Automating App Review Response Generation Based on Contextual Knowledge

Gao, Cuiyun; Zhou, Wenjie; Xia, Xin; Lo, David; Xie, Qi; Lyu, Michael R.

doi:10.1145/3464969

Cited by 11 publications

(2 citation statements)

References 51 publications

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“…Farooq et al develop AARSynth to generate responses that are aware of the app itself [3]. Gao et al develop CoRe, a response generation system that uses apps' description and information retrieval techniques [20]. Among these three, only RRGEN model is available.…”

Section: Related Workmentioning

confidence: 99%

Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation

Cao

Fard

2022

Preprint

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Studies show that developers' answers to the mobile app users' feedbacks on app stores can increase the apps' star rating. To help app developers generate answers that are related to the users' issues, recent studies develop models to generate the answers automatically. Aims: The app response generation models use deep neural networks and require training data. Pre-Trained neural language Models (PTM) used in Natural Language Processing (NLP) take advantage of the information they learned from a large corpora in an unsupervised manner, and can reduce the amount of required training data. In this paper, we evaluate PTMs to generate replies to the mobile app user feedbacks. Method: We train a Transformer model from scratch and fine tune two PTMs to evaluate the generated responses, which are compared to RRGEN, a current app response model. We also evaluate the models with different portions of the training data. Results: The results on a large dataset evaluated by automatic metrics show that PTMs obtain lower scores than the baselines. However, our human evaluation confirm that PTMs can generate more relevant and meaningful responses to the posted feedbacks. Moreover, the performance of PTMs has less drop compared to other model when the amount of training data is reduced to 1/3. Conclusion: PTMs are useful in generating responses to app reviews and are more robust models to the amount of training data provided. However, the prediction time is 19X than RRGEN. This study can provide new avenues for research in adapting the PTMs for analyzing mobile app user feedbacks.Index Terms-mobile app user feedback analysis, neural pretrained language models, automatic answer generation This research is supported by a fund from Natural Sciences and Engineering Research Council of Canada RGPIN-2019-05175.1 https://bit.ly/3EYvxv0 2 https://bit.ly/3iz7vNJ 3 https://bit.ly/2Y6raxi

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Section: Related Workmentioning

confidence: 99%

Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation

Cao

Fard

2022

Preprint

View full text Add to dashboard Cite

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“…In the survey, we randomly made 50 different review-response pairs from dataset A into a detailed questionnaire, but it will take a lot of time to complete all of them by one annotator at once. To reduce the workload of annotators, we follow the work 10,18 to only put 25 examples in one questionnaire. The examples in each questionnaire are randomly selected from the total examples.…”

Section: Human Evaluationmentioning

confidence: 99%

A transformer‐based approach for improving app review response generation

Zhang

Gao

et al. 2022

Softw Pract Exp

Self Cite

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Mobile apps are becoming an integral part of people's daily life by providing various functionalities, such as messaging and gaming. App developers try their best to ensure user experience during app development and maintenance to improve the rating of their apps on app platforms and attract more user downloads. Previous studies indicated that responding to users' reviews tends to change their attitude towards the apps positively. Users who have been replied are more likely to update the given ratings. However, reading and responding to every user review is not an easy task for developers since it is common for popular apps to receive tons of reviews every day. Thus, automation tools for review replying are needed. To address the need above, the paper introduces a Transformer-based approach, named TRRGen, to automatically generate responses to given user reviews. TRRGen extracts apps' categories, rating, and review text as the input features. By adapting a Transformer-based model, TRRGen can generate appropriate replies for new reviews. Comprehensive experiments and analysis on the real-world datasets indicate that the proposed approach can generate high-quality replies for users' reviews and significantly outperform current state-of-art approaches on the task. The manual validation results on the generated replies further demonstrate the effectiveness of the proposed approach. K E Y W O R D Sapp reviews, review response generation, transformer INTRODUCTIONMobile application (app) is a kind of software designed for mobile devices. With the increase in market occupancy of mobile devices, the number of app users grows drastically in recent years. Nowadays, the number of smartphone users

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NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity

Wu,

Weng,

Zheng

et al. 2024

J Software Evolu Process

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As software grows in size and complexity, software vulnerabilities are increasing, leading to a range of serious insecurity issues. Open‐source software vulnerability reports and documentation can provide researchers with great convenience for analysis and detection. However, the quality of different data sources varies, the data are duplicated and lack of correlation, which often requires a lot of manual management and analysis. In order to solve the problems of scattered and heterogeneous data and lack of correlation in traditional vulnerability repositories, this paper proposes a software vulnerability feature knowledge extraction method that combines the N‐gram model and mask similarity. The method generates mask text data based on the extraction of N‐gram candidate keywords and extracts vulnerability feature knowledge by calculating the similarity of mask text. This method analyzes the samples efficiently and stably in the environment of large sample size and complex samples and can obtain high‐value semi‐structured data. Then, the final node, relationship, and attribute information are obtained by secondary knowledge cleaning and extraction of the extracted semi‐structured data results. And based on the extraction results, the corresponding software vulnerability domain knowledge graph is constructed to deeply explore the semantic information features and entity relationships of vulnerabilities, which can help to efficiently study software security problems and solve vulnerability problems. The effectiveness and superiority of the proposed method is verified by comparing it with several traditional keyword extraction algorithms on Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) vulnerability data.

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Automating App Review Response Generation Based on Contextual Knowledge

Cited by 11 publications

References 51 publications

Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation

Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation

A transformer‐based approach for improving app review response generation

NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity

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