Abstract
Improving and updating applications based on user reviews is crucial to the continuous development of modern mobile applications. However, software bug descriptions in user reviews are often written by non-professional users, and contain a lot of irrelevant text, making it challenging to conduct bug localization. The current software bug localization technologies based on user reviews are not able to address these challenges effectively, resulting in suboptimal results. To address this issue, we propose ReviewLocator, which focuses on key phrases and learning from historical bug reports. It first utilizes syntactic analysis or source file parsing to convert each user review or source file into phrase representations. Then it depends on Key Phrase-based Ranking using a newly proposed Bug Report-based Term Weight to map review phrase sets to source file phrase sets. In our experiments on eight applications from the Google Play Store, the results prove our proposal surpasses ChangeAdvisor and Where2Change with an absolute improvement of 0.076 and 0.055 in terms of MAP correspondingly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
References
Leite, L., Rocha, C., Kon, F., Milojicic, D., Meirelles, P.: A survey of devops concepts and challenges. ACM Comput. Surv. 52(6) (2019)
Noei, E., Zhang, F., Zou, Y.: Too many user-reviews! what should app developers look at first? IEEE Trans. Software Eng. 47(2), 367–378 (2021)
Zhou, Y., Su, Y., Chen, T., Huang, Z., Gall, H., Panichella, S.: User review-based change file localization for mobile applications. IEEE Trans. Softw. Eng. 47(12) (2021)
Zhang, T., Chen, J., Zhan, X., Luo, X., Lo, D., Jiang, H.: Where2change: change request localization for app reviews. IEEE Trans. Softw. Eng. 47(11), 2590–2616 (2021)
Ciurumelea, A., Schaufelbühl, A., Panichella, S., Gall, H.C.: Analyzing reviews and code of mobile apps for better release planning. In: 24th International Conference on Software Analysis. Evolution and Reengineering, pp. 91–102. IEEE, Klagenfurt (2017)
Kurtanović, Z., Maalej, W.: On user rationale in software engineering. Requir. Eng. 23, 357–379 (2018)
Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C.A., Canfora, G., Gall, H.C.: How can i improve my app? classifying user reviews for software maintenance and evolution. In: Proceedings of the 2015 IEEE International Conference on Software Maintenance and Evolution, pp. 281–290. IEEE Computer Society, Bremen (2015)
Chen, R., Wang, Q., Xu, W.: Mining user requirements to facilitate mobile app quality upgrades with big data. Electron. Commer. Res. Appl. 38(C), 100889 (2019)
Hadi, M.A., Fard, F.H.: Aobtm: adaptive online biterm topic modeling for version sensitive short-texts analysis. In: International Conference on Software Maintenance and Evolution, pp. 593–604. IEEE, Adelaide (2020)
Di Sorbo, A., Panichella, S., Alexandru, C.V., Visaggio, C.A., Canfora, G.: Surf: summarizer of user reviews feedback. In: Proceedings of the 39th International Conference on Software Engineering Companion, pp. 55–58. IEEE Press, Buenos Aires (2017)
Gao, C., et al.: Listening to users’ voice: automatic summarization of helpful app reviews. IEEE Trans. Reliab. 1–13 (2022)
Phetrungnapha, K., Senivongse, T.: Classification of mobile application user reviews for generating tickets on issue tracking system. In: 12th International Conference on Information & Communication Technology and System, pp. 229–234. IEEE, Surabaya (2019)
Palomba, F., et al.: Recommending and localizing change requests for mobile apps based on user reviews. In: Proceedings of the 39th International Conference on Software Engineering, pp. 106–117. IEEE Press, Buenos Aires (2017)
Tang, X., Tian, H., Kong, P., Liu, K., Klein, J., Bissyande, T.F.: App review driven collaborative bug finding. arXiv preprint arXiv:2301.02818 (2023)
Yu, L., Chen, J., Zhou, H., Luo, X., Liu, K.: Localizing function errors in mobile apps with user reviews. In: 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, pp. 418–429. IEEE, Luxembourg (2018)
Yu, L., et al.: Towards automatically localizing function errors in mobile apps with user reviews. IEEE Trans. Softw. Eng. 49(4), 1464–1486 (2022)
Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C.A., Canfora, G., Gall, H.C.: Ardoc: app reviews development oriented classifier. In: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 1023–1027. Association for Computing Machinery, Seattle (2016)
Maalej, W., Kurtanović, Z., Nabil, H., Stanik, C.: On the automatic classification of app reviews. Requir. Eng. 21(3), 311–331 (2016)
Martens, D., Maalej, W.: Towards understanding and detecting fake reviews in app stores. Empir. Softw. Eng. 24(6), 3316–3355 (2019)
Mahmud, O., Niloy, N.T., Rahman, M.A., Siddik, M.S.: Predicting an effective android application release based on user reviews and ratings. In: 7th International Conference on Smart Computing & Communications, 1–5. IEEE, Sarawak (2019)
Tao, C., Guo, H., Huang, Z.: Identifying security issues for mobile applications based on user review summarization. Inf. Softw. Technol. 122, 106290 (2020)
Wu, H., Deng, W., Niu, X., Nie, C.: Identifying key features from app user reviews. In: Proceedings of the 43rd International Conference on Software Engineering, pp. 922–932. IEEE Press, Madrid (2021)
Nadeem, M., Shahzad, K., Majeed, N.: Extracting software change requests from mobile app reviews. In: 36th IEEE/ACM International Conference on Automated Software Engineering Workshops, pp. 198–203. IEEE, Melbourne (2021)
Binkley, D., Davis, M., Lawrie, D., Morrell, C.: To camelcase or under_score. In: Proceedings of the 17th International Conference on Program Comprehension, pp. 158–167. IEEE, Vancouver (2009)
Qu, Z., Rastogi, V., Zhang, X., Chen, Y., Zhu, T., Chen, Z.: Autocog: measuring the description-to-permission fidelity in android applications. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1354–1365. Association for Computing Machinery, Scottsdale (2014)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (61972219), the Research and Development Program of Shenzhen (JCYJ20190813174403598), the Overseas Research Cooperation Fund of Tsinghua Shenzhen International Graduate School (HW2021013), the Guangdong Basic and Applied Basic Research Foundation (2022A1515010417), the Key Project of Shenzhen Municipality (JSGG20211029095545002), the Science and Technology Research Project of Henan Province (222102210096), the Key Laboratory of Network Assessment Technology, Institute of Information Engineering, Chinese Academy of Sciences, and Shenzhen Science and Technology Innovation Commission (Research Center for Computer Network (Shenzhen) Ministry of Education).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xiao, R., Xiao, X., Yu, L., Zhang, B., Hu, G., Li, Q. (2023). ReviewLocator: Enhance User Review-Based Bug Localization with Bug Reports. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14180. Springer, Cham. https://doi.org/10.1007/978-3-031-46677-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-46677-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46676-2
Online ISBN: 978-3-031-46677-9
eBook Packages: Computer ScienceComputer Science (R0)