Abstract
The target of this research is to develop an automatic and quantitative methodology and tool combination using text analyses, data mining and machine learning for the analyses of process oriented international quality approaches and documented quality systems of organizations in the field of software development. Such comparisons require at the moment lots of engineering work by experts thus resulting in inefficient human resource utilization. Our long-term goal is to have a tool that enables the auditors and other stakeholders in a software organization to perform quantitative and automatic pre-assessment about the conformance of the organizations’ documented quality systems compared to international quality approach(es) with efficient human resource utilization. This article is presenting the results of searching for the optimal methodology via comparing CMMI-DEV 1.3 and HiS Scope of Automotive Spice 2.5 standards and creating similarity maps.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34, 1–47 (2002)
Weiss, S.M., Indurkhya, N., Zhang, T., Damerau, F.J.: Text Mining, Predictive Methods for Analyzing Unstructured Information. Springer (2005)
Gharehchopogh, F.S., Khalifelu, Z.A.: Analysis and evaluation of unstructured data: text mining versus natural language processing. In: 5th International Conference on Application of Information and Communication Technologies (AICT) (2011)
Uchyigit, G.: Experimental evaluation of feature selection methods for text classification. In: 9th International Conference on Fuzzy Systems and Knowledge Discovery (2012)
Rahman, A.A., Sahibuddin, S., Ibrahim, S.: A Taxonomy Analysis for Multi-Model Process Improvement from the Context of Software Engineering Processes and Services. International Journal of Digital Content Technology and its Applications 6(22) (2012)
Kelemen, Z.D., Kusters, R., Trienekens, J., Balla, K.: Towards Complexity Analysis of Software Process Improvement Frameworks. Technical report (2013)
Peldzius, S., Ragaisis, S.: Investigation Correspondence between CMMI-DEV and ISO/IEC 15504. International Journal of Education and information technologies 5(4) (2011)
Peldzius, S., Ragaisis, S.: Framework for usage of multiple software process models. In: Mas, A., Mesquida, A., Rout, T., O’Connor, R.V., Dorling, A. (eds.) SPICE 2012. CCIS, vol. 290, pp. 210–221. Springer, Heidelberg (2012)
Kelemen, Z.D., Kusters, R., Trienekens, J., Balla, K.: A Data Model for Multimodel Process Improvement. Technical report (2013)
Bella, F., Hörmann, K., Vanamali, B.: From CMMI to SPICE – experiences on how to survive a spice assessment having already implemented CMMI. In: Jedlitschka, A., Salo, O. (eds.) PROFES 2008. LNCS, vol. 5089, pp. 133–142. Springer, Heidelberg (2008)
Baldassarre, M.T., Piattini, M., Pino, F.J., Visaggio, G.: Comparing ISO/IEC 12207 and CMMI-DEV: towards a mapping of ISO/IEC 15504-7. In: Proceedings of the Seventh ICSE Conference on Software Quality (2009)
VDA QMC: Automotive Spice Process Assessment Model, Process assessment using Automotive Spice in the development of software based system, 1st edn. (2008)
Jeners, S., Lichter, H.: Smart integration of process improvement reference models based on an automated comparison approach. In: McCaffery, F., O’Connor, R.V., Messnarz, R. (eds.) EuroSPI 2013. CCIS, vol. 364, pp. 143–154. Springer, Heidelberg (2013)
Jeners, S., Lichter, H., Pyatkova, E.: Automated comparison of process improvement reference models based on similarity metrics. In: 19th Asia-Pacific Software Engineering Conference (2012)
Kelemen, Z.D., Kusters, R., Trienekens, J., Balla, K.: Towards Applying Text Mining Techniques on Software Quality Standards and Models. Technical report (2013)
Moens, M.-F.: Information Extraction: Algorithms and Prospects in a Retrieval Context. Springer (2006)
Apte, C., Damerau, F.: Automated Learning of Decision Text Categorization. ACM Transactions on Information Systems 12(3), 233–251 (1994)
Dave, K., Taghva, K.: Study of feature selection algorithms for text-categorization. UNLV Theses, University of Nevada (2011)
Peldzius, S., Ragaisis, S.: Usage of multiple process assessment models. In: Woronowicz, T., Rout, T., O’Connor, R.V., Dorling, A. (eds.) SPICE 2013. CCIS, vol. 349, pp. 223–234. Springer, Heidelberg (2013)
Ehsan, N., Perwaiz, A., Arif, J., Mirza, E., Ishaque, A.: CMMI / SPICE based Process Improvement. In: Proceedings of the 2010 IEEE ICMIT (2010)
Lan, M., Sun, S.-Y., Low, H.-B., Tan, C.-L.: A comparative study on term weighting schemes for text categorization neural networks. Proceedings of the IJCNN 2005 1, 546–551 (2005)
Cha, S.-H.: Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions. International Journal of Mathematical Models and Methods in Applied Sciences 1(4), 300 (2007)
Huang, A.: Similarity measures for text document clustering. In: New Zealand Computer Science Research Student Conference (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Karaffy, Z., Balla, K. (2015). Applying Text Analyses and Data Mining to Support Process Oriented Multimodel Approaches. In: O’Connor, R., Umay Akkaya, M., Kemaneci, K., Yilmaz, M., Poth, A., Messnarz, R. (eds) Systems, Software and Services Process Improvement. EuroSPI 2015. Communications in Computer and Information Science, vol 543. Springer, Cham. https://doi.org/10.1007/978-3-319-24647-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-24647-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24646-8
Online ISBN: 978-3-319-24647-5
eBook Packages: Computer ScienceComputer Science (R0)