Classification of application reviews into software maintenance tasks using data mining techniques
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Classification of application reviews into software maintenance tasks using data mining techniques Assem Al-Hawari 1 & Hassan Najadat 1 & Raed Shatnawi 1 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Mobile application reviews are considered a rich source of information for software engineers to provide a general understanding of user requirements and technical feedback to avoid main programming issues. Previous researches have used traditional data mining techniques to classify user reviews into several software maintenance tasks. In this paper, we aim to use associative classification (AC) algorithms to investigate the performance of different classifiers to classify reviews into several software maintenance tasks. Also, we proposed a new AC approach for review mining (ACRM). Review classification needs preprocessing steps to apply natural language preprocessing and text analysis. Also, we studied the influence of two feature selection techniques (information gain and chisquare) on classifiers. Association rules give a better understanding of users’ intent since they discover the hidden patterns in words and features that are related to one of the maintenance tasks, and present it as class association rules (CARs). For testing the classifiers, we used two datasets that classify reviews into four different maintenance tasks. Results show that the highest accuracy was achieved by AC algorithms for both datasets. ACRM has the highest precision, recall, F-score, and accuracy. Feature selection helps improving the classifiers’ performance significantly. Keywords Associative classification . Software reviews mining . Interesting measures
* Raed Shatnawi [email protected] Assem Al-Hawari [email protected] Hassan Najadat [email protected]
1
Jordan University of Science and Technology, Irbid, Jordan
Software Quality Journal
1 Introduction User feedback and rating are very important for both users and developers and represent a rich source of information. Users can rate an app from one to five stars and write a review about their experience or problems faced during downloading, upgrading, or running the app. Moreover, users can ask developers to enhance or add features. As we can see, these feedbacks are very important for developers in respect to software engineering (SE) maintenance tasks to improve their apps. We have a huge amount of user feedback as text reviews. Users add reviews every day, which makes it very difficult to track all of these reviews. This is a challenge faced by developers; some apps have tens of thousands of reviews. These reviews are unstructured data and may contain numbers, symbols, or even informal language. Since these reviews are written by the users, they have the freedom to write them in their way. In addition, some reviews have only a few specific words that indicate user intention. Furthermore, many reviews have no useful information for developers, for example: “I hate this app” or “this is a great app, love it.” These reviews do not
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