Dataset: an empirical study on architectural smells through a pipeline for continuous technical debt assessment

<h2><strong>Dataset of the study "An empirical study on architectural smells through a pipeline for continuous technical debt assessment"</strong></h2> <h3><strong>Abstract</strong></h3> <p>In recent years, researchers spent an increasing amount of effort investigating technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.</p> <p>However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.<br>In response to this concern, researchers strive to expand the scope of their studies by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.</p> <p>To address this issue, this paper presents an approach for tackling this problem and enabling researchers to study architectural smells, a well-known indicator of architectural technical debt, &nbsp;at a large scale. Specifically, we introduce a novel approach to a data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets with any static analysis tool.</p> <p>Finally, we use the data collected through the pipeline to study the correlation between architectural smells and logical coupling in order to understand how smells influence maintenance efforts.</p>