Updating the goal model with user reviews for the evolution of an app

Abstract

Goal model is an important model in requirements engineering, and it can describe features and their relationships for supporting the development of apps. Since an app evolves continually, the goal model also needs to be updated with new requirements to guide the whole process. As the feedback of users, reviews provide an abundant resource of user requirements for updating the goal model. In this paper, we propose an approach to help developers (a) analyze reviews to gain the information of user requirements by training a classifier and defining keyword‐based linguistic rules as well as grammar‐based rules and (b) update the goal model with the extracted information, including improving existing goals and extending the model with new goals. In addition, we design a framework to represent results so that they can be understood by developers easily. According to our experiments based on the data in Google Play, the F‐measure of classifier on reviews can reach 75.76%, and the average precision for extracting requirements‐related information from reviews is 84.04%, then we can map the information to goals with the F‐measure of 70.21%. Furthermore, the survey on 22 developers shows that the information provided by us is useful for updating the goal model.

Analysing app reviews for software engineering: a systematic literature review

“…Information retrieval is also used to establish traceability links between app reviews and other software engineering artefacts (Palomba et al 2015;Palomba et al 2018), such as source code (Palomba et al 2017;Zhou et al 2020;Shams et al 2020), stack tracers (Pelloni et al 2018, issues from tracking systems (Palomba et al 2015;, and warnings from static analysis tools (Wei et al 2017) in order to locate problems in source code (Palomba et al 2017;Ciurumelea et al 2017;Grano et al 2018), suggest potential changes (Palomba et al 2015;Palomba et al 2017), or to flag errors and bugs in an application under test (Wei et al 2017). Such traceability links can be also detected between reviews and feedback from other source like Twitter to study if the same issues are discussed in both digital channels Oehri and Guzman 2020); or between reviews and goals in goal-model to understand the extent to which app satisfies the users' goals (Liu et al 2020;Gao et al 2020).…”

“…Detect links between reviews and goals in goal model; to identify users' satisfaction w.r.t. these goals; or to recommend new goals that need to be satisfied by the app (Liu et al 2020;Gao et al 2020).…”

“…Recommendation task aims to suggest course of action that software engineers should follow. Several mining approaches, for instance (Chen et al 2014;Villarroel et al 2016;Scalabrino et al 2019;Gao et al 2020), have been proposed to recommend reviews that software engineers should investigate. These approaches typically assign priorities to a group of comments reporting the same bug Man et al 2016;Gao et al 2018b), requesting the same modification or improvement (Villarroel et al 2016;Keertipati et al 2016;Scalabrino et al 2019;Zhou et al 2020).…”

“…At a high level, pre-processing can be simply seen as turning review content into a form that is analysable for a specific mining task (see Section 3.2). There are different ways to pre-process reviews including text normalization, cleaning and augmenting (Manning et al 2008;Jurafsky and Martin 2009;Panichella et al 2015;Gao et al 2020). These pre-processing steps typically involve converting texts into lowercase (Fu et al 2013;Sänger et al 2016;Hadi and Fard 2020), breaking up a text into individual sentences (Lu and Liang 2017;Jha and Mahmoud 2017a;Zhou et al 2020), separating out words i.e., tokenization (Iacob et al 2016;Palomba et al 2017;Al-Hawari 2020), spelling correction (Palomba et al 2017;Grano et al 2018) as well as turning words into their base forms e.g., stemming or lemmatization (Maalej and Nabil 2015;Lu and Liang 2017;Panichella et al 2015;Xiao 2019).…”

“…Twenty three studies evaluated their review mining approaches through user studies Chen et al 2014;Gu and Kim 2015;Villarroel et al 2016;Panichella et al 2016;Di Sorbo et al 2016;Di Sorbo et al 2017;Ciurumelea et al 2017;Palomba et al 2017;Ciurumelea et al 2018;Greenheld et al 2018;Liu et al 2018;Gao et al 2018b;Dalpiaz and Parente 2019;Scalabrino et al 2019;Liu et al 2019;Zhou et al 2020;Gao et al 2020;Tao et al 2020;Shams et al 2020;Liu et al 2020). The objective of these evaluation was to qualitatively assess how the approach and/or their facilitated analysis are perceived by intended users (e.g., software engineers).…”