In: International conference on mobile software engineering and systems, pp 99–110Ĭhawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. In: 15th international conference on mining software repositories (MSR), pp 142–152Ĭatolino G, Di Nucci D, Ferrucci F (2019) Cross-project just-in-time bug prediction for mobile apps: an empirical assessment. In: IEEE/ACM 14th international conference on mining software repositories (MSR), pp 37–41Ĭalciati P, Kuznetsov K, Bai X, Gorla A (2018) What did really change with the new release of the app?. Machine Learn 45(1):5–32Ĭalciati P, Gorla A (2017) How do apps evolve in their permission requests? a preliminary study. Springer, pp 698–710īreiman L (2001) Random forests. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. In: European conference on genetic programming, pp 1–13īranco P, Torgo L, Ribeiro R P (2017) Relevance-based evaluation metrics for multi-class imbalanced domains. Empir Softw Eng 26 (5):94īhowan U, Zhang M, Johnston M (2010) Genetic programming for classification with unbalanced data. ![]() Ophthalmic Physiol Opt 34(5):502–508Īssi M, Hassan S, Tian Y, Zou Y (2021) Featcompare: Feature comparison for competing mobile apps leveraging user reviews. Springer, pp 33–47Īrmstrong R A (2014) When to use the b onferroni correction. In: International symposium on search based software engineering. In: 33rd international conference on software engineering (ICSE), pp 1–10Īrcuri A, Fraser G (2011) On parameter tuning in search based software engineering. , Accessed: Īrcuri A, Briand L (2011) A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: 15th ACM international conference on global software engineering, pp 43–54ĪppAnnie (2020) App annie. , Accessed: Īlmarimi N, Ouni A, Chouchen M, Saidani I, Mkaouer MW (2020) On the detection of community smells using genetic programming-based ensemble classifier chain. IEEE Trans Softw EngĪkdeniz (2013) Google play crawler. Empir Softw Eng 25(1):824–858Īhasanuzzaman M, Hassan S, Hassan A E (2020) Studying ad library integration strategies of top free-to-download apps. Furthermore, the features analysis reveals that (1) the previous updates ratings and (2) the APK size are the most important features for both within and cross-project scenarios.Īhasanuzzaman M, Hassan S, Bezemer C-P, Hassan A E (2020) A longitudinal study of popular ad libraries in the google play store. The statistical tests revealed that our approach achieves a clear advantage over machine learning approaches ( e.g., random forest, decision tree, etc.) with significant improvements of 18% and 6% in terms of F1-score within-project and cross-project validations, respectively. We evaluate our approach and investigate the performance of both within-project and cross-project validation scenarios on a benchmark of 50,700 updates from 1,717 free Android apps from Google Play Store. In particular, the search process aims to provide the optimal trade-off between two conflicting objectives to deal with the considered classes. To solve this problem, we evolve bad release detection rules using Multi-Objective Genetic Programming (MOGP) based on the adaptation of the Non-dominated Sorting Genetic Algorithm (NSGA-II). We formulate the problem as a three-class classification problem to label the apps updates as bad, neutral or good. ![]() To better support mobile applications evolution and cut-off the costs of users dissatisfaction, we propose in this paper, AppTracker, a novel approach to automatically track bad release updates in Android applications ( i.e., releases with higher percentage of negative reviews relative to the prior releases). Thus, ensuring that the application updates are deployed in a controlled way is of crucial importance. ![]() ![]() Hence, introducing changes in this context is risky and can harmfully impact the application rating and competitiveness. Indeed, mobile apps undergo frequent updates to introduce new features, fix reported issues or adapt to new technological or environment changes. The rapid growth of the mobile applications development industry raises several new challenges to developers as they need to respond quickly to the users’ needs in a world of continuous changes.
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