7,369 research outputs found
Expression-based aliasing for OO-languages
Alias analysis has been an interesting research topic in verification and
optimization of programs. The undecidability of determining whether two
expressions in a program may reference to the same object is the main source of
the challenges raised in alias analysis. In this paper we propose an extension
of a previously introduced alias calculus based on program expressions, to the
setting of unbounded program executions s.a. infinite loops and recursive
calls. Moreover, we devise a corresponding executable specification in the
K-framework. An important property of our extension is that, in a
non-concurrent setting, the corresponding alias expressions can be
over-approximated in terms of a notion of regular expressions. This further
enables us to show that the associated K-machinery implements an algorithm that
always stops and provides a sound over-approximation of the "may aliasing"
information, where soundness stands for the lack of false negatives. As a case
study, we analyze the integration and further applications of the alias
calculus in SCOOP. The latter is an object-oriented programming model for
concurrency, recently formalized in Maude; K-definitions can be compiled into
Maude for execution
Precise Null Pointer Analysis Through Global Value Numbering
Precise analysis of pointer information plays an important role in many
static analysis techniques and tools today. The precision, however, must be
balanced against the scalability of the analysis. This paper focusses on
improving the precision of standard context and flow insensitive alias analysis
algorithms at a low scalability cost. In particular, we present a
semantics-preserving program transformation that drastically improves the
precision of existing analyses when deciding if a pointer can alias NULL. Our
program transformation is based on Global Value Numbering, a scheme inspired
from compiler optimizations literature. It allows even a flow-insensitive
analysis to make use of branch conditions such as checking if a pointer is NULL
and gain precision. We perform experiments on real-world code to measure the
overhead in performing the transformation and the improvement in the precision
of the analysis. We show that the precision improves from 86.56% to 98.05%,
while the overhead is insignificant.Comment: 17 pages, 1 section in Appendi
Active Learning of Points-To Specifications
When analyzing programs, large libraries pose significant challenges to
static points-to analysis. A popular solution is to have a human analyst
provide points-to specifications that summarize relevant behaviors of library
code, which can substantially improve precision and handle missing code such as
native code. We propose ATLAS, a tool that automatically infers points-to
specifications. ATLAS synthesizes unit tests that exercise the library code,
and then infers points-to specifications based on observations from these
executions. ATLAS automatically infers specifications for the Java standard
library, and produces better results for a client static information flow
analysis on a benchmark of 46 Android apps compared to using existing
handwritten specifications
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