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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 \u2013 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>","DOI":"10.1017\/s0890060420000037","type":"journal-article","created":{"date-parts":[[2020,1,30]],"date-time":"2020-01-30T09:59:12Z","timestamp":1580378352000},"page":"132-146","source":"Crossref","is-referenced-by-count":7,"title":["Using evolutionary algorithms to select text features for mining design rationale"],"prefix":"10.1017","volume":"34","author":[{"given":"Miriam","family":"Lester","sequence":"first","affiliation":[]},{"given":"Miguel","family":"Guerrero","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0842-4185","authenticated-orcid":false,"given":"Janet","family":"Burge","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2020,1,30]]},"reference":[{"key":"S0890060420000037_ref10","unstructured":"Burge, JE (2005) Software Engineering Using Design RATionale (Dissertation). 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