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Article contents

Using evolutionary algorithms to select text features for mining design rationale

Published online by Cambridge University Press:  30 January 2020

Miriam Lester
Affiliation:
Department of Mathematics and Computer Science, Wesleyan University, Middletown, CT, USA
Miguel Guerrero
Affiliation:
Department of Mathematics and Computer Science, Colorado College, Colorado Springs, CO, USA
Janet Burge
Affiliation:
Department of Mathematics and Computer Science, Colorado College, Colorado Springs, CO, USA
Corresponding

Abstract

At its heart, design is a decision-making process. These decisions, and the reasons for making them, comprise the design rationale (DR) for the designed artifact. If available, DR provides a comprehensive record of the reasoning behind the decisions made during the design. Unfortunately, while this information is potentially quite valuable, it is usually not explicitly captured. Instead, it is often buried in other design and development artifacts. In this paper, we study how to identify rationale from text documents, specifically software bug reports and design discussion transcripts. The method we examined is statistical text mining where a model is built to use document features to classify sentences. Choosing which features are most likely to be good predictors is important. We studied two evolutionary algorithms to optimize feature selection – ant colony optimization and genetic algorithms. We found that for many types of rationale, models built with an optimized feature set outperformed those built using all the document features.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2020

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