Table of Contents
Advances in Software Engineering
Volume 2015, Article ID 898514, 12 pages
http://dx.doi.org/10.1155/2015/898514
Research Article

Supporting Technical Debt Cataloging with TD-Tracker Tool

Faculty of Science and Technology, São Paulo State University (UNESP), Roberto Simonsen Street, No. 305, 19060-900 Presidente Prudente, SP, Brazil

Received 1 June 2015; Revised 8 August 2015; Accepted 27 August 2015

Academic Editor: Andrea De Lucia

Copyright © 2015 Lucas Borante Foganholi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. W. Cunningham, “Object-oriented programming systems, languages, and applications,” in The WyCash Portfolio Management System, 1992. View at Google Scholar
  2. N. Brown, Y. Cai, Y. Guo et al., “Managing technical debt in software-reliant systems,” in Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research (FoSER '10), pp. 47–52, ACM, Santa Fe, NM, USA, November 2010. View at Publisher · View at Google Scholar
  3. P. Kruchten, R. L. Nord, I. Ozkaya, and D. Falessi, “Technical debt: towards a crisper definition report on the 4th international workshop on managing technical debt,” ACM SIGSOFT Software Engineering Notes, vol. 38, no. 5, pp. 51–54, 2013. View at Publisher · View at Google Scholar
  4. C. Seaman and Y. Guo, “Measuring and monitoring technical debt,” Advances in Computers, vol. 82, pp. 25–46, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. J.-L. Letouzey and M. Ilkiewicz, “Managing technical debt with the SQALE method,” IEEE Software, vol. 29, no. 6, pp. 44–51, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Izurieta, A. Vetrò, N. Zazworka, Y. Cai, C. Seaman, and F. Shull, “Organizing the technical debt landscape,” in Proceedings of the 3rd International Workshop on Managing Technical Debt (MTD '12), pp. 23–26, IEEE, Zürich, Switzerland, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. J. A. Kupsch and B. P. Miller, “Manual vs. automated vulnerability assessment: a case study,” CEUR Workshop Proceedings, vol. 469, pp. 83–97, 2009. View at Google Scholar
  8. Y. Guo and C. Seaman, “A portfolio approach to technical debt management,” in Proceedings of the 2nd working on Managing technical debt (MTD '11), pp. 31–34, Honolulu, Hawaii, USA, May 2011. View at Publisher · View at Google Scholar
  9. M. Fowler, K. Beck, J. Brant, W. Opdyke, and D. Roberts, Refactoring: Improving the Design of Existing Code, Addison-Wesley, 1999.
  10. M. Lanza, R. Marinescu, and S. Ducasse, Object-Oriented Metrics in Practice, Springer, Secaucus, NJ, USA, 2005.
  11. R. Marinescu, “Detection strategies: metrics-based rules for detecting design flaws,” in Proceedings of the 20th IEEE International Conference on Software Maintenance, pp. 350–359, September 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Moha, Y.-G. Guéhéneuc, L. Duchien, and A.-F. Le Meur, “Decor: a method for the specification and detection of code and design smells,” IEEE Transactions on Software Engineering, vol. 36, no. 1, pp. 20–36, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. E. van Emden and L. Moonen, “Java quality assurance by detecting code smells,” in Proceedings of the 9th Working Conference on Reverse Engineering, pp. 97–106. View at Publisher · View at Google Scholar
  14. F. Simon, F. Steinbrückner, and C. Lewerentz, “Metrics based refactoring,” in Proceedings of the 5th European Conference on Software Maintenance and Reengineering, pp. 30–38, March 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Schumacher, N. Zazworka, F. Shull, C. Seaman, and M. Shaw, “Building empirical support for automated code smell detection,” in Proceedings of the ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM '10), ACM, 2010. View at Publisher · View at Google Scholar
  16. F. Palomba, G. Bavota, M. Di Penta, R. Oliveto, A. De Lucia, and D. Poshyvanyk, “Detecting bad smells in source code using change history information,” in Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering (ASE '13), pp. 268–278, November 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. N. Zazworka, M. A. Shaw, F. Shull, and C. Seaman, “Investigating the impact of design debt on software quality,” in Proceedings of the 22nd Workshop on Managing Technical Debt (MTD '11), pp. 17–23, ACM, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. D. Binkley, “Source code analysis: a road map,” in Proceedings of the Future of Software Engineering (FOSE '07), pp. 104–119, May 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Vetro, M. Morisio, and M. Torchiano, “An empirical validation of FindBugs issues related to defects,” in Proceedings of the 15th Annual Conference on Evaluation and Assessment in Software Engineering (EASE '11), pp. 144–153, April 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. N. Zazworka, A. Vetro', C. Izurieta et al., “Comparing four approaches for technical debt identification,” Software Quality Journal, vol. 22, no. 3, pp. 403–426, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Zaidman, B. Van Rompaey, S. Demeyer, and A. van Deursen, “Mining software repositories to study co-evolution of production & test code,” in Proceedings of the 1st International Conference on Software Testing, Verification, and Validation (ICST '08), pp. 220–229, IEEE, Lillehammer, Norway, April 2008. View at Publisher · View at Google Scholar
  22. PMD, July 2015, https://pmd.github.io/.
  23. S. Wong, Y. Cai, M. Kim, and M. Dalton, “Detecting software modularity violations,” in Proceedings of the 33rd International Conference on Software Engineering (ICSE '11), pp. 411–420, IEEE, Honolulu, Hawaii, USA, May 2011. View at Publisher · View at Google Scholar
  24. Y.-G. Gueheneuc and H. Albin-Amiot, “Using design patterns and constraints to automate the detection and correction of inter-class design defects,” in Proceedings of the 39th International Conference and Exhibition on Technology of Object-Oriented Languages and Systems (TOOLS39 '01), pp. 296–305, IEEE, Santa Barbara, Calif, USA, July-August 2001. View at Publisher · View at Google Scholar
  25. C. Izurieta and J. M. Bieman, “A multiple case study of design pattern decay, grime, and rot in evolving software systems,” Software Quality Journal, vol. 21, no. 2, pp. 289–323, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. Q. Yang, J. J. Li, and D. M. Weiss, “A survey of coverage-based testing tools,” The Computer Journal, vol. 52, no. 5, pp. 589–597, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. S. M. A. Shah, M. Torchiano, A. Vetrò, and M. Morisio, “Exploratory testing as a source of technical debt,” IT Professional, vol. 16, no. 3, Article ID 6475929, pp. 44–51, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Forward and T. C. Lethbridge, “The relevance of software documentation, tools and technologies,” in Proceedings of the ACM Symposium on Document Engineering (DocEng '02), pp. 26–33, ACM, New York, NY, USA, November 2002. View at Publisher · View at Google Scholar
  29. G. Travassos, F. Shull, M. Fredericks, and V. R. Basili, “Detecting defects in object-oriented designs: using reading techniques to increase software quality,” in Proceedings of the 14th ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA '99), pp. 47–56, ACM, Denver, Colo, USA, November 1999. View at Publisher · View at Google Scholar
  30. W. Snipes, B. Robinson, Y. Guo, and C. Seaman, “Defining the decision factors for managing defects: a technical debt perspective,” in Proceedings of the 3rd International Workshop on Managing Technical Debt (MTD '12), pp. 54–60, June 2012, http://www.scopus.com/record/display.url?eid=2-s2.0-84864135572&origin=inward&txGid=7200AE1F63AC9EDA2F928E7752950AC2.CnvicAmOODVwpVrjSeqQ%3a1.
  31. Y. Guo, C. Seaman, R. Gomes et al., “Tracking technical debt—an exploratory case study,” in Proceedings of the 27th IEEE International Conference on Software Maintenance (ICSM '11), pp. 528–531, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. C. Seaman, Y. Guo, N. Zazworka et al., “Using technical debt data in decision making: potential decision approaches,” in Proceedings of the 3rd International Workshop on Managing Technical Debt (MTD '12), pp. 45–48, IEEE, Zürich, Switzerland, June 2012. View at Publisher · View at Google Scholar
  33. K. Schmid, “A formal approach to technical debt decision making,” in Proceedings of the 9th International ACM Sigsoft Conference on the Quality of Software Architectures (QoSA '13), pp. 153–162, ACM, New York, NY, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. N. Zazworka, R. O. Spínola, A. Vetro, F. Shull, and C. Seaman, “A case study on effectively identifying technical debt,” in Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering (EASE '13), pp. 42–47, ACM, Porto de Galinhas, Brazil, April 2013. View at Publisher · View at Google Scholar · View at Scopus
  35. A. Charland and B. Leroux, “Mobile application development: web vs. native,” Communications of the ACM, vol. 54, no. 5, pp. 49–53, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. DB-Engines Ranking, March 2015, http://db-engines.com/en/ranking.
  37. OpenRefine, March 2015, http://openrefine.org/.
  38. Elasticsearch, 2015, https://www.elastic.co/products/elasticsearch/.
  39. F. Shull, “Perfectionists in a world of finite resources,” IEEE Software, vol. 28, no. 2, pp. 4–6, 2011. View at Publisher · View at Google Scholar · View at Scopus