This article is a spin-off piece from the paper “Do We Need Improved Code Quality Metrics?” by myself and Prof. Diomidis Spinellis.
Earlier, I wrote an article about issues with code quality metrics; in another article I presented how all the variants of LCOMmetric misrepresent simple cases of class …
This article is a spin-off piece from the paper "Do We Need Improved Code Quality Metrics?" by myself and Prof. Diomidis Spinellis.
Software metrics have always been on a roller-coaster ride. On one hand, researchers and practitioners have adopted metrics not only to reveal quality characteristics of their programs, but …
Code smells indicate the presence of quality issues in source code. An excessive number of smells make a software system hard to evolve and maintain. In this article, we apply deep learning models based on CNN and RNN to detect code smells without extensive feature engineering, just by feeding the …
Poor design quality and huge technical debt are common issues perceived in real-life software projects. Carrying out a software design quality assessment effectively and adopting the recommendations from it could improve the design quality. A comprehensive design assessment requires us to know the requirements in detail and weight different design …
Writing high-quality code is not at all trivial and there is no shortcut to “add” quality in source-code. Quality is like spices in food; you need to add them at the right moment. You can’t just throw the spices in the vessel once the food is (almost) cooked and …
If code-quality is one of your concerns, you must be aware of code smells and design smells. Similarly, code written in configuration management systems such as Puppet and Chef is also prone to such maintainability smells.
Recently, we (myself, Prof. Diomidis Spinellis, and Marios Fragkoulis) have worked on configuration smells …
Machine Learning for Source Code Analysis (ML4SCA)