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Introduce the symbol clustering algorithm and improvements to CodeCompass. This change makes CodeCompass use the linkage information available in the
build.json(if present), to store linkage data to certain translation units in a quick and optimalised manner.This data is used when the user is searching between symbols (such as Jump to Definition or List Callees queries) to generate the list of symbols that are statically linked to the "search origin" (e.g. the
f()call site). These results are highlighted (while those that are not statically link are highlighted in another way).With this change, if a project contains ambiguous mangled names, the user can easier select a definition that is supposedly (!) more relevant to the point where he/she is searching from, than other definitions matching based on the function signature.
This change produces minimal overhead in the database and has a minimal impact on parse and navigation time. The algorithm and the results has been discussed in detail on the 33rd Conference of Scientific Students' Associations (XXXIII. OTDK), and on the 17th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM-2017).