Many approaches to support (semi-automatic) identification of objects in legacy code take data structures as the starting point for candidate classes. Unfortunately, legacy data structures tend to grow over time, and may contain many unrelated fields at the time of migration. We propose a method for identifying objects by semi-automatically restructuring the legacy data structures. Issues involved include the selection of record fields of interest, the identification of procedures actually dealing with such fields, and the construction of coherent groups of fields and procedures into candidate classes. We explore the use of cluster and concept analysis for the purpose of object identification, and we illustrate their effect on a 100000 LOC Cobol system. Furthermore, we use these results to contrast clustering with concept analysis techniques.
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机译：支持（半自动）遗留代码中对象的许多方法采用数据结构作为候选类的起点。不幸的是，传统数据结构往往会随着时间的推移而增长，并且在迁移时可能包含许多不相关的字段。我们提出了一种通过半自动重组传统数据结构来识别对象的方法。所涉及的问题包括选择感兴趣的记录领域，确定实际处理此类领域的程序，以及将相干领域的群体和程序建设到候选课程中。我们探讨了集群和概念分析的使用，以实现对象识别的目的，我们对100000 LOC COBOL系统的影响说明了它们的影响。此外，我们使用这些结果与概念分析技术对比聚类。
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