A systematic mapping study on text analysis techniques in software architecture

Topic modeling in software engineering research

“…However, we do not claim to present a fully-fledged systematic literature review (e.g., we did not assess the quality of primary studies) or a mapping study (e.g., we only analyzed papers from carefully selected venues). Furthermore, we used parts of the procedures from other literature surveys on similar topics (Bi et al 2018;Chen et al 2016;Sun et al 2016) as discussed throughout this section.…”

“…Topic modeling aims at automatically finding topics, typically represented as clusters of words, in a given textual document (Bi et al 2018). Unlike (supervised) machine learningbased techniques that solve classification problems, topic modeling does not use tags, training data or predefined taxonomies of concepts (Bi et al 2018).…”

“…Topic modeling aims at automatically finding topics, typically represented as clusters of words, in a given textual document (Bi et al 2018). Unlike (supervised) machine learningbased techniques that solve classification problems, topic modeling does not use tags, training data or predefined taxonomies of concepts (Bi et al 2018). Based on the frequencies of words and frequencies of co-occurrence of words within one or more documents, topic modeling clusters words that are often used together (Barua et al 2014;Treude and Wagner 2019).…”

“…Techniques for text mining include natural language processing (NLP) to process, search and understand the structure of text (e.g., part-of-speech tagging), web mining to discover information resources on the web (e.g., web crawling), and information extraction to extract structured information from unstructured text and relationships between pieces of information (e.g., co-reference, entity extraction) (Miner et al 2012). Text mining has been widely used in software engineering research (Bi et al 2018), for example, to uncover architectural design decisions in developer communication (Soliman et al 2016) or to link software artifacts to source code (Asuncion et al 2010).…”

“…-RQ3: How are data pre-processed for topic modeling? Topic modeling requires that the analyzed text is pre-processed (e.g., by removing stop words) to improve the quality of the produced output (Aggarwal and Zhai 2012;Bi et al 2018). This RQ aims at analysing how previous studies pre-processed textual data for topic modeling, including the steps for cleaning and transforming text.…”