Prior works have developed transformer-based language learning models to automatically
generate source code for a task without compilation errors. The datasets used to train these …

This paper presents a systematic literature review of deep learning (DL)-based software
refactoring, which involves restructuring and simplifying code without altering its external …

Featured Application The potential application of this study is to guide developers and
organizations in effectively detecting and refactoring code smells, enhancing the quality and …

In recent years, the use of automated source code generation utilizing transformer-based
generative models has expanded, and these models can generate functional code …

Test flakiness is a phenomenon occurring when a test case is non-deterministic and exhibits
both a passing and failing behavior when run against the same code. Over the last years …

Code smells indicate design issues that violate basic design principles such as hierarchy
encapsulation, abstraction, and others, potentially affecting software quality [1],[2]. Detecting …

The difficulty of software development tasks depends on several factors including the
characteristics of the underlying source-code. These characteristics can be captured and …

Python is an increasingly popular programming language used in various software projects
and domains. Code smells in Python significantly influences the maintainability …

Software maintenance is an imperative step in software development. Code smells can arise
as a result of poor design as well as frequent code changes due to changing needs. Early …

Reinforcement Learning (RL) is being increasingly used to learn and adapt application
behavior in many domains, including large-scale and safety critical systems, as for example …