Using evolutionary algorithms to select text features for mining design rationale

Abstract

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

“…The performance of this model was also compared with the best existing models in the literature, highlighting its potential for improving effort estimation in agile environments. Lester et al (2020) investigate the concept of design rationale (DR), a repository of decisions and justifications made during the design process, beneficial for guiding future designs and maintaining consistency. Capturing DR is typically avoided due to its perceived cost and effort, leading to an implicit presence in various project documents.…”

A Novel Patent Knowledge Extraction Method for Innovative Design

“…Lester et al [36] conducted a rationale extraction study on bug reports of Chrome web browser, which included decisions, alternatives, answers, arguments, assumptions, procedures, questions, and requirements. They studied two evolutionary algorithms to optimize feature selection to improve knowledge extraction performance based on NLTK's Naive Bayes classifier.…”

Natural language processing in-and-for design research

“…To identify computer-supported collaborative technologies, Brisco, Whitfield, and Grierson (2020, p. 65) obtain Global Design Project text data from 104 students and classify the sentences into requirements, technologies and technology functionalities using RapidMinerStudio 15 . Lester, Guerrero, and Burge (2020, pp. 133–135) classify the chrome bug reports 16 into requirements, decisions and alternatives using the Naïve Bayes algorithm to find that features selected using optimisation approaches (e.g., Ant colony) result in higher F-1 measure compared to document characteristics (e.g., TF-IDF).…”