The approach development of data extraction from lambda terms

Abstract

The study's object is the process of extracting the characteristics of lambda terms, which indicate the optimality of the reduction strategy and increase the productivity of compilers and interpreters. The solution to the problem of extracting specific strategy priority data from lambda terms using Machine Learning methods was considered. Such data was extracted using the large language model Microsoft CodeBERT, which was trained to solve the problem of summarizing the software code. The resulting matrices of embeddings were used to obtain vectors of average embeddings of size 768 and a latent space of size 8 thousand. Further, vectors of average embeddings were used for cluster analysis using the DBSCAN and Hierarchical Agglomerative clustering methods. The most informative variables affecting clustering were determined. Next, the clustering results were compared with the priorities of reduction strategies, which showed the impossibility of separating terms with RI priority. A feature of the obtained results is using machine learning methods to obtain knowledge. The clustering results showed many of the same informative variables, which is explained by the similar shape of the obtained clusters. The results of comparing the clustering values with the real priority are explained by the impossibility of clearly determining the priority and the use of the Microsoft CodeBERT model, which was not trained for the analysis of lambda terms. The proposed approach can find application in the development of compilers and interpreters of functional programming languages, allowing to analyze the code and extract important data to optimize the execution of programs. The obtained data can be used to develop rules aimed at improving the efficiency of compilation and interpretation