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Investigation of Quality Changes between Versions of WEKA Data Mining Software Using QMOOD

Year 2020, , 825 - 836, 30.12.2020
https://doi.org/10.35193/bseufbd.699266

Abstract

QMOOD (Quality Model for Object Oriented Design) is a hierarchical design quality model consisting of four layers and evaluates the relationships between these layers. This model calculates the values of software quality attributes using object-oriented software metrics. In this study, quality changes of open source WEKA data mining software versions were observed using QMOOD. While adding new features to the software and changing the software design structure directly affected the attributes of QMOOD, such as functionality, flexibility, and reusability, the hierarchy change of the versions caused volatility in the scores of extensibility and effectiveness. On the other hand, the increasing number of methods and classes in new versions negatively affected the value of understandability. As a result of the study, it was observed that the structural changes in the WEKA versions were parallel with the quality scores obtained with QMOOD.

References

  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.
  • Bansiya, J., & Davis, C. G. (2002). A hierarchical model for object-oriented design quality assessment. IEEE Transactions on software engineering, 28(1), 4-17.
  • Marinescu, C., Marinescu, R., Mihancea, P. F., & Wettel, R. (2005). iPlasma: An integrated platform for quality assessment of object- oriented design.
  • Jetter, A., Gall, H., Pinzger, M., & Knab, P. (2006). Assessing software quality attributes with source code metrics.
  • Dromey, R. G. (1995). A model for software product quality. IEEE Transactions on software engineering, 21(2), 146-162.
  • Lanza, M., & Marinescu, R. (2007). Object-oriented metrics in practice: using software metrics to characterize, evaluate, and improve the design of object-oriented systems. Springer Science & Business Media.
  • Padhy, N., Satapathy, S., & Singh, R. (2017). Utility of an object oriented reusability metrics and estimation complexity. Indian J. Sci. Technol, 10(3), 1-9.
  • McCall, J. A. (1977). Factors in software quality. US Rome Air development center reports.
  • Basili, V. R. (1992). Software modeling and measurement: The Goal/Question/Metric paradigm.
  • Caldiera, V. R. B. G., & Rombach, H. D. (1994). The goal question metric approach. Encyclopedia of software engineering, 528-532.
  • ISO “ISO, IEC 9126”, http://www.iso.org.
  • İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Yazılım Tasarımı Kalitesi Ders Notu. ( Doç. Dr. Feza Buzluca)
  • Jorgensen, P. C. (2018). Software testing: a craftsman’s approach. CRC press.
  • Gil, Y., & Lalouche, G. (2017). On the correlation between size and metric validity. Empirical Software Engineering, 22(5), 2585-2611.
  • Malhotra, R. (2016). Empirical research in software engineering: concepts, analysis, and applications. CRC Press.
  • Herbold, S., Trautsch, A., & Grabowski, J. (2017). A comparative study to benchmark cross-project defect prediction approaches. IEEE Transactions on Software Engineering, 44(9), 811-833.
  • Goyal, P. K., & Joshi, G. (2014, February). QMOOD metric sets to assess quality of Java program. In 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (pp. 520- 533). IEEE.
  • Rathore, S. S., & Kumar, S. (2019). A study on software fault prediction techniques. Artificial Intelligence Review, 51(2), 255-327.
  • Chawla, M. K., & Chhabra, I. (2013). Capturing OO Software metrics to attain quality attributes–a case study. International Journal of Scientific & Engineering Research, 4(6), 359-363.
  • Singh, H., & Hassan, S. I. (2015). Effect of SOLID design principles on quality of software: An empirical assessment. International Journal of Scientific and Engineering Research, 6(4).
  • Gupta, M., & Singh, S. (2018). Comparative Analysis of Software Design Patterns Based Design Metrics using Machine Learning Algorithms. Journal of Computer Engineering & Technology, 9(3), 32-41.
  • Radjenović, D., Heričko, M., Torkar, R., & Živkovič, A. (2013). Software fault prediction metrics: A systematic literature review. Information and software technology, 55(8), 1397-1418.
  • URL: www.cs.waikato.ac.nz/ml/weka/, (Erişilme Tarihi: 20.11.2019)
  • URL:http://wiki.pentaho.com/display/DATAMINING/Pentaho+Data+Mining+Community+ Documentation*, (Erişilme Tarihi:20.11.2019)

WEKA Veri Madenciliği Yazılımının Sürümleri Arasındaki Kalite Değişimlerinin QMOOD ile İncelenmesi

Year 2020, , 825 - 836, 30.12.2020
https://doi.org/10.35193/bseufbd.699266

Abstract

QMOOD (Quality Model for Object Oriented Design), dört katmandan oluşan ve bu katmanlar arasındaki ilişkileri değerlendiren hiyerarşik yapılı bir tasarım kalite modelidir. Bu model nesneye dayalı yazılım metriklerini kullanarak yazılım kalite niteliklerinin değerlerini hesaplar. Bu çalışmada, QMOOD kullanılarak, açık kaynak kodlu WEKA veri madenciliği yazılımı sürümlerinin kalite değişimleri gözlenmiştir. Yazılıma yeni sürümlerde farklı özelliklerin eklenmesi ve yazılım tasarım yapısının değişmesi QMOOD'un işlevsellik, esneklik ve yeniden kullanılabilirlik gibi niteliklerini doğrudan etkilerken, sürümlerin kalıtım hiyerarşisi değişikliği ise genişletilebilirlik ve etkinlik niteliklerinin puanlarında oynaklığa sebep olmuştur. Anlaşılırlık niteliğinin değerini ise yeni sürümlerde artan metot ve sınıf sayısı olumsuz yönde etkilemiştir. Çalışmanın sonucunda QMOOD ile elde edilen kalite puanlarıyla WEKA sürümlerindeki yapısal değişimlerin paralel olduğu gözlenmiştir.

References

  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.
  • Bansiya, J., & Davis, C. G. (2002). A hierarchical model for object-oriented design quality assessment. IEEE Transactions on software engineering, 28(1), 4-17.
  • Marinescu, C., Marinescu, R., Mihancea, P. F., & Wettel, R. (2005). iPlasma: An integrated platform for quality assessment of object- oriented design.
  • Jetter, A., Gall, H., Pinzger, M., & Knab, P. (2006). Assessing software quality attributes with source code metrics.
  • Dromey, R. G. (1995). A model for software product quality. IEEE Transactions on software engineering, 21(2), 146-162.
  • Lanza, M., & Marinescu, R. (2007). Object-oriented metrics in practice: using software metrics to characterize, evaluate, and improve the design of object-oriented systems. Springer Science & Business Media.
  • Padhy, N., Satapathy, S., & Singh, R. (2017). Utility of an object oriented reusability metrics and estimation complexity. Indian J. Sci. Technol, 10(3), 1-9.
  • McCall, J. A. (1977). Factors in software quality. US Rome Air development center reports.
  • Basili, V. R. (1992). Software modeling and measurement: The Goal/Question/Metric paradigm.
  • Caldiera, V. R. B. G., & Rombach, H. D. (1994). The goal question metric approach. Encyclopedia of software engineering, 528-532.
  • ISO “ISO, IEC 9126”, http://www.iso.org.
  • İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Yazılım Tasarımı Kalitesi Ders Notu. ( Doç. Dr. Feza Buzluca)
  • Jorgensen, P. C. (2018). Software testing: a craftsman’s approach. CRC press.
  • Gil, Y., & Lalouche, G. (2017). On the correlation between size and metric validity. Empirical Software Engineering, 22(5), 2585-2611.
  • Malhotra, R. (2016). Empirical research in software engineering: concepts, analysis, and applications. CRC Press.
  • Herbold, S., Trautsch, A., & Grabowski, J. (2017). A comparative study to benchmark cross-project defect prediction approaches. IEEE Transactions on Software Engineering, 44(9), 811-833.
  • Goyal, P. K., & Joshi, G. (2014, February). QMOOD metric sets to assess quality of Java program. In 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (pp. 520- 533). IEEE.
  • Rathore, S. S., & Kumar, S. (2019). A study on software fault prediction techniques. Artificial Intelligence Review, 51(2), 255-327.
  • Chawla, M. K., & Chhabra, I. (2013). Capturing OO Software metrics to attain quality attributes–a case study. International Journal of Scientific & Engineering Research, 4(6), 359-363.
  • Singh, H., & Hassan, S. I. (2015). Effect of SOLID design principles on quality of software: An empirical assessment. International Journal of Scientific and Engineering Research, 6(4).
  • Gupta, M., & Singh, S. (2018). Comparative Analysis of Software Design Patterns Based Design Metrics using Machine Learning Algorithms. Journal of Computer Engineering & Technology, 9(3), 32-41.
  • Radjenović, D., Heričko, M., Torkar, R., & Živkovič, A. (2013). Software fault prediction metrics: A systematic literature review. Information and software technology, 55(8), 1397-1418.
  • URL: www.cs.waikato.ac.nz/ml/weka/, (Erişilme Tarihi: 20.11.2019)
  • URL:http://wiki.pentaho.com/display/DATAMINING/Pentaho+Data+Mining+Community+ Documentation*, (Erişilme Tarihi:20.11.2019)
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Hakan Gündüz 0000-0003-2152-5490

Publication Date December 30, 2020
Submission Date March 19, 2020
Acceptance Date July 5, 2020
Published in Issue Year 2020

Cite

APA Gündüz, H. (2020). WEKA Veri Madenciliği Yazılımının Sürümleri Arasındaki Kalite Değişimlerinin QMOOD ile İncelenmesi. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 7(2), 825-836. https://doi.org/10.35193/bseufbd.699266