Recommender Model for Secure Software Engineering using Cosine Similarity Measures
Astrit Desku1, Bujar Raufi2, Artan Luma3, Besnik Selimi4
1Astrit Desku*, Faculty of Contemporary Sciences and Technologies, South East European University, Tetovo, North Macedonia.
2Bujar Raufi, Faculty of Contemporary Sciences and Technologies, South East European University, Tetovo, North Macedonia.
3Artan Luma, Faculty of Contemporary Sciences and Technologies, South East European University, Tetovo, North Macedonia.
4Besnik Selimi, Faculty of Contemporary Sciences and Technologies, South East European University, Tetovo, North Macedonia.
Manuscript received on 30 May 2022. | Revised Manuscript received on 01 June 2022. | Manuscript published on 30 June 2022. | PP: 144-148 | Volume-11 Issue-5, June 2022. | Retrieval Number: 100.1/ijeat.E36280611522 | DOI: 10.35940/ijeat.E3628.0611522
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Recommender Systems, Cosine Similarity, Software Engineering.
Scope of the Article: Software Engineering Methodologies