Two factors limit the utility of reverse engineering technology for many distributed software systems. First, with the exception of tools that support Ada and its explicit tasking constructs, reverse engineering tools fail to capture information concerning the flow of information between tasks. Second, relatively few reverse engineering tools are available for programming languages in which many older legacy applications were written (e.g., Jovial, CMS-2, and various assembly languages). We describe approaches that were developed for overcoming these limitations. In particular, we have implemented an approach for automatically extracting task flow information from a command and control system written in CMS-2. Our approach takes advantage of a small amount of externally provided design knowledge in order to recover design information relevant to the distributed nature of the target system.

Recovery of higher level "design" information and the ability to create dynamic, task adaptable software documentation is crucial to supporting a number of program understanding activities.This paper presents research that demonstrates that reverse engineering technology can be used to recover software architecture representations of source code.We have developed a framework that integrates reverse engineering technology and architectural style representations.Using the framework, analysts can recover custom, dynamic documentation to fit a variety of software analysis requirements.Our goal is to establish coherent abstractions appropriate for helping analysts to understand large software systems.We discuss a code coverage metric useful for assessing the degree of program understanding achieved.

An automated tool called the Requirements Apprentice (RA) which assists a human analyst in the creation and modification of software requirements is presented. Unlike most other requirements analysis tools, which start from a formal description language, the focus of the RA is on the transition between informal and formal specifications. The RA supports the earliest phases of creating a requirement, in which ambiguity, contradiction, and incompleteness are inevitable. From an artificial intelligence perspective, the central problem the RA faces is one of knowledge acquisition. The RA develops a coherent internal representation of a requirement from an initial set of disorganized imprecise statements. To do so, the RA relies on a variety of techniques, including dependency-directed reasoning, hybrid knowledge representations and the reuse of common forms (cliches). An annotated transcript showing an interaction with a working version of the RA is given.>