MSR 2020
Mon 29 - Tue 30 June 2020
co-located with ICSE 2020
Mon 29 Jun 2020 12:40 - 12:50 at MSR:Zoom - Code Smells Chair(s): Alessandro Garcia

Deep learning practitioners are often interested in improving their model accuracy rather than the interpretability of their models. As a result, deep learning applications are inherently complex in their structures. They also need to continuously evolve in terms of code changes and model updates. Given these confounding factors, there is a great chance of violating the recommended programming practices by the developers in their deep learning applications. In particular, the code quality might be negatively affected due to their drive for the higher model performance. Unfortunately, the code quality of deep learning applications has rarely been studied to date. In this paper, we conduct an empirical study using 118 open-source software systems from GitHub where we contrast between deep learning-based and traditional systems in terms of their code quality. We have several major findings. First, deep learning applications smell like the traditional ones. However, long lambda expression, long ternary conditional expression, and complex container comprehension smells are frequently found in deep learning projects. That is, the DL code involves more complex or longer expressions than the traditional code does. Second, code smells are found increasing across the releases of deep learning applications. Third, we found that there is a co-existence between code smells and software bugs in the deep learning code, which confirms our conjecture on the degraded code quality in deep learning applications.

Mon 29 Jun

Displayed time zone: (UTC) Coordinated Universal Time change

12:00 - 13:00
Code SmellsTechnical Papers / Registered Reports / Keynote / MSR Awards / FOSS Award / Education / Data Showcase / Mining Challenge / MSR Challenge Proposals / Ask Me Anything at MSR:Zoom
Chair(s): Alessandro Garcia PUC-Rio

Q/A & Discussion of Session Papers over Zoom (Joining info available on Slack)

12:00
10m
Live Q&A
Detecting Video Game-Specific Bad Smells in Unity ProjectsMSR - Technical Paper
Technical Papers
Pre-print Media Attached
12:10
10m
Live Q&A
Investigating Severity Thresholds for Test SmellsMSR - Technical Paper
Technical Papers
Davide Spadini Delft University of Technology, Netherlands, Martin Schvarcbacher , Ana Maria Oprescu , Magiel Bruntink Software Improvement Group, Alberto Bacchelli University of Zurich
DOI Pre-print Media Attached
12:20
10m
Live Q&A
On the Prevalence, Impact, and Evolution of SQL code smells in Data-Intensive SystemsMSR - Technical Paper
Technical Papers
Biruk Asmare Muse , Masud Rahman Dalhousie University, Csaba Nagy Software Institute - USI, Lugano, Anthony Cleve University of Namur, Foutse Khomh Polytechnique Montréal, Giuliano Antoniol Polytechnique Montréal
Pre-print Media Attached
12:30
10m
Live Q&A
Multi-language Design Smells: A Backstage PerspectiveMSR - Registered Reports
Registered Reports
A: Mouna Abidi , A: Moses Openja , A: Foutse Khomh Polytechnique Montréal
Pre-print Media Attached
12:40
10m
Live Q&A
The Scent of Deep Learning Code: An Empirical StudyMSR - Technical Paper
Technical Papers
Hadhemi Jebnoun , Masud Rahman Dalhousie University, Foutse Khomh Polytechnique Montréal, Houssem Ben Braiek
Pre-print Media Attached
12:50
10m
Live Q&A
Developer-Driven Code Smell PrioritizationMSR - Technical Paper
Technical Papers
Fabiano Pecorelli University of Salerno, Fabio Palomba University of Salerno, Foutse Khomh Polytechnique Montréal, Andrea De Lucia University of Salerno
Pre-print Media Attached