Determining the Limits of Automated Program Recognition

This working paper was submitted as a Ph.D. thesis proposal.Program recognition is a program understanding technique in which stereotypic computational structures are identified in a program. From this identification and the known relationships between the structures, a hierarchical description of the program's design is recovered. The feasibility of this technique for small programs has been shown by several researchers. However, it seems unlikely that the existing program recognition systems will scale up to realistic, full-sized programs without some guidance (e.g., from a person using the recognition system as an assistant). One reason is that there are limits to what can be recovered by a purely code-driven approach. Some of the information about the program that is useful to know for common software engineering tasks, particularly maintenance, is missing from the code. Another reason guidance must be provided is to reduce the cost of recognition. To determine what guidance is appropriate, therefore, we must know what information is recoverable from the code and where the complexity of program recognition lies. I propose to study the limits of program recognition, both empirically and analytically. First, I will build an experimental system that performs recognition on realistic programs on the order of thousands of lines. This will allow me to characterize the information that can be recovered by this code-driven technique. Second, I will formally analyze the complexity of the recognition process. This will help determine how guidance can be applied most profitably to improve the efficiency of program recognition.MIT Artificial Intelligence Laborator

Confluence up to garbage in graph transformation

The transformation of graphs and graph-like structures is ubiquitous in computer science. When a system is described by graph-transformation rules, it is often desirable that the rules are both terminating and confluent so that rule applications in an arbitrary order produce unique resulting graphs. However, there are application scenarios where the rules are not globally confluent but confluent on a subclass of graphs that are of interest. In other words, non-resolvable conflicts can only occur on graphs that are considered as "garbage". In this paper, we introduce the notion of confluence up to garbage and generalise Plump's critical pair lemma for double-pushout graph transformation, providing a sufficient condition for confluence up to garbage by non-garbage critical pair analysis. We apply our results in two case studies about efficient language recognition: we present backtracking-free graph reduction systems which recognise a class of flow diagrams and a class of labelled series-parallel graphs, respectively. Both systems are non-confluent but confluent up to garbage. We also give a critical pair condition for subcommutativity up to garbage which, together with closedness, implies confluence up to garbage even in non-terminating systems.Comment: 33 pages, 202