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The recommendation systems plays an important role in today’s life as it assist in reliable selection of common utilities. The code recommendation system is being used by the code databases (GitHub, source frog etc.) aiming to recommend the more appropriate code to the users. There are several factors that could negatively impact the performance of code recommendation systems (CRS). This study aims to empirically explore the challenges that could have critical impact on the performance of the CRS. Using systematic literature review and questionnaire survey approaches, 19 challenges were identified. Secondly, the investigated challenges were further prioritized using fuzzy-AHP analysis. The identification of challenges, their categorization and the fuzzy-AHP analysis provides the prioritization-based taxonomy of explored challenges. The study findings will assist the real-world industry experts and to academic researchers to improve and develop the new techniques for the improvement of CRS.

Multicriteria decision making taxonomy of code recommendation system challenges: a fuzzy-AHP analysis