Developing a hyperparameter optimization method for classification of code snippets and questions of stack overflow: HyperSCC
EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS, cilt.10, 2023 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 10
- Basım Tarihi: 2023
- Doi Numarası: 10.4108/eai.27-5-2022.174084
- Dergi Adı: EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Directory of Open Access Journals
- Anahtar Kelimeler: Multi-label classification, hyperparameter optimization, programming language prediction, MULTI-LABEL CLASSIFICATION, STACKOVERFLOW
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Süleyman Demirel Üniversitesi Adresli: Evet
Özet
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.