Advertisement

Validation of Lehman Laws of Growth and Familiarity for Open Source Java Databases

  • Arvinder Kaur
  • Vidhi Vig
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)

Abstract

Lehman’s laws of software evolution have been widely researched and validated but there exists very few studies that verified these laws for databases in open source. Database evolution jeopardize the semantical and syntactical cogency of an applications, but, is their incremental augmentation restrained by the growth and familiarity? To verify this, the current study explores the properties of growth for database evolution by analyzing Lehman’s fifth and sixth law of software evolution: Law of Conservation of Familiarity and Continuous Growth on three open source Java databases spread across 63 releases for 17774 number of bugs. The study found that Lehman’s laws of growth and familiarity applies on Open Source Java databases also and laws were validated by all the datasets.

Keywords

Open source Databases Lehman laws of software evolution Bugs 

References

  1. 1.
    Lehman, M.M., Perry, D.E., Ramil, J.F.: On evidence supporting the FEAST hypothesis and the laws of software evolution, 5th International Software Metrics Symposium, 84–88 (1998)Google Scholar
  2. 2.
    Lehman, M.M., Ramil, J.F., Wernick, P.D., Perry, D.E., Turski, W.M.: Metrics and laws of software evolution—the nineties view, International Software Metrics Symposium, 0–3 (1997)Google Scholar
  3. 3.
    Lehman, M.M., Ramil, J.F.: Rules and tools for software evolution planning and management, Annals of Software Engineering, 11 (1), 15–44 (2001)Google Scholar
  4. 4.
    Lehman, M.M.: Laws of software evolution revisited, European Workshop on Software Process Technology (1996)Google Scholar
  5. 5.
    Skoulis, I., Vassiliadis, P., Zarras, A.: Open-Source Databases: Within, Outside, or Beyond Lehman’s Laws of Software Evolution?. In International Conference on Advanced Information Systems Engineering, 379–393 (2014)Google Scholar
  6. 6.
    Cook, S., Harrison, R., Lehman, M.M., Wernick, P.: Evolution in software systems: foundations of the SPE classification scheme. Journal of Software Maintenance and Evolution: Research and Practice, 18(1), 1–35 (2006)Google Scholar
  7. 7.
    Kemerer, C.F., Slaughter, S.: An empirical approach to studying software evolution, IEEE Transactions on Software Engineering, 25(4), 493–509 (1999)Google Scholar
  8. 8.
    Kaur, A., Vig, V.: Mining software repositories for empirical validation of laws of software evolution for Java projects, International Journal of Computational Systems Engineering, 3, 155–173 (2016)Google Scholar
  9. 9.
    Papastefanatos, G., Vassiliadis, P., Simitsis, A., Vassiliou, Y.,: Metrics for the prediction of evolution impact in etl ecosystems: A case study. Journal on Data Semantics, 1(2), 75–97 (2012)Google Scholar
  10. 10.
    Sjøberg, D.: Quantifying schema evolution. Information and Software Technology, 35(1), 35–44 (1993)Google Scholar
  11. 11.
    Manousis, P., Panos V., Apostolos Z., George, P.: Schema Evolution for Databases and Data Warehouses, In European Business Intelligence Summer School, 1–31 (2015)Google Scholar
  12. 12.
    Cleve, A., Maxime, G., Loup, M., Jerome, M., Jens, W.: Understanding database schema evolution: A case study, Science of Computer Programming 113–121 (2015)Google Scholar
  13. 13.
    Apache Server Foundation, http://www.apache.org
  14. 14.

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.USICTGuru Gobind Singh Indraprastha UniversityNew DelhiIndia

Personalised recommendations