1995IEEE SoftwareRequires access

Structural redocumentation: a case study

Kevin K Wong, S. R. Tilley, Hausi A. Müller, M.-A. Storey

Open publisher page 154 citations

Abstract

Most software documentation typically describes the program at the algorithm and data-structure level. For large legacy systems, understanding the system's architecture is more important. The authors propose a method of reverse engineering through redocumentation that promises to extend the useful life of large systems.>

About this research paper

What this paper is about

Most software documentation typically describes the program at the algorithm and data-structure level. For large legacy systems, understanding the system's architecture is more important. The authors propose a method of reverse engineering through redocumentation that promises to extend the useful life of large systems.>

Why it matters

OpenAlex reports 154 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Most software documentation typically describes the program at the algorithm and data-structure level. For large legacy systems, understanding the system's architecture is more important. The authors propose a method of reverse engineering through redocumentation that promises to extend the useful life of large systems.>

Key concepts: Documentation, Reverse engineering, Software engineering, Computer science, Software, Legacy system, Software system, Programming language

Related papers

Back to paper searchBrowse research topicsOriginal source
Structural redocumentation: a case study — Research Paper | ScholarLens