Developing a hyperparameter optimization method for classification of code snippets and questions of stack overflow: HyperSCC

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ÖZTÜRK M. M.

EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS, vol.10, 2023 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 10
  • Publication Date: 2023
  • Doi Number: 10.4108/eai.27-5-2022.174084
  • Journal Name: EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Directory of Open Access Journals
  • Keywords: Multi-label classification, hyperparameter optimization, programming language prediction, MULTI-LABEL CLASSIFICATION, STACKOVERFLOW
  • Süleyman Demirel University Affiliated: Yes

Abstract

Although there exist various machine learning and text mining techniques to identity the programming language of complete code files, multi-label code snippet prediction was not considered by the research community. This work aims at devising a tuner for multi-label programming language prediction of stack overflow posts. To that end, a Hyper Source Code Classifier (HyperSCC) is devised along with rule-based automatic labeling by considering the bottlenecks of multi-label classification. The proposed method is evaluated on seven multi-label predictors to conduct an extensive analysis. !Ile method is further compared with the three competitive alternatives in terms of one-label programming language prediction. HyperSCC outperformed the other methods in terms of the H score. Preprocessing results in a high reduction (50%) of training time when ensemble multi-label predictors are employed. In one-label programming language prediction, Gradient Boosting Machine (gbm) yields the highest accuracy (0.99) in predicting R posts that have a lot of distinctive words determining labels. The findings support the hypothesis that multi-label predictors can be strengthened with sophisticated feature selection and labeling approaches.