Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …

Predictive models are one of the most important techniques that are widely applied in many
areas of software engineering. There have been a large number of primary studies that …

Large language models for code (LLM4Code), which demonstrate strong performance (eg,
high accuracy) in processing source code, have significantly transformed software …

Technical debt refers to taking shortcuts to achieve short-term goals while sacrificing the
long-term maintainability and evolvability of software systems. A large part of technical debt …

Recent years have witnessed increasing interest in code representation learning, which
aims to represent the semantics of source code into distributed vectors. Currently, various …

Artificial intelligence (AI) has witnessed an exponential increase in use in various
applications. Recently, the academic community started to research and inject new AI-based …

This paper provides a starting point for Software Engineering (SE) researchers and
practitioners faced with the problem of training machine learning models on small datasets …

Abstract Self-Admitted Technical Debt (SATD) is primarily studied in Object-Oriented (OO)
languages and traditionally commercial software. However, scientific software coded in …

Software reuse is a popular way to utilize existing software components to ensure the quality
of newly developed software in service-oriented architecture. However, how to find a …

Given the increasing complexity and volume of Self‐Admitted Technical Debts (SATDs), how
to efficiently detect them becomes critical in software engineering practice for improving …