TY - JOUR TT - Determining the Tested Classes with Software Metrics AU - Yücalar, Fatih AU - Borandağ, Emin PY - 2017 DA - December DO - 10.18466/cbayarfbe.330995 JF - Celal Bayar University Journal of Science JO - CBUJOS PB - Manisa Celal Bayar University WT - DergiPark SN - 1305-130X SP - 863 EP - 871 VL - 13 IS - 4 KW - Software Fault Prediction KW - Software Quality and Assuarance KW - Software Metrics KW - Software Testing N2 - Early detection andcorrection of errors appearing in software projects reduces the risk ofexceeding the estimated time and cost. An efficient and effective test planshould be implemented to detect potential errors as early as possible. In theearlier phases, codes can be analyzed by efficiently employing software metricand insight can be gained about error susceptibility and measures can be takenif necessary. It is possible to classify software metric according to the timeof collecting data, information used in the measurement, type and interval ofthe data generated. Considering software metric depending on the type andinterval of the data generated, object-oriented software metric is widely usedin the literature. There are three main metric sets used for software projectsthat are developed as object-oriented. These are Chidamber & Kemerer, MOODand QMOOD metric sets. In this study, an approach for identifying the classesthat should primarily be tested has been developed by using the object-orientedsoftware metric. Then, this approach is applied for selected versions of theproject developed. According to the results obtained, the correct determinationrate of sum of the metrics method, which was developed to identify the classesthat should primarily be tested, is ranged between 55% and 68%. In the randomselection method, which was used to make comparisons, the correct determinationrate for identifying the classes that should primarily be tested is rangedbetween 9.23% and 11.05%. In the results obtained using sum of the metricsmethod, a significant rate of improvement is observed compared to the randomselection method. CR - [1] Tiftik N., Öztarak, H., Ercek, G., Özgün, S.; Sis-tem/Yazılım Geliştirme Sürecinde Doğrulama Faaliyetleri, 3. Ulusal Yazilim Mühendisliği Sempozyumu (UYMS’07), Ankara, 2007. CR - [2] Song, O., Sheppard, M., Cartwright, M., and Mair, C.; Software Defect Association Mining and Defect Correction Effort Prediction, IEEE Transactions on Software Engineering, 2006, 32(2), pp. 69-82. CR - [3] Fenton, N. and Ohlsson, N.; Quantitative Analysis of Faults and Failures in a Complex Software System, IEEE Transactions on Software Engineering, 2000, 26(8), pp. 797-814. CR - [4] Xiaowei, W.; The Metric System about Software Maintenance”, 2011 International Conference of Information Technology, Computer Engineering and Mana-gement Sciences, Wuhan, 2011. CR - [5] Kaur, A., Sandhu, P. S., Brar, A. S.; An Empirical Approach for Software Fault Prediction, 5th International Conference on Industrial and Information Systems, pp. 261–265, Mangalore, India, 2010. CR - [6] Raymond, P. L., Weimer, B., Weimer, W. R.; Learning a Metric for Code Readability, IEEE Transactions of Software Engineering, 2010, 36(4), pp. 546-558. CR - [7] Ogasawara, H., Yamada, A., Kojo, M.; Experiences of Software Quality Management Using Metrics through the Life-Cycle, 18th International Conference on Software Engineering, pp. 179–188, Berlin, 1996. CR - [8] Chaumun, M., Kabaili, H., Keller, R., and Lustman, F.; Change Impact Model for Changeability Assessment in Object-Oriented Software Systems, Science of Computer Programming, Elsevier, 2002, 45(2-3), pp. 155-174. CR - [9] Lee, Y., Yang, J., and Chang, K. H.; Metrics and Evolution in Open Source Software, Seventh International Conference on Quality Software (QSIC 2007), pp. 191-197, IEEE: Portland, OR, 2007. CR - [10] Kastro, Y., Bener, A. B.; A defect prediction method for software versioning, Software Quality Journal, Springer, 2008, 16(4), pp. 543-562. CR - [11] Li, L., Leung, H.; Mining Static Code Metrics for a Robust Prediction of Software Defect Proneness, International Symposium on Empirical Software Engi-neering and Measurement, pp. 207-214, IEEE: Banff, AB, 2011. CR - [12] NASA Datasets, Erişim Tarihi: 22.08.2014, Çevrimiçi: http://promise.site.uottawa.ca/SERepository/datasetspage.html CR - [13] Efil, İ.; Toplam Kalite Yönetimi ve Toplam Kaliteye Ulaşmada Önemli Bir Araç: ISO 9000 Kalite Güvence Sistemi, Bursa: Uludağ Üniversitesi Basımevi, s.29, 1995. CR - [14] Galin, D.; Software Quality Assurance: From Theory to Implementation”, Addison Wesley, 2004; pp. 510-514. CR - [15] Loon, H. V.; Process Assessment and ISO/IEC 15504: A Reference Book, Springer, 2nd Edition, 2007. CR - [16] Hofmann, H., Yedlin, D. K., Mishler, J., Kushner, S.; CMMI for Outsourcing: Guidelines for Software, Systems, and IT Acquisition, Addison-Wesley Professional, 1st Ed., 2007, pp. 2-4. CR - [17] Yücalar, F.; Yazılım Ölçümüne Giriş, Maltepe Üniversitesi, Yazılım Mühendisliği Bölümü, Yazılım Ölçütleri Ders Notları, 2013. CR - [18] Chidamber, S., Kemerer, C.; A Metrics Suite for Object-Oriented Design, IEEE Transactions on Software Engineering, 1994, 20(6), pp. 476-493. CR - [19] Brito e Abreu, F., Pereira, G., Soursa, P.; Coupling-Guided Cluster Analysis Approach to Reengineer the Modularity of Object-Oriented Systems, Conference on Software Maintenance and Reengineering, pp. 13-22, IEEE: Washington, DC, USA, 2000. CR - [20] J. Bansiya and C. Davis; A Hierarchical Model for Object-Oriented Design Quality Assessment, IEEE Transactions on Software Engineering, 2002, 28(1), pp. 4-17. CR - [21] Erdemir, U., Tekin, U., Buzluca, F.; Nesneye Dayalı Yazılım Metrikleri ve Yazılım Kalitesi, Yazılım Kalitesi ve Yazılım Geliştirme Araçları Sempozyumu (YKGS’2008), İstanbul, 2008. UR - https://doi.org/10.18466/cbayarfbe.330995 L1 - https://dergipark.org.tr/en/download/article-file/394897 ER -