Published June 30, 2022 | Version CC BY-NC-ND 4.0
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Recommender Model for Secure Software Engineering using Cosine Similarity Measures

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  • 1. Faculty of Contemporary Sciences and Technologies, South East European University, Tetovo, North Macedonia.

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  • 1. Faculty of Contemporary Sciences and Technologies, South East European University, Tetovo, North Macedonia.

Description

Abstract: One of the essential components of Recommender Systems in Software Engineering is a static analysis that is answerable for producing recommendations for users. There are different techniques for how static analysis is carried out in recommender systems. This paper drafts a technique for the creation of recommendations using Cosine Similarity. Evaluation of such a system is done by using precision, recall, and so-called Dice similarity coefficient. Ground truth evaluations consisted of using experienced software developers for testing the recommendations. Also, statistical T-test has been applied in comparing the means of the two evaluated approaches. These tests point out the significant difference between the two compared sets.

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Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.

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Journal article: 2249-8958 (ISSN)

References

  • B. D. Vijay Kotu, "Recommendation Engines," in Data Science - Concepts and Practice (Second Edition), Morgan Kaufmann, 2019, pp. 343-394.
  • M. Bruch, M. Monperrus and M. Mezini, "Learning from Examples to Improve Code Completion Systems," in International Symposium on the Foundations of Software, 2009.
  • M. P. Robillard, W. Maalej, R. J. Walker and T. Zimmermann, Recommendation Systems in Software Engineering, Heidelberg New York Dordrecht London: Springer-Verlag, 2014.
  • A. Whitten and J. D. Tygar, "Why Johnny can't encrypt: a usability evaluation of PGP 5.0," in Proceedings of the 8th conference on USENIX Security Symposium - Volume 8, Washington, 1999.
  • Bhatti, U. A., Huang, M., Wu, D., Zhang, Y., Mehmood, A., & Han, H. (2019). Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterprise information systems, 13(3), 329-351.
  • Sahoo, A. K., Mallik, S., Pradhan, C., Mishra, B. S. P., Barik, R. K., & Das, H. (2019). Intelligence-based health recommendation system using big data analytics. In Big data analytics for intelligent healthcare management (pp. 227-246). Academic Press.
  • Gasparic, M., & Janes, A. (2016). What recommendation systems for software engineering recommend: A systematic literature review. Journal of Systems and Software, 113, 101-113.
  • Pakdeetrakulwong, U., Wongthongtham, P., & Siricharoen, W. V. (2014, December). Recommendation systems for software engineering: A survey from software development life cycle phase perspective. In The 9th International Conference for Internet Technology and Secured Transactions (ICITST-2014) (pp. 137-142). IEEE.
  • Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., & Stettinger, M. (2014). Basic approaches in recommendation systems. In Recommendation Systems in Software Engineering (pp. 15-37). Springer, Berlin, Heidelberg.
  • Desku, Astrit, et al. "Cosine Similarity through Control Flow Graphs For Secure Software Engineering." 2021 International Conference on Engineering and Emerging Technologies (ICEET). IEEE, 2021.
  • M. P. Robillard, R. J. Walker and T. Zimmermann, "Development tools: Recommendation Systems for Software Engineering," I E E E S O F T WA R E www. c omp u t e r . o r g / s o f tw a r e.
  • Fiarni, Cut, and Herastia Maharani. "Product Recommendation System Design Using Cosine Similarity and Content-based Filtering Methods." IJITEE (International Journal of Information Technology and Electrical Engineering) 3.2 (2019): 42-48
  • Huang, B. H., & Dai, B. R. (2015, June). A weighted distance similarity model to improve the accuracy of collaborative recommender system. In 2015 16th IEEE International Conference on Mobile Data Management (Vol. 2, pp. 104-109). IEEE
  • Nawar, A., Toma, N. T., Al Mamun, S., Kaiser, M. S., Mahmud, M., & Rahman, M. A. (2021, October). Cross-Content Recommendation between Movie and Book using Machine Learning. In 2021 IEEE 15th International Conference on Application of Information and Communication Technologies (AICT) (pp. 1-6). IEEE.
  • Ristanti, Putri Yuni, Aji Prasetya Wibawa, and Utomo Pujianto. "Cosine similarity for title and abstract of economic journal classification." 2019 5th International Conference on Science in Information Technology (ICSITech). IEEE, 2019.
  • Kohila, D. K. A. R. "Text Mining: Text Similarity Measure for News Articles Based On Global." Glob. J. Eng. Sci. Res. Manag. 3.7 (2016): 35-42.
  • J. A. Harer, L. Kim, R. L. Russell, O. Ozdemir, O. Ozdemir, E. Antelman and S. Chin, "Automated software vulnerability detection with machine learning," in ResearchGate, 2018.
  • Iriananda, Syahroni Wahyu. "Measure the Similarity of Complaint Document Using Cosine Similarity Based on Class-Based Indexing." International Journal of Computer Applications Technology and Research, Volume 7–Issue 08, 292-296, 2018
  • Salton, G.: Automatic Text Processing: The Transformation, Analysis, and Retrieval ofInformation by Computer. (1989) Addison-Wesley Longman Publishing, Boston, MA.
  • Li M., Chen X., Li X., Ma B., and Vitanyi P. M.B. : The Similarity Metric. IEEE Transactions on Information Theory, 50(12): 3250-3264 (2004)
  • K. Vijay and D. Bala, "Classification," in Data Science (Second Edition), organ Kaufmann, 2019, pp. 65-163.
  • Li, B., & Han, L. (2013, October). Distance weighted cosine similarity measure for text classification. In International conference on intelligent data engineering and automated learning (pp. 611-618). Springer, Berlin, Heidelberg.
  • B. Li and L. Han, "Distance Weighted Cosine Similarity Measure for Text Classification," in International Conference on Intelligent Data Engineering and Automated Learning, Berlin, Heidelberg, 2013.
  • K. Ottenstein and L. Ottenstein, "The program dependence graph in a software development environment," ACM Sigplan Notices, 1984.
  • M. Bruch, T. Schäfer and M. Mezini, "On Evaluating Recommender Systems for API Usages," in Proceedings of the 2008 international workshop on Recommendation systems for software engineering, 2008.
  • M. Bruch, M. Monperrus and M. Mezini, "Learning from Examples to Improve Code Completion Systems," in International Symposium on the Foundations of Software, 2009.
  • Kohila, D. K. A. R. "Text Mining: Text Similarity Measure for News Articles Based On Global." Glob. J. Eng. Sci. Res. Manag. 3.7 (2016): 35-42.

Subjects

ISSN: 2249-8958 (Online)
https://portal.issn.org/resource/ISSN/2249-8958#
Retrieval Number: 100.1/ijeat.E36280611522
https://www.ijeat.org/portfolio-item/E36280611522/
Journal Website: www.ijeat.org
https://www.ijeat.org
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
https://www.blueeyesintelligence.org