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Background: The App Stores, for example, Google Play and Apple Play Store provide a platform that allows users to provide feedback on the apps in the form of reviews. An app review typically includes star rating followed by a comment. Recent studies have shown that these reviews possess a vital source of information that can be used by the app developers and the vendors for improving the future versions of an app. However, in most of the cases, these reviews are present in unstructured form and extracting useful information from them requires a great effort. Objective: This article provides an optimized classification approach that automatically classifies the reviews into a bug report, feature request, and shortcoming & improvement request relevant to Requirement Engineering. Method: Our methodology merges three techniques, namely (1) Text Analysis, (2) Natural Language Processing, and (3) Sentiment Analysis to extract features set, which is then used to automatically classify app reviews into their relevant categories. Results: Result shows that we achieved best results with precision of 67.8 % and recall of 41.5 % with Logistic Regression Machine Learning technique, which we further optimized with PSO nature-inspired algorithm, i.e., with Logistic Regression + PSO, thus, resulting in a precision of 74.4 % and recall of 45.0 %. Conclusion: This optimized automatic classification improves the Requirement Engineering where developer straightforwardly knows what to improve further in the concerned app. |
