Registered user since Sat 21 Apr 2018
Contributions
Journal-first Papers
Tue 12 Sep 2023 11:30 - 11:42 at Room D - Infrastructure, Build, and Logs Chair(s): Fatemeh Hendijani Fard, Arie van DeursenDocker is a containerization technology that allows developers to ship software applications along with their dependencies in Docker images. Developers can extend existing images using them as base images when writing Dockerfiles. However, a lot of alternative functionally-equivalent base images are available. While many studies define and evaluate quality features that can be extracted from Docker artifacts, it is still unclear what are the criteria on which developers choose a base image over another. In this paper, we aim to fill this gap. First, we conduct a literature review through which we define a taxonomy of quality features, identifying two main groups: Configuration-related features (i.e., mainly related to the Dockerfile and image build process), and externally observable features (i.e., what the Docker image users can observe). Second, we ran an empirical study considering the developers’ preference for 2,441 Docker images in 1,911 open-source software projects. We want to understand (i) how the externally observable features influence the developers’ preferences, and (ii) how they are related to the configuration-related features. Our results pave the way to the definition of a reliable quality measure for Docker artifacts, along with tools that support developers for a quality-aware development of them
Link to publicationResearch Papers
Wed 13 Sep 2023 10:54 - 11:06 at Plenary Room 2 - Code Quality and Code Smells Chair(s): Bernd FischerReading source code occupies most of developer’s daily activities. Any maintenance and evolution task requires developers to read and understand the code they are going to modify. For this reason, previous research focused on the definition of techniques to automatically assess the readability of a given snippet. However, when many unreadable code sections are detected, developers might be required to manually modify them all to improve their readability. While existing approaches aim at solving specific readability-related issues, such as improving variable names or fixing styling issues, there is still no approach to automatically suggest which actions should be taken to improve code readability. In this paper, we define the first holistic readability-improving approach. As a first contribution, we introduce a methodology for automatically identifying readability-improving commits, and we use it to build a large dataset of 122k commits by mining the whole revision history of all the projects hosted on GitHub between 2015 and 2022. We show that such a methodology has ∼86% accuracy. As a second contribution, we train and test the T5 model to emulate what developers did to improve readability. We show that our model achieves a perfect prediction accuracy between 21% and 28%. The results of a manual evaluation we performed on 500 predictions shows that when the model does not change the behavior of the input and it applies changes (34% of the cases), in the large majority of the cases (79.4%) it allows to improve code readability.
Pre-print