Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions

  • Eliakim Gama UECE
  • Mariela I. Cortés UECE
  • Matheus Paixao UECE
  • Adson Damasceno UECE


In today’s fast-paced software industry, understanding and managing Technical Debt (TD) is crucial for software development. TD can compromise the long-term quality of software systems. The occurrence of TD is commonly reported and discussed by practitioners on Question and Answers (Q&A) platforms, such as Stack Overflow (SO). Data from Q&A platforms has been leveraged by the TD research community, most prominently regarding knowledge extraction. However, manual analyses of such data not only require considerable effort but also suffer from biases. Hence, this paper aims to propose an automated approach for identifying and classifying types of TD in SO discussions using machine learning (ML) and natural language processing. We divided our methodology into four main steps: i) data preprocessing, ii) application of natural language processing, iii) application of ML algorithms, and iv) computing the evaluation metrics for the proposed models. Our results indicate that ML algorithms have the potential to be successfully applied to automatically identify and classify TD types on SO discussions.We achieved a recall of 85% for test debt and a precision of 78% for design debt. Furthermore, the results of automated TD identification on SO benefit the software development community by enhancing solution quality, raising awareness of best practices, and facilitating collaboration among developers. This leads to more efficient development and the promotion of consistent standards. We make our entire dataset and pre-trained models available to encourage future research directions.

Palavras-chave: Technical Debt, Stack Overflow, Machine Learning


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GAMA, Eliakim; CORTÉS, Mariela I.; PAIXAO, Matheus; DAMASCENO, Adson. Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions. In: WORKSHOP BRASILEIRO DE ENGENHARIA DE SOFTWARE INTELIGENTE (ISE), 3. , 2023, Campo Grande/MS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 25-30. DOI: