Automated Program Recognition by Graph Parsing

Abstract

JAN 22 1993_ 93-01046 93 J21 034- REPORT DOCUMENTATION PAGEForM N=o07-1 01MB NO 0704-0188 Pub•ic reporting burden for this collection of information is estimated to averaqe I hour oer response. including the time for revewing Instructions. searchrg e.-st'ng sata sour'., gathering and maintaining the data needed, and completing and reviewing the collection of information Send comments retarding this burden estimate or any other asoect Of this ol lection of information, including suggestions for reducing this burden, We demonstrate that this graph parsing approach is a feasible and useful way to automate program recognition. In studying this approach, we have experimented with two medium-sized, real-world simulator programs. There are three aspects of our study. First, we evaluate our representation's ability to suppress many common forms of program variation which hinder recognition. Second, we investigate the expressiveness of our graph grammar formalism for capturing programming cliches. Third, we empirically and analytically study the computational cost of our recognition approach with respect to the real-world simulator programs. The recognition of standard computational structures (cliches) in a program can help an experienced programmer understand the program. Based on the known relationships between the cliches, a hierarchical description of the program's design can be recovered. We develop and study a graph parsing approach to automating program recognition in which programs are represented as attributed dataflow graphs and a library of cliches is encoded as an attributed graph grammar. Graph parsing is used to recognize clich6s in the code. We demonstrate that this graph parsing approach is a feasible and useful way to automate program recognition. In studying this approach, we have experimented with two medium-sized, real-world simulator programs. There are three aspects of our study. First, we evaluate our representation's ability to suppress many common forms of program variation which hinder recognition. Second, we investigate the expressiveness of our graph grammar formalism for capturing programming clich6s. Third, we empirically and analytically study the computational cost of our recognition approach with respect to the real-world simulator programs.

New Frontiers of Reverse Engineering

“…In his thesis, Koschke [67] proposed an approach to combine the different techniques by using a union of fuzzy sets, to benefit from each one, and combined these results with information obtained from system experts/developers. As discussed in Linda Wills' PhD thesis [114] and in many following papers, cliché matching is an effective strategy for architecture recovery. Fiutem et al [47] proposed an approach to detect architectural connectors -such as pipes, shared memory, remote procedure call, socket, etc.…”

Understanding interleaved code

“…Note that a plan is not necessarily stereotypical or used repeatedly; it may be novel or idiosyncratic. Following Waters, 1990, Selfridge et al, 1993) , we reserve the term cliché for a plan that represents a standard, stereotypical form, which can be detected by recognition techniques, such as (Hartman, 1991, Letovsky, 1988, Kozaczynski and Ning, 1994, Quilici, 1994, Rich and Wills, 1990, Wills, 1992. .…”

“…When what is interleaved is familiar (i.e., stereotypical, frequently used plans), cliché recognition (e.g., (Hartman, 1991, Johnson, 1986, Kozaczynski and Ning, 1994, Letovsky, 1988, Quilici, 1994, Rich and Wills, 1990, Wills, 1992 ) is a useful detection mechanism. In fact, most recognition systems deal explicitly with the recognition of clichés that are interleaved in specific ways with unrecognizable code or other clichés.…”

“…In fact, most recognition systems deal explicitly with the recognition of clichés that are interleaved in specific ways with unrecognizable code or other clichés. One of the key features of GRASPR (Wills, 1992), for instance, is its ability to deal with delocalization and redistribution-type function sharing optimizations.…”

“…The recognition and temporal abstraction of iteration clichés is similarly used in GRASPR to enable it to deal with generalized loop fusion forms of interleaving. Loop fusion is viewed as redistribution of sequences of values and treated as any other redistribution optimization (Wills, 1992).…”