An approach to incremental and modular context-sensitive analysis

Abstract

The flexibility of dynamic languages often comes at the cost
of having to perform at run time a number of additional
tasks when compared to static languages. This includes for
example detecting property violations in calls to built-ins
and libraries (as well as in user code) or performing dynamic
specialization and other optimizations. Analyzing programs
statically can help reduce the amount and cost of these dynamic tasks. However, performing global analysis can in turn
be costly at compile time for large code bases, and this can
be a special burden if it is performed at each development
iteration. On the other hand, changes between development
iterations are often isolated within a few components, and
analysis cost can be reduced by reusing the results of previous analyses. This has been achieved to date through modular analysis, which reduces memory consumption and often
localizes the computation during reanalysis mainly to the
modules affected by changes, and through global incremental
fixpoint analysis techniques, that achieve cost reductions at
finer levels of granularity, such as changes in program lines,
but which are not directly applicable to modular programs.
This paper describes, implements, and evaluates a context-
sensitive fixpoint analysis algorithm aimed at achieving both
inter-modular (coarse-grain) and intra-modular (fine-grain)
incrementality, solving the problems related to propagation
of the fine-grain change information and effects across module boundaries, for additions and deletions in multiple modules. The implementation and evaluation of our algorithm
shows encouraging results: the expected advantages of fine-
grain incremental analysis carry over to the modular analysis
context. Furthermore, the fine-grained propagation of analysis information of our algorithm improves performance with
respect to traditional modular analysis even when analyzing
from scratch.