Automated Detection of Code Smells for Machine Learning Applications

The popularity of machine learning has wildly expanded in recent years. Machine learning techniques have been heatedly studied in academia and applied in the industry to create business value. However, there is a lack of guidelines for code quality in machine learning applications. Although machine learning code is usually integrated as a small...

The effect of EHOP on the writing of Program Analyzers

Effect Handler Oriented Programming is a promising new programming paradigm, delivering separation of of concerns with regards to side effects in an otherwise functional environment.<br/>This paper discusses the applicability of this new paradigm to static code analysis programs. <br/>Different code analyzers often have many similar, if not...

The Prevalence of Code Smells in Machine Learning Projects

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 practices in this field. One such best practice, static code analysis, can be used to find code smells, i.e., (potential) defects in the source code,...

Studying the Machine Learning Lifecycle and Improving Code Quality of Machine Learning Applications

As organizations start to adopt machine learning in critical business scenarios, the development processes change and the reliability of the applications becomes more important. To investigate these changes and improve the reliability of those applications, we conducted two studies in this thesis. The first study aims to understand the evolution...