Abstract

From a maintenance perspective, only software that is well understood can evolve in a controlled and high-quality manner. Software evolution itself is a knowledge-driven process that requires the use and integration of different knowledge resources. The authors present a formal representation of an existing process model to support the evolution of software systems by representing knowledge resources and the process model using a common representation based on ontologies and description logics. This formal representation supports the use of reasoning services across different knowledge resources, allowing for the inference of explicit and implicit relations among them. Furthermore, an interactive story metaphor is introduced to guide maintainers during their software evolution activities and to model the interactions between the users, knowledge resources and process model.

Abstract

Traceability links provide support for software engineers in understanding relations and dependencies among software artefacts created during the software development process. The authors focus on re-establishing traceability links between existing source code and documentation to support software maintenance. They present a novel approach that addresses this issue by creating formal ontological representations for both documentation and source code artefacts. Their approach recovers traceability links at the semantic level, utilising structural and semantic information found in various software artefacts. These linked ontologies are supported by ontology reasoners to allow the inference of implicit relations among these software artefacts.

Abstract

In this paper, we present a formal process model to support the comprehension and maintenance of software systems. The model provides a formal ontological representation that supports the use of reasoning services across different knowledge resources. In the presented approach, we employ our Description Logic knowledge base to support the maintenance process management, as well as detailed analyses among resources, e.g., the traceability between various software artifacts. The resulting unified process model provides users with active guidance in selecting and utilizing these resources that are context-sensitive to a particular comprehension task. We illustrate both, the technical foundation based on our existing SOUND environment, as well as the general objectives and goals of our process model.

Keywords: Software maintenance, process modeling, ontological reasoning, software comprehension, traceability, text mining.

Abstract

Comprehension is an essential part of software maintenance. Only software that is well understood can evolve in a controlled manner. In this paper, we present a formal process model to support the comprehension of software systems by using Ontology and Description Logic. This formal representation supports the use of reasoning services across different knowledge resources and therefore, enables us to provide users with guidance during the comprehension process that is context sensitive to their particular comprehension task.

Keywords: Software maintenance, program comprehension, process modeling, ontological reasoning

Abstract

Traceability links provide support for software engineers in understanding the relations and dependencies among software artifacts created during the software development process. In this research, we focus on re-establishing traceability links between existing source code and documentation to support reverse engineering. We present a novel approach that addresses this issue by creating formal ontological representations for both the documentation and source code artifacts.

Abstract

Comprehension is an essential part of software evolution. Only software that is well understood can evolve in a controlled manner. In this paper, we present a formal process model to support the comprehension of software systems by using Ontology and Description Logic. This formal representation supports the use of reasoning services across different knowl- edge resources and therefore, enables us to provide users with guidance during the comprehension process that is context sensitive to their particular comprehension task. As part of the process model, we also adopt a new interactive story metaphor, to represent the interactions between users and the comprehension process.

Keywords: Software evolution, program comprehension, process modeling, story metaphor, ontological reasoning

Abstract

A challenge of existing program comprehension approaches is to provide consistent and flexible representations for software systems. Maintainers have to match their mental models with the different representations these tools provide. In this paper, we present a novel approach that addresses this issue by providing a consistent ontological representation for both source code and documentation. The ontological representation unifies information from various sources, and therefore reduces the maintainers’ comprehension efforts. In addition, representing software artifacts in a formal ontology enables maintainers to formulate hypotheses about various properties of software systems. These hypotheses can be validated through an iterative exploration of information derived by our ontology inference engine. The implementation of our approach is presented in detail, and a case study is provided to demonstrate the applicability of our approach during the architectural evolution of a website content management system.

Keywords: Program Comprehension, Software Evolution, Ontology, Automated Reasoning

Introduction

The FungalWeb Ontology is a knowledge representation vehicle designed to integrate information relevant to industrial applications of enzymes. The ontology integrates information from established sources and supports complex queries to the instantiated FungalWeb knowledge base. The ontology represents prototype Semantic Web technology customized to the domain of industrial enzymes with a focus on enzyme discovery, commercial enzyme products and vendors, and the industrial applications and benefits of industrial enzymes. Using a series of application scenarios we demonstrate the utility of this 'Semantic Web' infrastructure to the enzyme biotechnologist.

Abstract

Text Mining in biology and biomedicine requires a large amount of domain-specific knowledge. Publicly accessible resources hold much of the information needed, yet their practical integration into natural language processing (NLP) systems is fraught with manifold hurdles, especially the problem of semantic disconnectedness throughout the various resources and components. Ontologies can provide the necessary framework for a consistent semantic integration, while additionally delivering formal reasoning capabilities to NLP.

In this chapter, we address four important aspects relating to the integration of ontology and NLP: (i) An analysis of the different integration alternatives and their respective vantages; (ii) The design requirements for an ontology supporting NLP tasks; (iii) Creation and initialization of an ontology using publicly available tools and databases; and (iv) The connection of common NLP tasks with an ontology, including technical aspects of ontology deployment in a text mining framework. A concrete application example—text mining of enzyme mutations—is provided to motivate and illustrate these points.

Keywords: Text Mining, NLP, Ontology Design, Ontology Population, Ontological NLP

Abstract

The biomedical literature is growing at an ever-increasing rate, which pronounces the need to support scientists with advanced, automated means of accessing knowledge. We investigate a novel approach employing description logics (DL)-based queries made to formal ontologies that have been created using the results of text mining full-text research papers. In this paradigm, an OWL-DL ontology becomes populated with instances detected through natural language processing (NLP). The generated ontology can be queried by biologists using DL reasoners or integrated into bioinformatics workflows for further automated analyses. We demonstrate the feasibility of this approach with a system targeting the protein mutation literature.

Keywords: text mining; semantic web; ontological NLP; protein mutations; automated reasoning in bioinformatics; querying OWL-DL ontologies; description logics.