Abstract
Context: The generic quality attributes fail to comprehend the current state-of-the-art challenges and constraints of mobile apps. Objectives: The goal of this study is to fill the gap in the systematic procedures to identify and extract specific quality features relevant to Android apps. Method: To accomplish the objective, we have proposed an ML-based Quality Features Extraction (QFE) framework for Android apps. QFE analyzes, parses, and gains insights from use reviews utilizing Natural Language Processing (NLP), Sentimental Analysis, Topic Modelling, and Lexical Semantics. Results: This study was tested on three different datasets and QFE successfully discovered 23 unique Android-specific quality features. Moreover, a comparative study with related studies was conducted and the analysis delineates that QFE provides a more reliable, efficient, and easy-to-use approach. Contribution: Briefly, (i) an ML-based empirical framework is proposed for discovering quality features for Android apps; (ii) the popular Topic Modelling technique is enhanced by RBLSALT, that is to automate the manual process of labeling topics in Topic Modelling; and finally, (iii) the pseudo-code and Python implemented notebook of the framework is also given to provide ease in the applicability of QFE. Conclusion and Future Work: Future work, is planned to evaluate the framework by comparing it with different techniques of feature extraction and to propose a specific features-oriented comprehensive quality model based on Android apps.
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References
Jain, P., Sharma, A., Aggarwal, P.K.: Key attributes for a quality mobile application, pp. 50–54, January 2020
Koushki, M.M., AbuAlhaol, I., Raju, A.D., Zhou, Y., Giagone, R.S., Shengqiang, H.: On building machine learning pipelines for android malware detection: a procedural survey of practices, challenges, and opportunities. Cybersecurity 5, 1–37 (2022)
Inukollu, V.N., Keshamoni, D.D., Kang, T., Inukollu, M.: Factors influencing quality of mobile apps: role of mobile app development life cycle. Int. J. Softw. Eng. Appl. 5, 1–34 (2014)
Jianwena, X., et al.: Software aging and rejuvenation in android new models and metrics. Softw. Qual. J. 28, 85–106 (2019)
Maia, V., Rocha, A.R., Gonçalves, T.G.: Identification of quality characteristics in mobile applications, pp. 1–119, January 2020
El-Dahshan, K.A., Elsayed, E.K., Ghannam, N.E.: Comparative study for detecting mobile application’s anti-patterns, pp. 1–8, April 2019
Chuang, H.-Y., Wang, S.-D.: Machine learning based hybrid behavior models for android malware analysis, pp. 201–206, August 2015
Shan, Z., Azim, T., Neamtiu, I.: Finding resume and restart errors in android applications. ACM SIGPLAN Not. 51, 864–880 (2016)
Moreira, J.S., Alves, E.L.G., Andrade, W.L.: A systematic mapping on energy efficiency testing in android applications, pp. 1–10, December 2021
Cui, J., Wang, L., Zhao, X., Zhang, H.: Towards a predictive analysis of android vulnerability using statistical codes and machine learning for IoT applications. Comput. Commun. 155, 125–131 (2020)
Dehkordi, M.R., Seifzadeh, H., Beydoun, G., Nadimi-Shahraki, M.H.: Success prediction of android applications in a novel repository using neural networks. Complex Intell. Syst. 6, 573–590 (2020)
Bin, Y., Yuan, C.-H.: Detecting latent topics and trends in blended learning using LDA topic modeling. Educ. Inf. Technol. 973, 1–24 (2022)
Dubey, G., Rana, A., Ranjan, J.: A research study of sentiment analysis and various techniques of sentiment classification. Int. J. Data Anal. Tech. Strat. 8, 122–142 (2016)
Govilkar, S., Bakal, J.W., Rathod, S.: Part of speech tagger for the Marathi language. Int. J. Comput. Appl. 119, 29–32 (2015)
Alebachew, C., Yitagesu, B.: Part of speech tagging: a systematic review of deep learning and machine learning approaches. J. Big Data 9, 1–25 (2022)
Chand, R., Rehman Khan, S.U., Hussain, S., Wang, W.: TTAG+R: a dataset of google play store’s top trending android games and user reviews, pp. 580–586 (2022)
Teymourzadeh, M., Gandomani, T.J.: Introducing a particular quality model in mobile application development: the mobile application developers’ perspective. J. Softw. 12, 339–347 (2017)
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Chand, R., Khan, S.U.R., Hussain, S., Wang, WL. (2024). An ML-Based Quality Features Extraction (QFE) Framework for Android Apps. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 802. Springer, Cham. https://doi.org/10.1007/978-3-031-45651-0_27
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