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.
Any software project needs metrics to measure closeness to the goal. However, it is a complicated task to identify appropriate and goal-focused metrics by hand. That is why this chapter presents the GQM-based metrics recommender—a recommender system that can automatically deduce metrics from the project goals and questions.
Анотація -Анализ использования судоходными компаниями программного обеспечения для управления активами на судах позволил выработать концептуальные рекомендации по выбору и адаптации программного обеспечения.Abstract -Analysis of the software usage by shipping companies for asset management on ships allowed to develop the following conceptual recommendations for the selection and adaptation of software.
Purpose: The aim of this SLR is to look at recommendation systems which receive textual information as an input. By analysing them it is possible to understand how the textual information is preprocessed and which algorithms are then used to generate recommendations. Methods: With the Search Query I frst identifed 487 papers, from which 65 were removed as duplicates. After the IC and EC application, 28 articles remained as relevant. Results: From these articles’ analysis, it was found that the most commonly used pre-processing techniques are tokenization, TF-IDF, and stopwords removal. I also determined that all algorithms for suggestions generation in such systems can be divided into 4 categories: classifcation, ranking, clustering, and heuristic-based algorithms. In the last step I found that the most frequent output of such systems are API, code, and workers suggestions. Conclusion: With this work, I looked at which pre-processing techniques are used in the text-based recommender systems for software developers and which are the most common. I have also looked at the classifcation of algorithms for such recommendation systems. Finally, I considered what kind of objects are recommended by these text-based recommendation systems.
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