of. As a result, they must simultaneously reverse engineer parts of the system they are unfamiliar with while interpreting dynamic observation data (performance profiling traces, error-propagation channels, memory leaks), a task particularly difficult. To support developers in this kind of comprehension task, filtering and aggregation have long been suggested as key enabling strategies. Unfortunately, traditional approaches typically only provide a uniform level of aggregation, thus limiting the ability of developers to construct context-dependent representations of a program's execution. In this paper, we propose a localised approach to navigate and analyse the CPU usage of little-known programs and libraries. Our method exploits the structural information present in profiling call trees to selectively raise or lower the local abstraction level of the performance data. We explain the formalism underpinning our approach, describe a prototype, and present a preliminary user study that shows our tool has the potential to complement more traditional navigation approaches.