AN EVOLUTION-BASED APPROACH TO PROGRAM UNDERSTANDING USING CULTURAL ALGORITHMS

Program understanding plays a very important role in the software engineering field as an essential part of the widely accepted reuse process. Unfortunately, the lack of general tools for this purpose often prevents users from effectively retrieving software modules for reuse. The primary difficulty is in determining which of the already-available modules performs the desired function. This paper explores the task of extracting functional knowledge from a program object (i.e., coded module) using an evolutionary-learning technique, cultural algorithms, to automatically learn classification rules for a reuse library. The modules are stored in a reuse library in the PM system using a faceted classification scheme. Therefore, the goal is to learn a set of rules to characterize the semantics of code modules based on their syntactic structure. The most difficult part of the process is to identify those parts of the program that provide specific evidence of the presence of a concept. Here, evolutionary-learning techniques are employed to interactively identify lines in a module that are "believed" to provide evidence for the presence of a facet value (concept) in the code by a user. A prototype is used to learn concepts relating to the "list" and "stack" programming concepts.