Context: Code smells are symptoms of wrong design decisions or coding shortcuts that may
increase defect rate and decrease maintainability. Research on code smells is accelerating …

Software smells indicate design or code issues that might degrade the evolution and
maintenance of software systems. Detecting and identifying these issues are challenging …

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 …

Code smells indicate problems in design or code which makes software hard to change and
maintain. It has become a sign of software systems that cause complications in maintaining …

Artificial Intelligence (AI) and Machine Learning (ML) are pervasive in the current computer
science landscape. Yet, there still exists a lack of software engineering experience and best …

Context Code smells in the software systems are indications that usually correspond to
deeper problems that can negatively influence software quality characteristics. This review is …

As Deep learning (DL) systems continuously evolve and grow, assuring their quality
becomes an important yet challenging task. Compared to non-DL systems, DL systems have …

Different security issues are a common problem for open source packages archived to and
delivered through software ecosystems. These often manifest themselves as software …

Deep learning practitioners are often interested in improving their model accuracy rather
than the interpretability of their models. As a result, deep learning applications are inherently …

Context Code smell detection is the process of identifying poorly designed and implemented
code pieces. Machine learning-based approaches require enormous amounts of manually …