The paper reports on a method for software risk assessments that take into account "primary facts" and "secondary facts". Primary facts are those obtained through automatically analyzing the source code of a system, and secondary facts are those facts obtained from people working with or on the system, and available documentation. We describe how both types of facts are retrieved, and how we are bridging the interpretation gap from the raw facts (either primary or secondary) to a concise risk assessment, which includes recommendations to minimize the risk. This method has been developed while performing numerous risk assessments, and is continuously being fine-tuned.

We want to use the advanced language processing technology available in the ASF+SDF in combination with general purpose programming languages. In particular, we want to combine the syntax definition formalism (SDF) and the associated components that support generalized LR parsing, with the object-oriented language Java. To this end, we implemented JJForester, a tool that generates class structures from SDF grammar definitions. The generated class structures implement a number of design patterns to facilitate construction and traversal of parse trees represented by object structures. In a detailed case study, we demonstrate how program analyses and transformations for languages of non-trivial size can be constructed with JJForester.

Many approaches to support (semi-automatic) identification of objects in legacy code take the data structures as 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 100,000 LOC Cobol system.Furthermore, we use these results to contrast clustering with concept analysis techniques.

In order to maintain the consistency between sources and documentation, while at the same time providing documentation at the design level, it is necessary to generate documentation from sources in such a way that it can be integrated with hand-written documentation. In order to simplify the construction of documentation generators, we introduce island grammars, which only define those syntactic structures needed for (re)documentation purposes. We explain how they can be used to obtain various forms of documentation, such as data dependency diagrams for mainframe batch jobs. Moreover, we discuss how the derived information can be made available via a hypertext structure. We conclude with an industrial case study in which a 600,000 LOC COBOL legacy system is redocumented using the techniques presented in the paper.