(Georgia Institute of Technology, 2002)
Orso, Alessandro; Liang, Donglin; Sinha, Saurabh; Harrold, Mary Jean
Identifying and understanding data dependences is important for a variety of
software-engineering tasks. The presence of pointers, arrays, and dynamic
memory allocation introduces subtle and complex data dependences that may be
difficult to understand. In this paper, we present a refinement of our
previously developed classification that also distinguishes the types of
memory locations, considers interprocedural data dependences, and further
distinguishes such data dependences based on the kinds of interprocedura
paths on which they occur. This new classification enables reasoning about
the complexity of data dependences in programs using features such as pointers, arrays, and dynamic memory
allocation. We present an algorithm for computing interprocedural data
dependences according to our classification. To evaluate the classification,
we compute the distribution of data dependences for a set of real C programs
and we discuss how the distribution can be useful in understanding the
characteristics of a program. We also evaluate how alias information
provided by different algorithms, varying in precision, affects the
distribution. Finally, we investigate how the classification can be exploited
to estimate complexity of the data dependences in a program.
(Georgia Institute of Technology, 2002)
Orso, Alessandro; Liang, Donglin; Harrold, Mary Jean; Lipton, Richard J.
In this paper, we present the Gamma system---a new approach for
continuous improvement of software systems after their deployment. The
Gamma system facilitates remote monitoring of deployed software
using a revolutionary approach that exploits the opportunities presented by a
software product being used by many users connected through a network.
Gamma splits monitoring tasks across different instances of the software,
so that partial information can be collected from different users by
means of light-weight instrumentation, and integrated to gather the
overall monitoring information. This system enables software producers
(1) to perform continuous, minimally intrusive analyses of their
software's behavior, and (2) to use the information thus gathered to
improve and evolve their software. We describe the Gamma system and its
underlying technology in detail, and illustrate the different components of
the system. We also present a prototype implementation of the system and
show our initial experiences with it.