A Comprehensive Hybrid Model for Language-Independent Defect Prediction in Microservices Architecture


  • Yashwant Kumar Joint Director (IT, NIC), Department of Computing and Information Technology, Usha Martin University, Jharkhand, India
  • Vinay Singh Associate Professor, Department of Computing and Information Technology, Usha Martin University, Jharkhand, India




Software Defect Prediction, Machine Learning Approach, Predictive Accuracy, Hybrid Model, BiD-LSTM, BERT, ASTs


The transformation of software development from monolithic frameworks to microservices-based architectures, focusing on the challenges of creating a unified defect prediction model that spans various programming languages in practice of automating integration of code modification into a single codebase. It proposes a hybrid machine learning approach to enhance defect prediction accuracy by integrating different data sources and algorithms. The goal is to create a language and project-independent model. The hybrid model combines Bi-Directional LSTM (BiD-LSTM) networks and Attention mechanisms, static code metrics, and BERT-based language models. BiLSTM-Attention captures temporal dependencies within Abstract Syntax Trees (ASTs), static code metrics provide insights into software complexity, and BERT interprets textual context for a holistic understanding of code snippets. The research methodology involves quantitative techniques, starting with a literature review to establish the theoretical foundation. An empirical study follows, encompassing data gathering, feature crafting and pre-processing, model building, training and evaluation, validation and analysis and conclusions. The research’s insights aim to improve defect prediction techniques, contributing to software engineering’s pursuit of better quality and reliability.


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How to Cite

Kumar, Y., & Singh, V. (2023). A Comprehensive Hybrid Model for Language-Independent Defect Prediction in Microservices Architecture. Asian Journal of Computer Science and Technology, 12(2), 48–65. https://doi.org/10.51983/ajcst-2023.12.2.3763