Points-to analysis is a static program analysis that extracts reference information from programs, e.g., possible targets of a call and possible objects referenced by a field. Previous works evaluating different approaches to context-sensitive Pointsto analyses use coarse-grained precision metrics focusing on references between source code entities like methods and classes. Two typical examples of such metrics are the number of nodes and edges in a call-graph. These works indicate that context-sensitive analysis with a call-depth k = 1 only provides slightly better precision than contextinsensitive analysis. Moreover, these works could not find a substantial precision improvement when using the more expensive analyses with call-depth k < 1. The hypothesis in the present paper is that substantial differences between the contextsensitive approaches show if (and only if) the precision is measured by more fine-grained metrics focusing on individual objects (rather than methods and classes) and references between them. These metrics are justified by the many applications requiring such detailed object reference information. In order to experimentally validate our hypothesis we make a systematic comparison of ten different variants of context-sensitive Points-to analysis using different call-depths k

static program analysis, points-to analysis, context sensitivity