Describes a general technique for identifying modules in legacy code. The method is based on concept analysis - a branch of lattice theory that can be used to identify similarities among a set of objects based on their attributes. We discuss how concept analysis can identify potential modules using both "positive" and "negative" information. We present an algorithmic framework to construct a lattice of concepts from a program, where each concept represents a potential module. We define the notion of a concept partition, present an algorithm for discovering all concept partitions of a given concept lattice, and prove the algorithm to be correct.

Describes a general technique for identifying modules in legacy code. The method is based on concept analysis-a branch of lattice theory that can be used to identify similarities among a set of objects based on their attributes. We discuss how concept analysis can identify potential modules using both “positive” and “negative” information. We present an algorithmic framework to construct a lattice of concepts from a program, where each concept represents a potential module

We consider the problem of software generalization: Given a program component C, create a parameterized program component C′ such that C′ is usable in a wider variety of syntactic contexts than C. Furthermore, C′ should be a semantically meaningful generalization of C; namely, there must exist an instantiation of C′ that is equivalent in functionality to C.In this paper, we present an algorithm that generalizes C functions via type inference. The original functions operate on specific data types; the result of generalization is a collection of C++ function templates that operate on parameterized types. This version of the generalization problem is useful in the context of converting existing C programs to C++.