Combining concern input with program analysis for bloat detection

Abstract

Framework based software tends to get bloated by accumulating optional features (or concerns) just-in-case they are needed. The good news is that such feature bloat need not always cause runtime execution bloat. The bad news is that often enough, only a few statements from an optional concern may cause execution bloat that may result in as much as 50% runtime overhead. We present a novel technique to analyze the connection between optional concerns and the potential sources of execution bloat induced by them. Our analysis automatically answers questions such as (1) whether a given set of optional concerns could lead to execution bloat and (2) which particular statements are the likely sources of bloat when those concerns are not required. The technique combines coarse grain concern input from an external source with a fine-grained static analysis. Our experimental evaluation highlights the effectiveness of such concern augmented program analysis in execution bloat assessment of ten programs. Copyright © 2013 ACM.