Using Artificial Intelligence Techniques to Enhance Traceability Links

André Di Thommazo, Rafael Rovina, Thiago Ribeiro, Guilherme Olivatto, Elis Hernandes, Vera Werneck, Sandra Fabbri

2014

Abstract

One of the most commonly used ways to represent requirements traceability is the requirements traceability matrix (RTM). The difficulty of manually creating it motivates investigation into alternatives to generate it automatically. This article presents two approaches to automatically creating the RTM using artificial intelligence techniques: RTM-Fuzzy, based on fuzzy logic and RTM-N, based on neural networks. They combine two other approaches, one based on functional requirements entry data (RTM-E) and the other based on natural language processing (RTM-NLP). The RTMs were evaluated through an experimental study and the approaches were improved using a genetic algorithm and a decision tree. On average, the approaches that used fuzzy logic and neural networks to combine RTM-E and RTM-NLP had better results compared with RTM-E and RTM-NLP singly. The results show that artificial intelligence techniques can enhance effectiveness for determining the requirement’s traceability links.

References

  1. Artero, A. O. 2009. Artificial intelligence - theory and practice. , 1st ed., São Paulo: Livraria Fisica.
  2. Baeza-Yates, R., Berthier, A., Ribeiro-Neto, A. 1999. Modern Information Retrieval. 1st ed. New York: ACM Press / Addison-Wesley.
  3. Cleland-Huang, J., Gotel, O., Zisman, A. 2012. Software and Systems Traceability. 1st ed., London: Springer.
  4. Coppin, B. 2004. Artificial Intelligence Illuminated, 1st ed. Burlington: Jones and Bartlett Publishers.
  5. Cuddeback, D., Dekhtyar, A., Hayes, J.H. (2010). Automated requirements traceability: the study of human analysts. In 18th IEEE International Requirements Engineering Conference, RE. Sydney, Australia, Sep.: IEE Computer Society. 231-241.
  6. Cysneiros, Zisman, A. (2008). Traceability and completeness checking for agent oriented systems. In ACM Symposium on Applied Computing, Fortaleza, Brasil, New York: ACM Digital Library. 71-77.
  7. Deeptimahanti, D. K., Sanyal, R. (2011). Semi-automatic generation of UML models from natural language requirements. In India Software Engineering Conf.. Kerala, India, Feb. New York: ACM Digital Library. 165-174.
  8. Deerwester, S., Dumais, S.T., Furnas, G. W., Landauer, T.K., Harshman, R. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407.
  9. Di Thommazo, A., Martins, M. D. C., Fabbri, S.C.P.F. (2007). Requirements management in COCAR environment (in portuguese). In Requirements Engineering Workshop, WER, Toronto, Canada, May. Rio de Janeiro: PUC-Rio. 11-23.
  10. Di Thommazo, A., Malimpensa, G., Olivatto, G., Ribeiro T., Fabbri, S. (2012). Requirements traceability matrix: automatic generation and visualization. In 26th Brazilian Symposium on Software Engineering, Natal, Brazil. May.: IEE Computer Society. 101-110.
  11. Di Thommazo, A., Ribeiro, T., Olivatto, G., Rovina, R., Werneck, V., Fabbri, S. (2013). Detecting traceability links through neural networks. In 25th International Conference on Software Engineering and Knowledge Engineering, SEKE, Boston, USA. July. Illinois: Knowledge Systems Institute, 2013. 36-41.
  12. Goknil, A., 2011. Semantics of trace relations in requirements models for consistency checking and inferencing. Software and Systems Modeling, 31-54.
  13. Guo, Y, Yang, M., Wang, J., Yang, P., Li, F. (2009). An ontology based improved software requirement traceability matrix. In 2nd International Symposium on Knowledge Acquisition and Modeling, KAM, China, Dec. Los Alamitos: IIEE Computer Society. 160-163.
  14. Hayes, J. H., Dekhtyar, A., Osborne, J. (2003). Improving requirements tracing via information retrieval. In 11th International IEEE Requirements Engineering Conference, Monterey, CA, Sep. Los Alamitos: IEE Computer Society. 138-147.
  15. Hayes, J. H., Dekhtyar, A., Sundaram, S. 2006. Advancing candidate link generation for requirements tracing: the study of methods. IEEE Transactions on Software Engineering, 32(1), 4-19.
  16. Herrera F. 2008. Genetic fuzzy systems: taxonomy, current research trends and prospects. Evolutionary Intelligence , Volume 1, Issue 1 , pp 27-46 DOI 10.1007/s12065-007-0001-5.
  17. IBM, Ten Steps to Better Requirements Management. [Online]. Available at: http://public.dhe.ibm.com/ common/ssi/ecm/en/raw14059usen/RAW14059USEN .PDF. [20 May 2012].
  18. Kannenberg, A., Saiedian, A. 2009. Why software requirements traceability remains a challenge: CrossTalk. The Journal of Defense Software Engineering, 14-19.
  19. Kawai, K. K., 2005. Guidelines for preparation of requirements document with emphasis on the functional requirements (in Portuguese). Master degree thesis. São Carlos, Brazil: Universidade Federal de São Carlos.
  20. McCulloch , W. S., Pitts, W. 1943. A logical calculus of the ideas imminent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133.
  21. Real , R., Vargas, J. M. 1996. The probabilistic basis of Jaccard's index of similarity. Systematic Biology, [Online]. 45(3), 380-385. Available at: http://sysbio.oxfordjournals.org/content/45/3/380.full [Accessed 31 July 2013].
  22. Salem, A. M., (2006). Improving software quality through requirements traceability models. In 4th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA, .Dubai, Sharjah, March, Los Alamitos: IEE Computer Society. 1159-1162.
  23. Salton, G., Allan, J. (1994). Text retrieval using the vector processing model. In 3rd Symposium on Document Analysis and Information Retrieval, Univ.of Nevada,
  24. Sommerville, I. 2010. Software Engineering. 9th ed. New York: Addison Wesley.
  25. Standish Group, CHAOS Report 2005, 2005. [Online]. Available at: http://www.standishgroup.com/sample _research/PDFpages/q3-spotlight. [Accessed 20 July 2013].
  26. Standish Group, CHAOS Report 1994, 1994. [Online]. Available at: http://www.standishgroup.com/sample _research/chaos_1994_2.php. [Accessed 20 July 2013].
  27. Sundaram, S. K. A. Hayes, J. H. B., Dekhtyar, A. C., Holbrook, E. A. D., (2010). Assessing traceability of software engineering artifacts. In 18th International IEEE Requirements Engineering Conference, Sydney, Australia, Sep.: IEE Computer Society. 313-335.
  28. Zadeh, L. A. 1965. Fuzzy sets. Information Control, 8(1), 338-353.
  29. Zisman, A., Spanoudakis, G. 2004. Software Traceability: Past, Present & Future, Requirenautics Quarterly, 13, Newsletter of the Requirements Engineering Specialist Group of the British Computer Society, Sep.
  30. Wang, X., Lai, G., Liu, C. 2009. Recovering relationships between documentation and source code based on the characteristics of software engineering. Electronic Notes in Theoretical Computer Science, 243(1),
Download


Paper Citation


in Harvard Style

Di Thommazo A., Rovina R., Ribeiro T., Olivatto G., Hernandes E., Werneck V. and Fabbri S. (2014). Using Artificial Intelligence Techniques to Enhance Traceability Links . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-028-4, pages 26-38. DOI: 10.5220/0004879600260038


in Bibtex Style

@conference{iceis14,
author={André Di Thommazo and Rafael Rovina and Thiago Ribeiro and Guilherme Olivatto and Elis Hernandes and Vera Werneck and Sandra Fabbri},
title={Using Artificial Intelligence Techniques to Enhance Traceability Links},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2014},
pages={26-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004879600260038},
isbn={978-989-758-028-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Using Artificial Intelligence Techniques to Enhance Traceability Links
SN - 978-989-758-028-4
AU - Di Thommazo A.
AU - Rovina R.
AU - Ribeiro T.
AU - Olivatto G.
AU - Hernandes E.
AU - Werneck V.
AU - Fabbri S.
PY - 2014
SP - 26
EP - 38
DO - 10.5220/0004879600260038