'Project smells' - Experiences in Analysing the Software Quality of ML Projects with mllint

Machine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes...

Can We Predict Types of Code Changes? An Empirical Analysis

Preprint of paper published in: 9th IEEE Working Conference on Mining Software Repositories (MSR), 2-3 June 2012; doi:10.1109/MSR.2012.6224284 There exist many approaches that help in pointing developers to the change-prone parts of a software system. Although beneficial, they mostly fall short in providing details of these changes. Fine-grained...