ESEIW 2024
Sun 20 - Fri 25 October 2024 Barcelona, Spain

This program is tentative and subject to change.

Background. Mutation analysis is the premier technique to evaluate software test suite quality and estimating residual software defects. However, the reliability of mutation analysis is hampered by the so called equivalent mutants which are unkillable by any test case. Reliably detecting and eliminating killable mutants is difficult as it is highly program and location dependent. Hence, statistical estimation of killable mutants offers a promising approach.

Aims. Recently, frequency based species estimation methods has been proposed as a solution for related problems in software testing. While promising, there has been no comprehensive study on whether such frequency based species estimation methods can deliver an accurate estimate for killable mutants. Hence, this paper investigates the following: Can the frequency based estimation methods provide an accurate estimate for the number of killable mutants?

Method. In this paper, we report the results of a large-scale empirical study on the application of twelve widely known frequency based statistical models for estimating the number of killable mutants in ten mature software projects.

Result. Our investigation suggests that the considered statistical estimators lack sufficient predictive power and cannot produce reliable or useful estimates of killable mutants.

This program is tentative and subject to change.

Thu 24 Oct

Displayed time zone: Brussels, Copenhagen, Madrid, Paris change

16:00 - 17:30
Software vulnerabilities and defectsESEM Technical Papers / ESEM Journal-First Papers / ESEM Emerging Results, Vision and Reflection Papers Track at Aula de graus (C4 Building)
Chair(s): Daniela Cruzes Norwegian University of Science and Technology
16:00
20m
Full-paper
Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++
ESEM Technical Papers
Anh Nguyen The , Triet Le The University of Adelaide, Muhammad Ali Babar School of Computer Science, The University of Adelaide
DOI Pre-print
16:20
20m
Full-paper
Empirical Evaluation of Frequency Based Statistical Models for Estimating Killable Mutants
ESEM Technical Papers
Konstantin Kuznetsov Saarland University, CISPA, Alessio Gambi Austrian Institute of Technology (AIT), Saikrishna Dhiddi Passau University, Julia Hess Saarland University, Rahul Gopinath University of Sydney
16:40
20m
Full-paper
Reevaluating the Defect Proneness of Atoms of Confusion in Java Systems
ESEM Technical Papers
Guoshuai Shi University of Waterloo, Farshad Kazemi University of Waterloo, Michael W. Godfrey University of Waterloo, Canada, Shane McIntosh University of Waterloo
Pre-print
17:00
15m
Journal Early-Feedback
Identifying concerns when specifying machine learning-enabled systems: A perspective-based approach
ESEM Journal-First Papers
Hugo Villamizar fortiss GmbH, Marcos Kalinowski Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Helio Côrtes Vieira Lopes PUC-Rio, Daniel Mendez Blekinge Institute of Technology and fortiss
17:15
15m
Vision and Emerging Results
DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
ESEM Emerging Results, Vision and Reflection Papers Track
Tiezhu Sun University of Luxembourg, Nadia Daoudi Luxembourg Institute of Science and Technology, Kisub Kim Singapore Management University, Singapore, Kevin Allix Independent Researcher, Tegawendé F. Bissyandé University of Luxembourg, Jacques Klein University of Luxembourg