Context: Several tertiary studies have criticized the reporting of software engineering
secondary studies. Objective: Our objective is to identify guidelines for reporting software …

The Internet has revolutionized the way information is retrieved, and the increase in the
number of users has resulted in a surge in the volume and heterogeneity of available data …

Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …

Code smells are the result of not following software engineering principles during software
development, especially in the design and coding phase. It leads to low maintainability. To …

Context Code smells are patterns in source code associated with an increased defect rate
and a higher maintenance effort than usual, but without a clear definition. Code smells are …

Code smells violate software development principles that make the software more prone to
errors and changes. Researchers have developed code smell detectors using manual and …

Code smells are poorly designed code structures indicating that the code may need to be
refactored. Recognizing code smells in practice is complex, and researchers strive to …

Abstract Background: Developers use Static Analysis Tools (SATs) to control for potential
quality issues in source code, including defects and technical debt. Tool vendors have …

Abstract EXplainable Artificial Intelligence (XAI) aims at improving users' trust in black-boxed
models by explaining their predictions. However, XAI techniques produced unreasonable …

Code smells are structures in code that may indicate maintainability issues. They are
challenging to define, and software engineers detect them differently. Mitigation of this …