ESEIW 2022
Sun 18 - Fri 23 September 2022 Helsinki, Finland
Thu 22 Sep 2022 16:27 - 16:38 at Sonck - Session 3B - Registered Reports 1 Chair(s): Sérgio Soares

Context: The identification of bugs within the reported issues in an issue tracker is crucial for the triage of issues. Machine learning models have shown promising results regarding the performance of automated issue type prediction. However, we have only limited knowledge beyond our assumptions how such models identify bugs. LIME and SHAP are popular technique to explain the predictions of classifiers.

Objective: We want to understand if machine learning models provide explanations for the classification that are reasonable to us as humans and align with our assumptions of what the models should learn. We also want to know if the prediction quality is correlated with the quality of explanations.

Method: We conduct a study where we rate LIME and SHAP explanations based on their quality of explaining the outcome of an issue type prediction model. For this, we rate the quality of the explanations themselves, i.e., if they align with our expectations and if they help us to understand the underlying machine learning model.

Thu 22 Sep

Displayed time zone: Athens change

15:45 - 17:00
Session 3B - Registered Reports 1ESEM Registered Reports at Sonck
Chair(s): Sérgio Soares Universidade Federal de Pernambuco
The Relevance of Model Transformation Language Features on Qualitative Properties of MTLs: A Study Protocol
ESEM Registered Reports
Stefan Höppner Ulm University, Matthias Tichy Ulm University, Germany
On the acceptance by code reviewers of candidate security patches suggested by Automated Program Repair tools
ESEM Registered Reports
Aurora Papotti Vrije Universiteit Amsterdam, Ranindya Paramitha University of Trento, Fabio Massacci University of Trento; Vrije Universiteit Amsterdam
DOI Pre-print
Does Road Diversity Really Matter in Testing Automated Driving Systems? A Registered Report
ESEM Registered Reports
Stefan Klikovits , Vincenzo Riccio USI Lugano, Ezequiel Castellano National Institute of Informatics, Ahmet Cetinkaya Shibaura Institute of Technology, Alessio Gambi IMC University of Applied Sciences Krems, Paolo Arcaini National Institute of Informatics
Link to publication
A Unified and Holistic Classification Scheme for Software Engineering Research
ESEM Registered Reports
Angelika Kaplan Karlsruhe Institute of Technology, Thomas Kühn Karlsruhe Institute of Technology, Ralf Reussner Karlsruhe Institute of Technology (KIT) and FZI - Research Center for Information Technology (FZI)
Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP
ESEM Registered Reports
Benjamin Ledel TU Clausthal, Steffen Herbold TU Clausthal
Team performance and large-scale agile software development
ESEM Registered Reports
Muhammad Ovais Ahmad Karlstad University, Hadi Ghanbari Aalto University, Tomas Gustavsson Karlstad University
Research paper
Comparative analysis of real bugs in open-source Machine Learning projects - A Registered Report
ESEM Registered Reports
Tuan Dung Lai Deakin University, Anj Simmons Deakin University, Scott Barnett Deakin University, Jean-Guy Schneider Deakin University, Rajesh Vasa Deakin University, Australia
Link to publication Pre-print