Modeling Relevance Relations Using Machine Learning Techniques

View Sample PDF

Author(s): Jelber Sayyad Shirabad (University of Ottawa, Canada), Timothy C. Lethbridge (University of Ottawa, Canada)and Stan Matwin (University of Ottawa, Canada)
Copyright: 2007
Pages: 40
Source title: Advances in Machine Learning Applications in Software Engineering
Source Author(s)/Editor(s): Du Zhang (California State University, USA)and Jeffery J.P. Tsai (University of Illinois at Chicago, USA)
DOI: 10.4018/978-1-59140-941-1.ch008

Keywords: Computational Intelligence / Computer Science & IT / Information Science Reference / Systems and Software Engineering

Purchase

View Modeling Relevance Relations Using Machine Learning Techniques on the publisher's website for pricing and purchasing information.

Abstract

This chapter presents the notion of relevance relations, an abstraction to represent relationships between software entities. Relevance relations map tuples of software entities to values that reflect how related they are to each other. Although there are no clear definitions for these relationships, software engineers can typically identify instances of these complex relationships. We show how a classifier can model a relevance relation. We also present the process of creating such models by using data mining and machine learning techniques. In a case study, we applied this process to a large legacy system; our system learned models of a relevance relation that predict whether a change in one file may require a change in another file. Our empirical evaluation shows that the predictive quality of such models makes them a viable choice for field deployment. We also show how by assigning different misclassification costs such models can be tuned to meet the needs of the user in terms of their precision and recall.

The IRMA Community

Research IRM

Modeling Relevance Relations Using Machine Learning Techniques

Purchase

Abstract

Related Content

IRMA Sponsors