App Competition Matters: How to Identify Your Competitor Apps?

Analysing app reviews for software engineering: a systematic literature review

“…Pattern matching techniques (employed in 22 studies) localize parts of review text (or its linguistic analysis) matching hand-crafted patterns. Such patterns can take many forms, such as, regular expressions (Yang and Liang 2015;Groen et al 2017;Uddin et al 2020), PoS sequences (Vu et al 2016;Johann et al 2017), dependencies between words (Gu and Kim 2015;Peng et al 2016;Di Sorbo et al 2017;Srisopha et al 2020c) or simple keyword matching (Yang and Liang 2015;Di Sorbo et al 2017;Tao et al 2020). The technique has been adopted in Information Extraction e.g., to extract requirements from reviews (Yang and Liang 2015;Groen et al 2017), Classification e.g., to classify requirements into functional and non-functional (Yang and Liang 2015) and Summarization e.g., to provide a bug report summary (Groen et al 2017).…”

“…This was practiced for evaluating Classification (Li et al 2017), Clustering (Guzman and Maalej 2014;Vu et al 2015a;Palomba et al 2017), Information Extraction (Johann et al 2017;Li et al 2017), Searching and Information Retrieval (Wei et al 2017), and Recommendation (Shams et al 2020). -Seven studies used other software artefacts as an evaluation baseline rather than creating an annotated dataset Man et al 2016;Gao et al 2018b;Uddin et al 2020;Srisopha et al 2020a;Srisopha et al 2020c;Xiao et al 2020). To evaluate Recommendation (e.g., determining priorities for reported issues), the studies compared recommended priorities for issues with priorities for the issues reported in user forums or changelogs; to assess the quality of Clustering, the studies benchmarked the output of their approaches with topics from app changelogs; whereas to evaluate their approaches in Recommending reviews that need to be responded, the studies used information of already responded reviews that developers posted in app stores.…”