Managing Traceability Links with MaTraca

Lozano, Ángela; Noguera, Carlos; Jonckers, Viviane

doi:10.1109/saner.2016.16

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…The .QL language [49] is powering a commercial quality assessment tool suite that computes metrics and violations of coding rules [50]. Ekeko [31] powers academic prototypes for tracing [51] and automating [52,53] changes to source code, and has been used in a large corpus study about the maintenance of automated functional tests [54]. Soul [29] powers, among others, a tool for enforcing design regularities in code [55] or for performing annotation-aware refactorings [56].…”

Section: Discussionmentioning

confidence: 99%

AspectJ code analysis and verification with GASR

Fabry

Roover

Noguera

et al. 2016

Journal of Systems and Software

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• We argue for a general-purpose source code analysis tool that is aware of aspects.• We present the logic program querying tool GASR, the first such tool..• We discuss its implementation along with its library of logical predicates.• We show how it can automatically verify previously published aspectual assumptions. AbstractAspect-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.

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