Publikation - Einzelansicht
Details zur Publikation
|Autorinnen und Autoren||Lea Kristin Gerling und Klaus Schmid|
|Titel||Variability-Aware Semantic Slicing Using Code Property Graphs|
|Publikationsart||Beitrag zu einer Tagung / Konferenz|
|Herausgebende Einrichtung / Verlag||ACM|
|Titel des Buches||Proceedings of the 23rd International Systems and Software Product Line Conference (SPLC'19)|
Acommonproblem in program analysis is to identify semantically related statements in programs, for example, which statements change the value of a variable, or implement a specific feature or functionality. This is a very challenging task for large programs and gets even more complicated in the presence of variability implementations like #ifdef-annotations. Program slicing is a technique that can be used to aid developers with this challenge. But while slicing is a well-established technique for individual programs, there has been so far only little work on program slicing of product lines.
Here,we introduce a static-analysis approach for semantic slicing of product lines. Our approach introduces the novel concept of a variability-aware code property graph, which combines information about code properties (like statement type) and syntactical structure with data- and control-flow information. This graph is then traversed to gather semantically-related lines of code for a given entry node.We demonstrate our approach with a C-example, including preprocessor statements.