Mining Association Rules from Code (MARC) to support legacy software management

Abstract

This paper presents a methodology for Mining Association Rules from Code (MARC), aiming at capturing program structure, facilitating system understanding and supporting software management. MARC groups program entities (paragraphs or statements) based on similarities, such as variable use, data types and procedure calls. It comprises three stages: code parsing/analysis, association rule mining and rule grouping. Code is parsed to populate a database with records and respective attributes. Association rules are then extracted from this database and subsequently processed to abstract programs into groups containing interrelated entities. Entities are then grouped together if their attributes participate to common rules. This abstraction is performed at the program level or even the paragraph level, in contrast to other approaches that work at the system level. Groups can then be visualised as collections of interrelated entities. The methodology was evaluated using real-life COBOL programs. Results showed that the methodology facilitates program comprehension by using source code only, where domain knowledge and documentation are either unavailable or unreliable.