King's College London

Research portal

AspectJ Code Analysis and Verification with GASR

Research output: Contribution to journalArticle

Johan Fabry, Coen De Roover, Carlos Noguera, Steffen Zschaler, Awais Rashid, Viviane Jonckers

Original languageEnglish
Pages (from-to)528-544
JournalJournal of Systems and Software
Volume117
Early online date16 Apr 2016
DOIs
Accepted/In press14 Apr 2016
E-pub ahead of print16 Apr 2016
PublishedJul 2016

Documents

  • 1-s2.0-S0164121216300279-main

    1_s2.0_S0164121216300279_main.pdf, 811 KB, application/pdf

    Uploaded date:18 Apr 2016

    Version:Accepted author manuscript

    Licence:CC BY-NC-ND

King's Authors

Abstract

Aspect-oriented programming languages extend existing languages with new features for supporting modularization of crosscutting concerns. These features however make existing source code analysis tools unable to reason over this code. Consequently, all code analysis efforts of aspect-oriented code that we are aware of have either built limited analysis tools or were performed manually. Given the significant complexity of building them or manual analysis, a lot of duplication of effort could have been avoided by using a general-purpose tool. To address this, in this paper we present Gasr: a source code analysis tool that reasons over AspectJ source code, which may contain metadata in the form of annotations. Gasr provides multiple kinds of analyses that are general enough such that they are reusable, tailorable and can reason over annotations. We demonstrate the use of Gasr in two ways: we first automate the recognition of previously identified aspectual source code assumptions. Second, we turn implicit assumptions into explicit assumptions through annotations and automate their verification. In both uses Gasr performs detection and verification of aspect assumptions on two well-known case studies that were manually investigated in earlier work. Gasr finds already known aspect assumptions and adds instances that had been previously overlooked.

Download statistics

No data available

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454