1. bookVolume 46 (2021): Issue 2 (June 2021)
Zeitschriftendaten
License
Format
Zeitschrift
Erstveröffentlichung
24 Oct 2012
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
access type Open Access

A Statistical Evaluation of The Depth of Inheritance Tree Metric for Open-Source Applications Developed in Java

Online veröffentlicht: 17 Jun 2021
Seitenbereich: 159 - 172
Eingereicht: 14 Jul 2020
Akzeptiert: 10 Jan 2021
Zeitschriftendaten
License
Format
Zeitschrift
Erstveröffentlichung
24 Oct 2012
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

[1] Barkmann, H., Lincke, R., Lowe, W., Quantitative evaluation of software quality metrics in open-source projects, in: Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops, Bradford, UK, 2009, 1067–1072. https://doi.org/10.1109/WAINA.2009.190 Search in Google Scholar

[2] Bouktif, S., Sahraoui, H., Ahmed, F., Predicting stability of open-source software systems using combination of Bayesian classifiers, ACM Transactions on Management Information Systems, 5, 1, Article 3, 2014, 1-26. https://doi.org/10.1145/2555596 Search in Google Scholar

[3] Bousquet, L.d., Shaheen, M.R., Relation between depth of inheritance tree and number of methods to test, in: Proceedings of the 1st International Conference on Software Testing, Verification, and Validation, Lillehammer, Norway, 2008, 161-170. https://doi.org/10.1109/ICST.2008.34 Search in Google Scholar

[4] Box, G.E.P., Cox, D.R., An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological), 26, 2, 1964, 211-252. Search in Google Scholar

[5] Chidamber, S.R., Kemerer, C.F., A metrics suite for object oriented design. IEEE Transactions on Software Engineering, 20, 6, 1994, 476-493. http://dx.doi.org/10.1109/32.295895 Search in Google Scholar

[6] Depth of Inheritance Tree, https://www.cachequality.com/docs/metrics/depth-inheritance-tree, last accessed 2020/04/16. Search in Google Scholar

[7] Elahi, E., Kanwal, S., Asif, A.N., A new ensemble approach for software fault prediction, in: Proceedings of the 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, 2020, 407-412. https://doi.org/10.1109/IBCAST47879.2020.9044596 Search in Google Scholar

[8] Foucault, M., Teyton, C., Lo, D., Blanc, X., Falleri, J.R., On the usefulness of ownership metrics in open-source software projects, Information and Software Technology, 64, 2015, 102-112. https://doi.org/10.1016/j.infsof.2015.01.013 Search in Google Scholar

[9] Freedman, D., Pisani, R., Purves, R., Statistics. 4th edn. Wiley, 2007. Search in Google Scholar

[10] Grubbs, F., Procedures for detecting outlying observations in samples, Technometrics, 11, 1, 1969, 1-21. Search in Google Scholar

[11] Johnson, R.A., Wichern, D.W., Applied multivariate statistical analysis, Pearson Prentice Hall, 2007. Search in Google Scholar

[12] Kendall, M.G., Stuart, A., The advanced theory of statistics. Vol. 1, Distribution Theory. 2nd edn., Charles Griffin & Company Limited, London, 1963. Search in Google Scholar

[13] Makkar, G., Chhabra, J.K., Challa, R.K., Object oriented inheritance metric-reusability perspective, in: Proceedings of the International Conference on Computing, Electronics and Electrical Technologies (ICCEET), Kumaracoil, India, 2012, 852-859. https://doi.org/10.1109/ICCEET.2012.6203815 Search in Google Scholar

[14] Mishra, D., New Inheritance Complexity Metrics for Object-Oriented Software Systems: An Evaluation with Weyuker’s Properties, Computing and Informatics, 30, 2, 2011, 267-293. Search in Google Scholar

[15] Molnar AJ., Neamţu A., Motogna S., Evaluation of software product quality metrics, in: Damiani E., Spanoudakis G., Maciaszek L. (eds.), Evaluation of Novel Approaches to Software Engineering. ENASE 2019. Communications in Computer and Information Science, vol. 1172, Springer, Cham, 2020, 163-187. https://doi.org/10.1007/978-3-030-40223-5_8 Search in Google Scholar

[16] Prykhodko, S.B., Statistical anomaly detection techniques based on normalizing transformations for non-Gaussian data, in: Proceedings of the International Conference on Computational Intelligence (Results, Problems and Perspectives), Kyiv-Cherkasy, Ukraine, 2015, 286-287. Search in Google Scholar

[17] Prykhodko, S., Prykhodko, N., Makarova, L., Pugachenko, K., Detecting outliers in multivariate non-Gaussian data on the basis of normalizing transformations, in: Proceedings of the 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), Kyiv, Ukraine, 2017, 846-849. https://doi.org/10.1109/UKRCON.2017.8100366 Search in Google Scholar

[18] Prykhodko, N., Prykhodko, S., Vorona, M., The non-linear regression model to estimate the part of NPLS in the whole loan portfolio of Ukrainian banks, in: Proceedings of the 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC), Kyiv, Ukraine, 2018, 261-265. https://doi.org/10.1109/SAIC.2018.8516899 Search in Google Scholar

[19] Rathore, S.S., Kumar, S., A study on software fault prediction techniques, Artificial Intelligence Review, 51, 2, 2019, 255-327. https://doi.org/10.1007/s10462-017-9563-5 Search in Google Scholar

[20] Shaheen, M.R., Bousquet, L.d., Is depth of inheritance tree a good cost prediction for branch coverage testing? in: Proceedings of the First International Conference on Advances in System Testing and Validation Lifecycle, Porto, Portugal, 2009, 42-47. https://doi.org/10.1109/VALID.2009.11 Search in Google Scholar

[21] Shatnawi, R., Empirical study of fault prediction for open-source systems using the Chidamber and Kemerer metrics, IET Software, 8, 3, 2014, 113-119. http://dx.doi.org/10.1049/iet-sen.2013.0008 Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo