# Transpilers: A Systematic Mapping Review of Their Usage in Research and Industry
## 6 Discussion
### Discussion about Research Questions  
* RQ1: What are the descriptive statistics of the publications among selected articles? As seen in the data analysis section, it has been possible to make a broad statistical description of the studies selected for this study. From this it is possible to highlight the following:
* A total of 683 articles were selected, considered as the primary studies or the ones that met the search chains and applicable criteria. * The terms most found in the analysis, which refer to a particular research area, correspond to the following: performance, parallel, architecture, optimization, software, hardware, development, memory, GPU, and test. * As mentioned in Figure 5, most of the articles come from the Scopus database, after removing duplicates. * In Table 6 it is possible to see the articles that refer to transpilers, which have the largest number of references. * Figure 6 and the Equation (1) show that the trend of publications regarding transpilers has remained stable. Through a linear regression, it has been possible to see that the trend is slightly downward, which indicates that it is expected that there will be publications on the subject in a stable manner. * According to Figure 7, it has been possible to see that practically half of the selected articles have been published in scientific journals and the other half in scientific conferences. * In Table 8 it is possible to see the journals most used for publications related to transpilers. In Table 9 it is possible to see the conferences most used for publications related to transpilers. * In Tables 10 and 11 it is possible to see the most recurrent authors in the publication of articles in relation to transpilers. * In Table 13 it is possible to see the relationship of greater co-occurrence of terms in the same articles, allowing the determination of the most common research areas. * In Table 15, it is possible to see the programming languages most used in relation to transpilers. * Table 16 shows the most commonly used transpiler synonyms in scientific articles. * RQ2: In which scenarios transpilers are most used? Referring to Table 14, the major research areas in relation to the use of transpilers can be evidenced. From this it has been possible to see that the scenarios where transpilers are mostly used are the following:
* To improve performance in algorithms related to high-performance computing (HPC). * For preparation for parallel execution, mainly in relation to the transformation of sequential methods to algorithms prepared to take advantage of the GPU, CUDA cores, OpenMP, and others. * For the creation of automatic transformations between programming languages commonly used in the industry. * As preparation of methods to support different processor architectures. * As part of the internal procedures of a compiler and its execution platforms. * As an element to facilitate the processes of diagnosis and execution of software testing exercises. * To improve graphic processing procedures. * To generate compatibility with embedded systems so that the common code can be transferred to the specific languages of the specific circuits. * As part of the source code generation architecture. * As a constituent element in artificial intelligence methods. * As a base for the transformation of code for preparation of execution of the front-end of applications. * As part of the compatibility processes with the different mobile platforms. * As a method to transform sentences between parameterization languages, configuration or platform of network devices. * As a means to transfer, transform, or reuse mathematical models. * As a compatibility tool for the different platforms for the creation and execution of digital games. * RQ3: In which kind of industry applications transpilers are commonly used? As it is possible to see in Section 4 of this document, the applications in the industry are very varied and of diverse uses.