2007 OntologicalTextMiningOfSoftwareDocuments
- (Witte, Li et al., 2007) ⇒ René Witte, Qiangqiang Li, Yonggang Zhang, and Juergen Rilling. (2007). “Ontological Text Mining of Software Documents.” In: 12th International Conference on Applications of Natural Language to Information Systems (NLDB 2007).
Subject Headings: Software System Document, Ontological Text Mining.
Notes
- There is also a less referenced journal version of this paper.
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Quotes
Abstract
Documents written in natural languages constitute a major part of the software engineering lifecycle artifacts. Especially during software maintenance or reverse engineering, semantic information conveyed in these documents can provide important knowledge for the software engineer. In this paper, we present a[ text mining system]] capable of populating a software ontology with information detected in documents.
1 Introduction
With the ever increasing number of computers and their support for business processes, an estimated 250 billion lines of source code were being maintained in 2000, with that number rapidly increasing [1]. The relative cost of maintaining and managing the evolution of this large software base now represents more than 90% of the total cost [2] associated with a software product. One of the major challenges for software engineers while performing a maintenance task is the need to comprehend a multitude of often disconnected artifacts created originally as part of the software development process [3]. These artifacts include, among others, source code and corresponding software documents, e.g., requirements specifications, design description, and user’s guides. From a maintainer’s perspective, it becomes essential to establish and maintain the semantic connections among all these artifacts. Automated source code analysis, implemented in integrated development environments like Eclipse, has improved software maintenance significantly. However, integrating the often large amount of corresponding documentation requires new approaches to the analysis of natural language documents that go beyond simple full-text search or information retrieval (IR) techniques [4].
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