Finding objects in procedural programs: an alternative approach
- 19 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 208-216
- https://doi.org/10.1109/wcre.1995.514709
Abstract
Effective software maintenance requires a detailed knowledge of the system's artifacts, the way these artifacts are used or modified and their interrelationships. Based on some useful characteristics of the object-oriented paradigm the identification of objects within procedural programs has become a promising approach to reduce the effort in program understanding and, hence, the maintenance cost. In this paper we present a new approach to object identification in procedural programs that not only relies on information exclusively extractable from source code but integrates human expertise and external domain- and application-specific knowledge.Keywords
This publication has 17 references indexed in Scilit:
- On the use of cluster analysis for assisting maintenance of large software systemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Migration of procedurally oriented COBOL programs in an object-oriented architecturePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Identifying objects in a conventional procedural language: an example of data design recoveryPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- An object finder for program structure understanding in software maintenanceJournal of Software Maintenance: Research and Practice, 1994
- A new approach to finding objects in programsJournal of Software Maintenance: Research and Practice, 1994
- Reverse-engineering cobol via formal methodsJournal of Software Maintenance: Research and Practice, 1993
- Software reuseACM Computing Surveys, 1992
- Reengineering of old systems to an object-oriented architecturePublished by Association for Computing Machinery (ACM) ,1991
- Design recovery for maintenance and reuseComputer, 1989
- System Structure Analysis: Clustering with Data BindingsIEEE Transactions on Software Engineering, 1985