{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:36:25Z","timestamp":1771612585032,"version":"3.50.1"},"reference-count":134,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100008247","name":"University of Otago","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100008247","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Empir Software Eng"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Just-In-Time  (JIT) defect prediction aims to identify defects early, at commit time. Hence, developers can take precautions to avoid defects when the code changes are still fresh in their minds. However, the utility of JIT defect prediction has not been investigated in relation to crashes of mobile apps. We therefore conducted a multi-case study employing both quantitative and qualitative analysis. In the quantitative analysis, we used machine learning techniques for prediction. We collected 113 reliability-related metrics for about 30,000 commits from 14 Android apps and selected 14 important metrics for prediction. We found that both standard JIT metrics and static analysis warnings are important for JIT prediction of mobile app crashes. We further optimized prediction performance, comparing seven state-of-the-art defect prediction techniques with hyperparameter optimization. Our results showed that Random Forest is the best performing model with an AUC-ROC of 0.83. In our qualitative analysis, we manually analysed a sample of 642 commits and identified different types of changes that are common in crash-inducing commits. We explored whether different aspects of changes can be used as metrics in JIT models to improve prediction performance. We found these metrics improve the prediction performance significantly. Hence, we suggest considering static analysis warnings <jats:italic>and<\/jats:italic> Android-specific metrics to adapt standard JIT defect prediction models for a mobile context to predict crashes. Finally, we provide recommendations to bridge the gap between research and practice and point to opportunities for future research.<\/jats:p>","DOI":"10.1007\/s10664-024-10455-7","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T10:02:11Z","timestamp":1715162531000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Just-in-Time crash prediction for mobile apps"],"prefix":"10.1007","volume":"29","author":[{"given":"Chathrie","family":"Wimalasooriya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sherlock A.","family":"Licorish","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel Alencar","family":"da Costa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen G.","family":"MacDonell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"key":"10455_CR1","doi-asserted-by":"crossref","unstructured":"Aljamaan H, Alazba A (2020) Software defect prediction using tree-based ensembles, in Proceedings of the 16th ACM international conference on predictive models and data analytics in software engineering, pp. 1\u201310","DOI":"10.1145\/3416508.3417114"},{"key":"10455_CR2","unstructured":"Allison P (2013) in What\u2019s the Best R-Squared for Logistic Regression? vol. 2022, ed. Statistical Horizons"},{"key":"10455_CR3","doi-asserted-by":"crossref","unstructured":"An L, Khomh F (2015) An empirical study of crash-inducing commits in mozilla firefox, in Proceedings of the 11th international conference on predictive models and data analytics in software engineering, pp. 1\u201310","DOI":"10.1145\/2810146.2810152"},{"key":"10455_CR4","doi-asserted-by":"crossref","unstructured":"Andr\u00e4 L-M, Taufner B, Schefer-Wenzl S, Miladinovic I (2020) Maintainability Metrics for Android Applications in Kotlin: An Evaluation of Tools, in Proceedings of the 2020 European Symposium on Software Engineering, pp. 1\u20135","DOI":"10.1145\/3393822.3432334"},{"issue":"5","key":"10455_CR5","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1109\/TSE.2014.2312942","volume":"40","author":"V Arnaoudova","year":"2014","unstructured":"Arnaoudova V, Eshkevari LM, Di Penta M, Oliveto R, Antoniol G, Gu\u00e9h\u00e9neuc Y-G (2014) Repent: Analyzing the nature of identifier renamings. IEEE Trans Software Eng 40(5):502\u2013532","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR6","doi-asserted-by":"crossref","unstructured":"Asaduzzaman M, Bullock MC, Roy CK, Schneider KA (2012) Bug introducing changes: A case study with android, in 2012 9th IEEE Working Conference on Mining Software Repositories (MSR), IEEE, pp. 116\u2013119","DOI":"10.1109\/MSR.2012.6224267"},{"key":"10455_CR7","doi-asserted-by":"crossref","unstructured":"Barnett JG, Gathuru CK, Soldano LS, McIntosh S (2016) The relationship between commit message detail and defect proneness in java projects on github, in 2016 IEEE\/ACM 13th Working Conference on Mining Software Repositories (MSR), IEEE, pp. 496\u2013499","DOI":"10.1145\/2901739.2903496"},{"issue":"2","key":"10455_CR8","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1145\/1646353.1646374","volume":"53","author":"A Bessey","year":"2010","unstructured":"Bessey A et al (2010) A few billion lines of code later: using static analysis to find bugs in the real world. Commun ACM 53(2):66\u201375","journal-title":"Commun ACM"},{"key":"10455_CR9","unstructured":"Black TR (1999) Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics. sage"},{"issue":"5","key":"10455_CR10","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MS.1987.231780","volume":"4","author":"BW Boehm","year":"1987","unstructured":"Boehm BW (1987) Industrial software metrics top 10 list. IEEE Softw 4(5):84\u201385","journal-title":"IEEE Softw"},{"issue":"1","key":"10455_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"5","key":"10455_CR12","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s10664-022-10137-2","volume":"27","author":"J Callan","year":"2022","unstructured":"Callan J, Krauss O, Petke J, Sarro F (2022) How do Android developers improve non-functional properties of software? Empir Softw Eng 27(5):113","journal-title":"Empir Softw Eng"},{"issue":"6","key":"10455_CR13","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/s10664-022-10186-7","volume":"27","author":"J Carka","year":"2022","unstructured":"Carka J, Esposito M, Falessi D (2022) On effort-aware metrics for defect prediction. Empir Softw Eng 27(6):152","journal-title":"Empir Softw Eng"},{"issue":"6","key":"10455_CR14","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1109\/TSE.2009.42","volume":"35","author":"M Cataldo","year":"2009","unstructured":"Cataldo M, Mockus A, Roberts JA, Herbsleb JD (2009) Software dependencies, work dependencies, and their impact on failures. IEEE Trans Software Eng 35(6):864\u2013878","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR15","doi-asserted-by":"crossref","unstructured":"Catolino G (2017) Just-in-time bug prediction in mobile applications: the domain matters!, in 2017 IEEE\/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft), IEEE, pp. 201\u2013202.","DOI":"10.1109\/MOBILESoft.2017.58"},{"key":"10455_CR16","doi-asserted-by":"crossref","unstructured":"Catolino G, Di Nucci D, Ferrucci F (2019) Cross-project just-in-time bug prediction for mobile apps: an empirical assessment, in IEEE\/ACM 6th International Conference on Mobile Software Engineering and Systems, pp. 99\u2013110","DOI":"10.1109\/MOBILESoft.2019.00023"},{"key":"10455_CR17","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"10455_CR18","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system, in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"issue":"6","key":"10455_CR19","first-page":"1","volume":"16","author":"T Cheng","year":"2022","unstructured":"Cheng T, Zhao K, Sun S, Mateen M, Wen J (2022) Effort-aware cross-project just-in-time defect prediction framework for mobile apps. Front Comp Sci 16(6):1\u201315","journal-title":"Front Comp Sci"},{"key":"10455_CR20","doi-asserted-by":"crossref","unstructured":"Chirila C-B, Juratoni D, Tudor D, Cre\u0163u V (2011) Towards a software quality assessment model based on open-source statical code analyzers, in 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE, pp. 341\u2013346","DOI":"10.1109\/SACI.2011.5873026"},{"key":"10455_CR21","doi-asserted-by":"crossref","unstructured":"Christakis M, Bird C (2016) What developers want and need from program analysis: an empirical study, in Proceedings of the 31st IEEE\/ACM international conference on automated software engineering, pp. 332\u2013343","DOI":"10.1145\/2970276.2970347"},{"issue":"1","key":"10455_CR22","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1):37\u201346","journal-title":"Educ Psychol Measur"},{"issue":"7","key":"10455_CR23","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/TSE.2016.2616306","volume":"43","author":"DA Da Costa","year":"2016","unstructured":"Da Costa DA, McIntosh S, Shang W, Kulesza U, Coelho R, Hassan AE (2016) A framework for evaluating the results of the szz approach for identifying bug-introducing changes. IEEE Trans Software Eng 43(7):641\u2013657","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR24","unstructured":"\"Data Leakage And Its Effect On The Performance of An ML Model.\" https:\/\/www.analyticsvidhya.com\/blog\/2021\/07\/data-leakage-and-its-effect-on-the-performance-of-an-ml-model\/ Accessed Nov 2022"},{"issue":"1","key":"10455_CR25","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1177\/0049124102031001002","volume":"31","author":"A DeMaris","year":"2002","unstructured":"DeMaris A (2002) Explained variance in logistic regression: A Monte Carlo study of proposed measures. Soc Methods Res 31(1):27\u201374","journal-title":"Soc Methods Res"},{"issue":"1","key":"10455_CR26","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/TSE.2017.2659747","volume":"44","author":"D Di Nucci","year":"2017","unstructured":"Di Nucci D, Palomba F, De Rosa G, Bavota G, Oliveto R, De Lucia A (2017) A developer centered bug prediction model. IEEE Trans Software Eng 44(1):5\u201324","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR27","unstructured":"Dickerson J (2016) Mobile apps: what consumers really need and want. A global study of consumers expectations and experiences of mobile applications. [Online]. Available: https:\/\/silo.tips\/download\/mobile-apps-what-consumers-really-need-and-want-a-global-study-of-consumers-expe#. Accessed\u00a0May 2022"},{"key":"10455_CR28","first-page":"1","volume-title":"International workshop on multiple classifier systems","author":"TG Dietterich","year":"2000","unstructured":"Dietterich TG (2000) Ensemble methods in machine learning. International workshop on multiple classifier systems. Springer, pp 1\u201315"},{"issue":"3","key":"10455_CR29","doi-asserted-by":"publisher","first-page":"93","DOI":"10.6025\/jic\/2018\/9\/3\/93-101","volume":"9","author":"J Ding","year":"2018","unstructured":"Ding J, Fu L (2018) A Hybrid Feature Selection Algorithm Based on Information Gain and Sequential Forward Floating Search\u2460. J Intell Comput 9(3):93","journal-title":"J Intell Comput"},{"key":"10455_CR30","unstructured":"Do LNQ, Wright J, Ali K (2020) Why do software developers use static analysis tools? a user-centered study of developer needs and motivations. IEEE Transactions on Software Engineering"},{"key":"10455_CR31","doi-asserted-by":"publisher","first-page":"4805","DOI":"10.1007\/s10664-020-09868-x","volume":"25","author":"D Falessi","year":"2020","unstructured":"Falessi D, Huang J, Narayana L, Thai JF, Turhan B (2020) On the need of preserving order of data when validating within-project defect classifiers. Empir Softw Eng 25:4805\u20134830","journal-title":"Empir Softw Eng"},{"issue":"8","key":"10455_CR32","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TSE.2019.2929761","volume":"47","author":"Y Fan","year":"2019","unstructured":"Fan Y, Xia X, Da Costa DA, Lo D, Hassan AE, Li S (2019) The impact of mislabeled changes by szz on just-in-time defect prediction. IEEE Trans Software Eng 47(8):1559\u20131586","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR33","doi-asserted-by":"crossref","unstructured":"Fan L et al. (2018a) Efficiently manifesting asynchronous programming errors in android apps, in Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering, pp. 486\u2013497","DOI":"10.1145\/3238147.3238170"},{"key":"10455_CR34","doi-asserted-by":"crossref","unstructured":"Fan L et al. (2018b) Large-scale analysis of framework-specific exceptions in android apps, in IEEE\/ACM 40th International Conference on Software Engineering, pp. 408\u2013419","DOI":"10.1145\/3180155.3180222"},{"issue":"5","key":"10455_CR35","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1109\/32.815326","volume":"25","author":"NE Fenton","year":"1999","unstructured":"Fenton NE, Neil M (1999) A critique of software defect prediction models. IEEE Trans Software Eng 25(5):675\u2013689","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR36","doi-asserted-by":"crossref","unstructured":"Feurer M, Hutter F (2019) Hyperparameter optimization, in Automated machine learning: Springer, Cham, pp. 3\u201333","DOI":"10.1007\/978-3-030-05318-5_1"},{"key":"10455_CR37","doi-asserted-by":"crossref","unstructured":"Fukushima T, Kamei Y, McIntosh S, Yamashita K, Ubayashi N (2014) An empirical study of just-in-time defect prediction using cross-project models, in Proceedings of the 11th working conference on mining software repositories, pp. 172\u2013181","DOI":"10.1145\/2597073.2597075"},{"issue":"14\u201315","key":"10455_CR38","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","volume":"32","author":"MW Gardner","year":"1998","unstructured":"Gardner MW, Dorling S (1998) Artificial neural networks (the multilayer perceptron)\u2014a review of applications in the atmospheric sciences. Atmos Environ 32(14\u201315):2627\u20132636","journal-title":"Atmos Environ"},{"key":"10455_CR39","doi-asserted-by":"crossref","unstructured":"Ghotra B, McIntosh S, Hassan AE (2015) Revisiting the impact of classification techniques on the performance of defect prediction models, in 2015 IEEE\/ACM 37th IEEE International Conference on Software Engineering, vol. 1: IEEE, pp. 789\u2013800","DOI":"10.1109\/ICSE.2015.91"},{"issue":"6","key":"10455_CR40","doi-asserted-by":"publisher","first-page":"3356","DOI":"10.1007\/s10664-019-09727-4","volume":"24","author":"B G\u00f3is Mateus","year":"2019","unstructured":"G\u00f3is Mateus B, Martinez M (2019) An empirical study on quality of Android applications written in Kotlin language. Empir Softw Eng 24(6):3356\u20133393","journal-title":"Empir Softw Eng"},{"key":"10455_CR41","doi-asserted-by":"crossref","unstructured":"Gopstein D, Zhou HH, Frankl P, Cappos J (2018) Prevalence of Confusing Code in Software Projects, in Proceedings of the 15th International Conference on Mining Software Repositories-MSR 18: 281\u2013291","DOI":"10.1145\/3196398.3196432"},{"issue":"10","key":"10455_CR42","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1109\/TSE.2005.112","volume":"31","author":"T Gyim\u00f3thy","year":"2005","unstructured":"Gyim\u00f3thy T, Ferenc R, Siket I (2005) Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Trans Software Eng 31(10):897\u2013910","journal-title":"IEEE Trans Software Eng"},{"issue":"6","key":"10455_CR43","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1109\/TSE.2011.103","volume":"38","author":"T Hall","year":"2011","unstructured":"Hall T, Beecham S, Bowes D, Gray D, Counsell S (2011) A systematic literature review on fault prediction performance in software engineering. IEEE Trans Software Eng 38(6):1276\u20131304","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR44","unstructured":"\"How to Avoid Data Leakage When Performing Data Preparation.\" (n.d.) Machine Learning Mastery. https:\/\/machinelearningmastery.com\/data-preparation-without-data-leakage\/. Accessed\u00a0May 2022"},{"key":"10455_CR45","doi-asserted-by":"crossref","unstructured":"He Y, Zhu X, Wang G, Sun H, Wang Y (2017) Predicting bugs in software code changes using isolation forest, in 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS), IEEE, pp. 296\u2013305","DOI":"10.1109\/QRS.2017.40"},{"key":"10455_CR46","doi-asserted-by":"crossref","unstructured":"Hecht G, Benomar O, Rouvoy R, Moha N, Duchien L (2015a) Tracking the software quality of android applications along their evolution (t), in 2015 30th IEEE\/ACM International Conference on Automated Software Engineering (ASE), IEEE, pp. 236\u2013247","DOI":"10.1109\/ASE.2015.46"},{"key":"10455_CR47","doi-asserted-by":"crossref","unstructured":"Hecht G, Rouvoy R, Moha N, Duchien L (2015b) Detecting antipatterns in android apps, in 2015 2nd ACM international conference on mobile software engineering and systems, IEEE, pp. 148\u2013149","DOI":"10.1109\/MobileSoft.2015.38"},{"issue":"3","key":"10455_CR48","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1177\/0049124116638107","volume":"47","author":"GA Hemmert","year":"2018","unstructured":"Hemmert GA, Schons LM, Wieseke J, Schimmelpfennig H (2018) Log-likelihood-based pseudo-R 2 in logistic regression: deriving sample-sensitive benchmarks. Sociol Methods Res 47(3):507\u2013531","journal-title":"Sociol Methods Res"},{"key":"10455_CR49","doi-asserted-by":"crossref","unstructured":"Hoang T, Dam HK, Kamei Y, Lo D, Ubayashi N (2019) DeepJIT: an end-to-end deep learning framework for just-in-time defect prediction, in 2019 IEEE\/ACM 16th International Conference on Mining Software Repositories (MSR), IEEE, pp. 34\u201345","DOI":"10.1109\/MSR.2019.00016"},{"key":"10455_CR50","doi-asserted-by":"crossref","unstructured":"Huang J, Borges N, Bugiel S, Backes M (2019) Up-to-crash: Evaluating third-party library updatability on android, in 2019 IEEE European Symposium on Security and Privacy (EuroS&P), IEEE, pp. 15\u201330","DOI":"10.1109\/EuroSP.2019.00012"},{"key":"10455_CR51","unstructured":"Immaculate SD, Begam MF, Floramary M (2019) Software bug prediction using supervised machine learning algorithms, in 2019 International conference on data science and communication (IconDSC), IEEE, pp. 1\u20137"},{"key":"10455_CR52","doi-asserted-by":"crossref","unstructured":"Imtiaz N, Murphy B, Williams L (2019) How do developers act on static analysis alerts? an empirical study of coverity usage, in 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE), IEEE, pp. 323\u2013333","DOI":"10.1109\/ISSRE.2019.00040"},{"key":"10455_CR53","unstructured":"ISO\/IEC (2011) ISO\/IEC 25010: 2011-Systems and software engineering\u2014Systems and software Quality Requirements and Evaluation (SQuaRE)\u2014System and software quality models, vol 25010. BSI,\u00a0London"},{"key":"10455_CR54","doi-asserted-by":"crossref","unstructured":"Jovi\u0107 A, Brki\u0107 K, Bogunovi\u0107 N (2015) A review of feature selection methods with applications, in 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), Ieee, pp. 1200\u20131205","DOI":"10.1109\/MIPRO.2015.7160458"},{"issue":"6","key":"10455_CR55","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1109\/TSE.2012.70","volume":"39","author":"Y Kamei","year":"2012","unstructured":"Kamei Y et al (2012) A large-scale empirical study of just-in-time quality assurance. IEEE Trans Software Eng 39(6):757\u2013773","journal-title":"IEEE Trans Software Eng"},{"issue":"5","key":"10455_CR56","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1007\/s10664-015-9400-x","volume":"21","author":"Y Kamei","year":"2016","unstructured":"Kamei Y, Fukushima T, McIntosh S, Yamashita K, Ubayashi N, Hassan AE (2016) Studying just-in-time defect prediction using cross-project models. Empir Softw Eng 21(5):2072\u20132106","journal-title":"Empir Softw Eng"},{"key":"10455_CR57","doi-asserted-by":"crossref","unstructured":"Kamei Y, Monden A, Matsumoto S, Kakimoto T, Matsumoto K-I (2007) The effects of over and under sampling on fault-prone module detection, in First international symposium on empirical software engineering and measurement (ESEM 2007), IEEE, pp. 196\u2013204","DOI":"10.1109\/ESEM.2007.28"},{"key":"10455_CR58","doi-asserted-by":"crossref","unstructured":"Kaur A, Kaur K, Kaur H (2016) Application of machine learning on process metrics for defect prediction in mobile application, in Information Systems Design and Intelligent Applications: Springer, pp. 81\u201398","DOI":"10.1007\/978-81-322-2755-7_10"},{"issue":"2","key":"10455_CR59","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/TSE.2007.70773","volume":"34","author":"S Kim","year":"2008","unstructured":"Kim S, Whitehead EJ, Zhang Y (2008) Classifying software changes: Clean or buggy? IEEE Trans Software Eng 34(2):181\u2013196","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR60","doi-asserted-by":"crossref","unstructured":"Kim S, Zimmermann T, Pan K, James Jr E (2006) Automatic identification of bug-introducing changes, in 21st IEEE\/ACM international conference on automated software engineering (ASE'06), IEEE, pp. 81\u201390","DOI":"10.1109\/ASE.2006.23"},{"key":"10455_CR61","doi-asserted-by":"crossref","unstructured":"Kim S, Zhang H, Wu R, Gong L (2011) Dealing with noise in defect prediction, in Proceedings of the 33rd International Conference on Software Engineering, pp. 481\u2013490","DOI":"10.1145\/1985793.1985859"},{"key":"10455_CR62","doi-asserted-by":"crossref","unstructured":"Koziarski M (2021) CSMOUTE: Combined synthetic oversampling and undersampling technique for imbalanced data classification, in 2021 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9533415"},{"issue":"1","key":"10455_CR63","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2307\/2529310","volume":"33","author":"JR Landis","year":"1977","unstructured":"Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159\u2013174","journal-title":"Biometrics"},{"key":"10455_CR64","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.infsof.2014.07.005","volume":"58","author":"IH Laradji","year":"2015","unstructured":"Laradji IH, Alshayeb M, Ghouti L (2015) Software defect prediction using ensemble learning on selected features. Inf Softw Technol 58:388\u2013402","journal-title":"Inf Softw Technol"},{"key":"10455_CR65","doi-asserted-by":"publisher","first-page":"106287","DOI":"10.1016\/j.infsof.2020.106287","volume":"122","author":"N Li","year":"2020","unstructured":"Li N, Shepperd M, Guo Y (2020a) A systematic review of unsupervised learning techniques for software defect prediction. Inf Softw Technol 122:106287","journal-title":"Inf Softw Technol"},{"key":"10455_CR66","doi-asserted-by":"publisher","first-page":"106364","DOI":"10.1016\/j.infsof.2020.106364","volume":"126","author":"W Li","year":"2020","unstructured":"Li W, Zhang W, Jia X, Huang Z (2020b) Effort-aware semi-supervised just-in-time defect prediction. Inf Softw Technol 126:106364","journal-title":"Inf Softw Technol"},{"key":"10455_CR67","doi-asserted-by":"crossref","unstructured":"Li R, Zhou L, Zhang S, Liu H, Huang X, Sun Z (2019) Software defect prediction based on ensemble learning, in Proceedings of the 2019 2nd International conference on data science and information technology, pp. 1\u20136","DOI":"10.1145\/3352411.3352412"},{"key":"10455_CR68","doi-asserted-by":"publisher","first-page":"111283","DOI":"10.1016\/j.jss.2022.111283","volume":"188","author":"F Lomio","year":"2022","unstructured":"Lomio F, Iannone E, De Lucia A, Palomba F, Lenarduzzi V (2022) Just-in-time software vulnerability detection: Are we there yet? J Syst Softw 188:111283","journal-title":"J Syst Softw"},{"issue":"3","key":"10455_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3130944","volume":"1","author":"X Lu","year":"2017","unstructured":"Lu X, Chen Z, Liu X, Li H, Xie T, Mei Q (2017) Prado: Predicting app adoption by learning the correlation between developer-controllable properties and user behaviors. Proc ACM Interact Mob Wearable Ubiquit Technol 1(3):1\u201330","journal-title":"Proc ACM Interact Mob Wearable Ubiquit Technol"},{"issue":"3","key":"10455_CR70","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1007\/s10664-015-9388-2","volume":"21","author":"S McIlroy","year":"2016","unstructured":"McIlroy S, Ali N, Hassan AE (2016) Fresh apps: an empirical study of frequently-updated mobile apps in the Google play store. Empir Softw Eng 21(3):1346\u20131370","journal-title":"Empir Softw Eng"},{"key":"10455_CR71","doi-asserted-by":"crossref","unstructured":"McIntosh S, Kamei Y (2018) Are fix-inducing changes a moving target? a longitudinal case study of just-in-time defect prediction, in Proceedings of the 40th International Conference on Software Engineering, pp. 560\u2013560","DOI":"10.1145\/3180155.3182514"},{"key":"10455_CR72","doi-asserted-by":"publisher","first-page":"102516","DOI":"10.1016\/j.scico.2020.102516","volume":"199","author":"S Meldrum","year":"2020","unstructured":"Meldrum S, Licorish SA, Owen CA, Savarimuthu BTR (2020) Understanding stack overflow code quality: A recommendation of caution. Sci Comput Program 199:102516","journal-title":"Sci Comput Program"},{"issue":"6","key":"10455_CR73","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1109\/TSE.2012.83","volume":"39","author":"T Menzies","year":"2012","unstructured":"Menzies T et al (2012) Local versus global lessons for defect prediction and effort estimation. IEEE Trans Software Eng 39(6):822\u2013834","journal-title":"IEEE Trans Software Eng"},{"issue":"2","key":"10455_CR74","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1002\/bltj.2229","volume":"5","author":"A Mockus","year":"2000","unstructured":"Mockus A, Weiss DM (2000) Predicting risk of software changes. Bell Labs Tech J 5(2):169\u2013180","journal-title":"Bell Labs Tech J"},{"issue":"5","key":"10455_CR75","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/32.135775","volume":"18","author":"JC Munson","year":"1992","unstructured":"Munson JC, Khoshgoftaar TM (1992) The detection of fault-prone programs. IEEE Trans Software Eng 18(5):423","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR76","doi-asserted-by":"crossref","unstructured":"Nayebi M, Adams B, Ruhe G (2016) Release Practices for Mobile Apps--What do Users and Developers Think?, in 2016 ieee 23rd international conference on software analysis, evolution, and reengineering (saner) 1: IEEE, pp. 552\u2013562","DOI":"10.1109\/SANER.2016.116"},{"key":"10455_CR77","doi-asserted-by":"crossref","unstructured":"Neto EC, Da Costa DA, Kulesza U (2018) The impact of refactoring changes on the szz algorithm: An empirical study, in 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, pp. 380\u2013390","DOI":"10.1109\/SANER.2018.8330225"},{"key":"10455_CR78","doi-asserted-by":"crossref","unstructured":"Neto EC, da Costa DA, Kulesza U (2019) Revisiting and improving szz implementations, in 2019 ACM\/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), IEEE, pp. 1\u201312","DOI":"10.1109\/ESEM.2019.8870178"},{"issue":"1","key":"10455_CR79","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s10664-012-9218-8","volume":"19","author":"A Okutan","year":"2014","unstructured":"Okutan A, Y\u0131ld\u0131z OT (2014) Software defect prediction using Bayesian networks. Empir Softw Eng 19(1):154\u2013181","journal-title":"Empir Softw Eng"},{"issue":"6","key":"10455_CR80","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3533378","volume":"55","author":"A Paleyes","year":"2022","unstructured":"Paleyes A, Urma R-G, Lawrence ND (2022) Challenges in deploying machine learning: a survey of case studies. ACM Comput Surv 55(6):1\u201329","journal-title":"ACM Comput Surv"},{"key":"10455_CR81","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.jss.2018.12.001","volume":"150","author":"L Pascarella","year":"2019","unstructured":"Pascarella L, Palomba F, Bacchelli A (2019) Fine-grained just-in-time defect prediction. J Syst Softw 150:22\u201336","journal-title":"J Syst Softw"},{"key":"10455_CR82","doi-asserted-by":"crossref","unstructured":"Pascarella L, Geiger F-X, Palomba F, Di Nucci D, Malavolta I, Bacchelli A (2018) Self-reported activities of android developers, in Proceedings of the 5th International Conference on Mobile Software Engineering and Systems, pp. 144\u2013155","DOI":"10.1145\/3197231.3197251"},{"key":"10455_CR83","doi-asserted-by":"crossref","unstructured":"Perry DE, Porter AA, Votta LG (2000) Empirical studies of software engineering: a roadmap, in Proceedings of the conference on The future of Software engineering, pp. 345\u2013355","DOI":"10.1145\/336512.336586"},{"key":"10455_CR84","doi-asserted-by":"crossref","unstructured":"Phong MV, Nguyen TT, Pham HV, Nguyen TT (2015) Mining user opinions in mobile app reviews: A keyword-based approach (t), in 2015 30th IEEE\/ACM International Conference on Automated Software Engineering (ASE), IEEE, pp. 749\u2013759","DOI":"10.1109\/ASE.2015.85"},{"issue":"1","key":"10455_CR85","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF00116251","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81\u2013106","journal-title":"Mach Learn"},{"key":"10455_CR86","doi-asserted-by":"crossref","unstructured":"Rahman F, Posnett D, Hindle A, Barr E, Devanbu P (2011) BugCache for inspections: hit or miss?, in Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering, pp. 322\u2013331","DOI":"10.1145\/2025113.2025157"},{"key":"10455_CR87","unstructured":"R-Documentation. \"Pseudo R2 Statistics.\" https:\/\/search.r-project.org\/CRAN\/refmans\/DescTools\/html\/PseudoR2.html Accessed Nov 2022"},{"key":"10455_CR88","unstructured":"Rish I (2001) An empirical study of the naive Bayes classifier. IJCAI workshop on empirical methods in artificial intelligence,\u00a0Seattle, pp 41\u201346"},{"key":"10455_CR89","doi-asserted-by":"crossref","unstructured":"Rodriguez D, Herraiz I, Harrison R, Dolado J, Riquelme JC (2014) Preliminary comparison of techniques for dealing with imbalance in software defect prediction, in Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, pp. 1\u201310","DOI":"10.1145\/2601248.2601294"},{"key":"10455_CR90","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.infsof.2018.03.009","volume":"99","author":"G Rodr\u00edguez-P\u00e9rez","year":"2018","unstructured":"Rodr\u00edguez-P\u00e9rez G, Robles G, Gonz\u00e1lez-Barahona JM (2018) Reproducibility and credibility in empirical software engineering: A case study based on a systematic literature review of the use of the szz algorithm. Inf Softw Technol 99:164\u2013176","journal-title":"Inf Softw Technol"},{"issue":"1","key":"10455_CR91","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","volume":"33","author":"L Rokach","year":"2010","unstructured":"Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1):1\u201339","journal-title":"Artif Intell Rev"},{"key":"10455_CR92","doi-asserted-by":"crossref","unstructured":"Rosen C, Grawi B, Shihab E (2015) Commit guru: analytics and risk prediction of software commits, in Proceedings of the 2015 10th joint meeting on foundations of software engineering, pp. 966\u2013969","DOI":"10.1145\/2786805.2803183"},{"key":"10455_CR93","unstructured":"S. Inc. \"Mobile operating systems' market share worldwide from January 2012 to November 2022.\" https:\/\/www.statista.com\/statistics\/272698\/global-market-share-held-by-mobile-operating-systems-since-2009\/ Accessed Nov 2022"},{"issue":"6","key":"10455_CR94","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TSE.2016.2615307","volume":"43","author":"A Sadeghi","year":"2016","unstructured":"Sadeghi A, Bagheri H, Garcia J, Malek S (2016) A taxonomy and qualitative comparison of program analysis techniques for security assessment of android software. IEEE Trans Software Eng 43(6):492\u2013530","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR95","doi-asserted-by":"crossref","unstructured":"Santos G, Figueiredo E, Veloso A, Viggiato M, Ziviani N (2020) Predicting software defects with explainable machine learning, in Proceedings of the XIX Brazilian Symposium on Software Quality, pp. 1\u201310","DOI":"10.1145\/3439961.3439979"},{"key":"10455_CR96","doi-asserted-by":"crossref","unstructured":"Scalabrino S, Bavota G, Linares-V\u00e1squez M, Lanza M, Oliveto R (2019) Data-driven solutions to detect api compatibility issues in android: an empirical study, in 2019 IEEE\/ACM 16th International Conference on Mining Software Repositories (MSR), IEEE, pp. 288\u2013298","DOI":"10.1109\/MSR.2019.00055"},{"key":"10455_CR97","unstructured":"scikit-learn. \"sklearn.model_selection.StratifiedKFold.\" https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold Accessed Nov 2022"},{"key":"10455_CR98","unstructured":"Shihab E (2012) An exploration of challenges limiting pragmatic software defect prediction. Queen's University (Canada)"},{"key":"10455_CR99","doi-asserted-by":"crossref","unstructured":"Shin J, Aleithan R, Nam J, Wang J, Wang S (2021) Explainable Software Defect Prediction: Are We There Yet?. arXiv preprint arXiv:2111.10901","DOI":"10.1109\/ICSE-Companion52605.2021.00056"},{"key":"10455_CR100","unstructured":"Shu R, Xia T, Williams L, Menzies T (2019) Better security bug report classification via hyperparameter optimization, arXiv preprint arXiv:1905.06872"},{"issue":"4","key":"10455_CR101","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1082983.1083147","volume":"30","author":"J \u015aliwerski","year":"2005","unstructured":"\u015aliwerski J, Zimmermann T, Zeller A (2005) When do changes induce fixes? ACM Sigsoft Software Eng Notes 30(4):1\u20135","journal-title":"ACM Sigsoft Software Eng Notes"},{"key":"10455_CR102","unstructured":"Sommerville I (2011) Software engineering 9th Edition, ISBN-10, vol. 137035152, p. 18"},{"issue":"12","key":"10455_CR103","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1109\/TSE.2018.2836442","volume":"45","author":"Q Song","year":"2018","unstructured":"Song Q, Guo Y, Shepperd M (2018) A comprehensive investigation of the role of imbalanced learning for software defect prediction. IEEE Trans Software Eng 45(12):1253\u20131269","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR104","doi-asserted-by":"publisher","unstructured":"Su T et al. (2020) Why my app crashes understanding and benchmarking framework-specific exceptions of android apps, IEEE Transactions on Software Engineering. https:\/\/doi.org\/10.1109\/TSE.2020.3013438","DOI":"10.1109\/TSE.2020.3013438"},{"key":"10455_CR105","doi-asserted-by":"crossref","unstructured":"Tan SH, Dong Z, Gao X, Roychoudhury A (2018) Repairing crashes in android apps. in IEEE\/ACM 40th International Conference on Software Engineering, pp. 187\u2013198","DOI":"10.1145\/3180155.3180243"},{"issue":"1","key":"10455_CR106","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TSE.2016.2584050","volume":"43","author":"C Tantithamthavorn","year":"2016","unstructured":"Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2016) An empirical comparison of model validation techniques for defect prediction models. IEEE Trans Software Eng 43(1):1\u201318","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR107","first-page":"19","volume":"1","author":"K Thitichaimongkhol","year":"2016","unstructured":"Thitichaimongkhol K, Senivongse T (2016) Enhancing usability heuristics for android applications on mobile devices. Proc World Congress Eng Comput Sci 1:19\u201321","journal-title":"Proc World Congress Eng Comput Sci"},{"key":"10455_CR108","doi-asserted-by":"crossref","unstructured":"Trautsch A, Herbold S, Grabowski J (2020) Static source code metrics and static analysis warnings for fine-grained just-in-time defect prediction, in 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, pp. 127\u2013138","DOI":"10.1109\/ICSME46990.2020.00022"},{"key":"10455_CR109","unstructured":"Vang J (2019) Data science topics. one-off coder. https:\/\/datascience.oneoffcoder.com\/psuedo-r-squared-logistic-regression.html"},{"issue":"9","key":"10455_CR110","first-page":"1857","volume":"47","author":"Z Wan","year":"2019","unstructured":"Wan Z, Xia X, Lo D, Murphy GC (2019) How does machine learning change software development practices? IEEE Trans Softw Eng 47(9):1857\u20131871","journal-title":"IEEE Trans Softw Eng"},{"key":"10455_CR111","doi-asserted-by":"crossref","unstructured":"Wang Q (2014) A hybrid sampling SVM approach to imbalanced data classification, in Abstract and Applied Analysis, vol. 2014: Hindawi","DOI":"10.1155\/2014\/972786"},{"key":"10455_CR112","doi-asserted-by":"crossref","unstructured":"Wen M, Wu R, Cheung S-C (2016) Locus: Locating bugs from software changes, in 2016 31st IEEE\/ACM International Conference on Automated Software Engineering (ASE), IEEE, pp. 262\u2013273","DOI":"10.1145\/2970276.2970359"},{"key":"10455_CR113","doi-asserted-by":"crossref","unstructured":"Williams C, Spacco J (2008) Szz revisited: verifying when changes induce fixes, in Proceedings of the 2008 workshop on Defects in large software systems, pp. 32\u201336","DOI":"10.1145\/1390817.1390826"},{"key":"10455_CR114","doi-asserted-by":"publisher","first-page":"111166","DOI":"10.1016\/j.jss.2021.111166","volume":"186","author":"C Wimalasooriya","year":"2022","unstructured":"Wimalasooriya C, Licorish SA, da Costa DA, MacDonell SG (2022) A systematic mapping study addressing the reliability of mobile applications: The need to move beyond testing reliability. J Syst Softw 186:111166","journal-title":"J Syst Softw"},{"key":"10455_CR115","doi-asserted-by":"crossref","unstructured":"Wright HK, Kim M, Perry DE (2010) Validity concerns in software engineering research, in Proceedings of the FSE\/SDP workshop on Future of software engineering research, pp. 411\u2013414","DOI":"10.1145\/1882362.1882446"},{"issue":"5","key":"10455_CR116","doi-asserted-by":"publisher","first-page":"2866","DOI":"10.1007\/s10664-017-9567-4","volume":"23","author":"R Wu","year":"2018","unstructured":"Wu R, Wen M, Cheung S-C, Zhang H (2018) Changelocator: locate crash-inducing changes based on crash reports. Empir Softw Eng 23(5):2866\u20132900","journal-title":"Empir Softw Eng"},{"issue":"1","key":"10455_CR117","first-page":"26","volume":"17","author":"J Wu","year":"2019","unstructured":"Wu J, Chen X-Y, Zhang H, Xiong L-D, Lei H, Deng S-H (2019) Hyperparameter optimization for machine learning models based on Bayesian optimization. J Electron Sci Technol 17(1):26\u201340","journal-title":"J Electron Sci Technol"},{"key":"10455_CR118","doi-asserted-by":"crossref","unstructured":"Wu R, Zhang H, Kim S, Cheung S-C (2011) Relink: recovering links between bugs and changes, in Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering, pp. 15\u201325","DOI":"10.1145\/2025113.2025120"},{"key":"10455_CR119","doi-asserted-by":"publisher","unstructured":"Xia X, Shihab E, Kamei Y, Lo D, Wang X (2016) Predicting crashing releases of mobile applications, in 10th ACM\/IEEE International Symposium on Empirical Software Engineering and Measurement, pp. 1\u201310. https:\/\/doi.org\/10.1145\/2961111.2962606","DOI":"10.1145\/2961111.2962606"},{"key":"10455_CR120","doi-asserted-by":"crossref","unstructured":"Xia H et al. (2020) How android developers handle evolution-induced api compatibility issues: A large-scale study, in 2020 IEEE\/ACM 42nd International Conference on Software Engineering (ICSE), IEEE, pp. 886\u2013898","DOI":"10.1145\/3377811.3380357"},{"issue":"1","key":"10455_CR121","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1109\/TR.2021.3066170","volume":"71","author":"Z Xu","year":"2021","unstructured":"Xu Z et al (2021) Effort-aware just-in-time bug prediction for mobile apps via cross-triplet deep feature embedding. IEEE Trans Reliab 71(1):204\u2013220","journal-title":"IEEE Trans Reliab"},{"key":"10455_CR122","doi-asserted-by":"publisher","unstructured":"Xu Z, Liu J, Yang Z, An G, Jia X (2016) The Impact of Feature Selection on Defect Prediction Performance: An Empirical Comparison, in 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE), 2016, pp. 309\u2013320. https:\/\/doi.org\/10.1109\/ISSRE.2016.13","DOI":"10.1109\/ISSRE.2016.13"},{"key":"10455_CR123","unstructured":"Yang H, Wang C, Shi Q, Feng Y, Chen Z (2014) Bug Inducing Analysis to Prevent Fault Prone Bug Fixes, in SEKE, pp. 620\u2013625"},{"key":"10455_CR124","doi-asserted-by":"crossref","unstructured":"Yang X, Lo D, Xia X, Zhang Y, Sun J (2015) Deep learning for just-in-time defect prediction, in 2015 IEEE International Conference on Software Quality, Reliability and Security, IEEE, pp. 17\u201326","DOI":"10.1109\/QRS.2015.14"},{"issue":"6","key":"10455_CR125","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1109\/TSE.2018.2791521","volume":"45","author":"T Yu","year":"2018","unstructured":"Yu T, Wen W, Han X, Hayes JH (2018) Conpredictor: Concurrency defect prediction in real-world applications. IEEE Trans Software Eng 45(6):558\u2013575","journal-title":"IEEE Trans Software Eng"},{"key":"10455_CR126","doi-asserted-by":"publisher","unstructured":"El Zarif O, Da Costa DA, Hassan S, Zou Y (2020) On the Relationship between User Churn and Software Issues, in 17th International Conference on Mining Software Repositoriespp. 339\u2013349. https:\/\/doi.org\/10.1145\/3379597.3387456","DOI":"10.1145\/3379597.3387456"},{"key":"10455_CR127","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.jss.2016.03.065","volume":"117","author":"S Zein","year":"2016","unstructured":"Zein S, Salleh N, Grundy J (2016) A systematic mapping study of mobile application testing techniques. J Syst Softw 117:334\u2013356","journal-title":"J Syst Softw"},{"key":"10455_CR128","doi-asserted-by":"publisher","first-page":"2140","DOI":"10.1007\/s10664-019-09696-8","volume":"24","author":"G Zhao","year":"2019","unstructured":"Zhao G, da Costa DA, Zou Y (2019) Improving the pull requests review process using learning-to-rank algorithms. Empir Softw Eng 24:2140\u20132170","journal-title":"Empir Softw Eng"},{"issue":"2","key":"10455_CR129","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1109\/TR.2021.3060937","volume":"70","author":"K Zhao","year":"2021","unstructured":"Zhao K, Xu Z, Zhang T, Tang Y, Yan M (2021a) Simplified deep forest model based just-in-time defect prediction for android mobile apps. IEEE Trans Reliab 70(2):848\u2013859","journal-title":"IEEE Trans Reliab"},{"key":"10455_CR130","unstructured":"Zhao K (2022) Pre-Process Data with Pipeline to Prevent Data Leakage during Cross-Validation. Medium https:\/\/towardsdatascience.com\/pre-process-data-with-pipeline-to-prevent-data-leakage-during-cross-validation-e3442cca7fdc Accessed 29\/07\/2022"},{"key":"10455_CR131","doi-asserted-by":"crossref","unstructured":"Zhao K, Xu Z, Yan M, Tang Y, Fan M, Catolino G (2021b) Just-in-time defect prediction for Android apps via imbalanced deep learning model, in Proceedings of the 36th Annual ACM Symposium on Applied Computing, pp. 1447\u20131454","DOI":"10.1145\/3412841.3442019"},{"key":"10455_CR132","doi-asserted-by":"publisher","first-page":"111245","DOI":"10.1016\/j.jss.2022.111245","volume":"188","author":"W Zheng","year":"2022","unstructured":"Zheng W, Shen T, Chen X, Deng P (2022) Interpretability application of the Just-in-Time software defect prediction model. J Syst Softw 188:111245","journal-title":"J Syst Softw"},{"key":"10455_CR133","doi-asserted-by":"crossref","unstructured":"Zhihao P, Fenglong Y, Xucheng L (2019) Comparison of the different sampling techniques for imbalanced classification problems in machine learning,\" in 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), IEEE, pp. 431\u2013434","DOI":"10.1109\/ICMTMA.2019.00101"},{"key":"10455_CR134","unstructured":"Zhu A (2021) Select Features for Machine Learning Model with Mutual Information, ed: Medium"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-024-10455-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-024-10455-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-024-10455-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T04:06:03Z","timestamp":1717214763000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-024-10455-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":134,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["10455"],"URL":"https:\/\/doi.org\/10.1007\/s10664-024-10455-7","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5]]},"assertion":[{"value":"7 February 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}],"article-number":"68"}}