Technologies for GQM-Based Metrics Recommender Systems: A Systematic Literature Review

Abstract

Purpose: With this Systematic Literature Review (SLR), we aim to discover technologies to construct a Goal-Question-Metrics (GQM) based metrics recommender for software developers. Since such a system has not yet been described in the literature, we decided to analyse the technologies used in three main components of recommender systems - data sets, algorithms, and recommendations - independently. Methods: To achieve our goal we performed - following the best norms in our discipline - a systematic literature review (SLR). We first identified, through searches aptly performed, 422 potentially relevant papers, from which we selected - after applying inclusion and exclusion criteria - 30 papers, which we eventually included in our final log. Results: Systems with textual data set preprocess information in nearly the same way and the majority use similarity scores to create recommendations. Systems with GQM-based algorithms consist of questionnaires and require users to explicitly answer questions to produce suggestions. With respect to the recommendations of reviewed systems, they range from application programming interfaces (APIs) to requirements, but no system presently recommends metrics. Conclusion: In our SLR we: (a) identified a sequence of the most popular steps for preprocessing in recommender systems; (b) proposed an optimisation strategy for such steps; (c) found out that the most promising approach includes both ranking and classification; and (d) established that there are no recommendation systems developed to date to process metrics.

Keywords

• Goal question metrics model
• recommender systems
• text processing