Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews

Henao, Pablo Restrepo; Fischbach, Jannik; Spies, Dominik; Frattini, Julian; Vogelsang, Andreas

doi:10.1109/rew53955.2021.00019

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…The reported results demonstrate that deep learning methods perform better than machine learning. Henao et al, (2021) have applied BERT model for classification of app reviews into problem report, feature request, and irrelevant. In their paper, they improved classical machine learning and deep learning model using pre-trained language model.…”

Section: Deep Models For App Review Classificationmentioning

confidence: 99%

“…Few researchers have utilized pre-trained language model for app issue classification. Hadi and Fard, (2020) reported a lite BERT model achieves satisfactory results than other pre-trained models, whereas Henao et al, (2021) have claimed that simple BERT model does not leads to performance gain. To fill these gaps, our research work fine-tuned BERT model by integrating with deep learning layers.…”

Section: Motivationmentioning

confidence: 99%

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BERT-RCNN: an Automatic Classification of App Reviews using Transfer Learning based RCNN Deep Model

Kaur

2023

Preprint

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App reviews considered as the major concern over the internet. It influences the user’s mind before purchasing the app. However, such user reviews might contain technical information about the app that can be valuable for the developers and software companies. Due to pervasive use of mobile apps, large amount of data is created by users on daily basis. Manual identification and classification of such reviews is very time-consuming and laborious task. Hence, automating this process is essential for helping developers in managing these reviews efficiently. Recently, traditional machine learning and deep learning oriented approaches have been exploited for classification of app review into software requirements. Most of the machine learning techniques utilizes traditional word techniques to extract the features from the textual reviews. In addition, deep learning techniques with efficient word embedding technique also improve the classification performance of the model. However, it is found that these techniques suffer from polysemic problem. To address aforementioned problem, this research paper presents novel neural language based framework, BERT-RCNN to classify app reviews into specified categories of functional requirements such as Bug report, Feature Request and Relevant etc. The presented framework has two main modules: Bidirectional Encoder Representation for Transformer (BERT) module and Recurrent-Convolutional Neural Network (RCNN) module. BERT module extracts the contextual relationship between the textual reviews. Then, these features are input to RCNN module to capture more important features with deep semantic information. The output of RCNN is fed to fully connected layer for classification purpose. Extensive experiments are conducted on five datasets to investigate the effectiveness of the presented BERT-RCNN model. For evaluation purpose, standard performance measures are used to obtain the results. Moreover, hyperparameter such as BiLSTM memory unit, CNN filters and CNN kernel size are tuned to ensure the quality of prediction. Comparative analysis of proposed model is conducted with existing state-of-the-art models. It shows that BERT-RCNN outperforms other state-of-the-art models with respect to precision, recall, f-measure and accuracy.

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Section: Deep Models For App Review Classificationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

BERT-RCNN: an Automatic Classification of App Reviews using Transfer Learning based RCNN Deep Model

Kaur

2023

Preprint

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“…Since the recently published review data [41], [42] do not involve the helpfulness number, we train the helpfulness prediction model based on the old dataset. Moreover, the features of helpful reviews from different periods would be similar, so the freshness of the reviews would not be a great threat.…”

Section: A Threat and Validitymentioning

confidence: 99%

“…Haering et al [50] focused on the gap between technically-written bug reports with colloquially-written app reviews, extracting issues from app reviews and matching them to bug reports. Henao et al [51] proposed a framework for mining feature requests and bug reports from tweets and app store reviews via transfer learning.…”

Section: B App Review Miningmentioning

confidence: 99%

Listening to Users' Voice: Automatic Summarization of Helpful App Reviews

Gao¹,

Li²,

Qi³

et al. 2022

Preprint

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App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features to add. There exist prior explorations on app review mining for release planning, however, most of the studies strongly rely on pre-defined classes or manually-annotated reviews. Also, the new review characteristic, i.e., the number of users who rated the review as helpful, which can help capture important reviews, has not been considered previously.In the paper, we propose a novel framework, named SOLAR, aiming at accurately summarizing helpful user reviews to developers. The framework mainly contains three modules: The review helpfulness prediction module, topic-sentiment modeling module, and multi-factor ranking module. The review helpfulness prediction module assesses the helpfulness of reviews, i.e., whether the review is useful for developers. The topic-sentiment modeling module groups the topics of the helpful reviews and also predicts the associated sentiment, and the multi-factor ranking module aims at prioritizing semantically representative reviews for each topic as the review summary. Experiments on five popular apps indicate that SOLAR is effective for review summarization and promising for facilitating app release planning.

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“…In a recent review analysis survey [4], it had been raised that App store reviews can be integrated with other feedbacks available for developers to attain users" needs from more than one source, such as: Github [12,13] and tweets [14,15]. Since a mobile App can be hosted across platforms, such integration would demand a lot of manual effort from the App developer a lot of time and effort from App developer.…”

Section: Introductionmentioning

confidence: 99%

Integrating User Reviews and Issue Reports of Mobile Apps for Change Requests Detection

Al-Safoury¹,

Salah²,

Makady³

2022

IJACSA

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There is abundance of mobile Apps released continuously on the App store, where developers are required to maintain these Apps to attain user satisfaction. Developers should consider all user feedback, as they are important resources for planning of next App's release. In order to consider user feedback, many platforms host mobile Apps and allow users to submit their opinions, such as: Google Play App store and Github Open-Source Development platform. The automated consolidation of user feedback from such platforms, and transforming it into a list of change requests would result in satisfying users across different platforms, and their analysis helps developer to reduce cost of time and effort to plan for the new release of the mobile App. In this paper, a framework is proposed which integrates user feedback from different sources and analyzes them using a state-of-art user reviews analysis tool to obtain a list of change requests, such list is further examined for similarity to remove duplicates and prioritize the identified change requests. A prototype is designed to implement the proposed framework and applied to AntennaPod. Consequently, the framework experimentation results show that reviews and issue reports can be analyzed almost equally despite the difference of text's nature.

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Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews

Cited by 19 publications

References 18 publications

BERT-RCNN: an Automatic Classification of App Reviews using Transfer Learning based RCNN Deep Model

BERT-RCNN: an Automatic Classification of App Reviews using Transfer Learning based RCNN Deep Model

Listening to Users' Voice: Automatic Summarization of Helpful App Reviews

Integrating User Reviews and Issue Reports of Mobile Apps for Change Requests Detection

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