activity
20192026
most citedUsing Evolutionary Coupling to Establish Relevance Links Between Tests and Code Units. A case study on fault localization

2 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

cs.SE2026

Assessing Behavioral Validation in UI Component Test Suites Using Inferred Metamorphic Relations

Yu Pei, Cunming Zhang, Jeongju Sohn +1

UI component libraries are commonly assessed using execution-based metrics such as statement and branch coverage, yet these metrics provide limited insight into whether tests verif…

cs.SE2025

Latent Mutants: A large-scale study on the Interplay between mutation testing and software evolution

Jeongju Sohn, Ezekiel Soremekun, Michail Papadakis

In this paper we apply mutation testing in an in-time fashion, i.e., across multiple project releases. Thus, we investigate how the mutants of the current version behave in the fut…

cs.SE2023

Time-based Repair for Asynchronous Wait Flaky Tests in Web Testing

Yu Pei, Jeongju Sohn, Sarra Habchi +1

Asynchronous waits are one of the most prevalent root causes of flaky tests and a major time-influential factor of web application testing. To investigate the characteristics of as…

cs.SE2022★ 1 cited

What Made This Test Flake? Pinpointing Classes Responsible for Test Flakiness

Sarra Habchi, Guillaume Haben, Jeongju Sohn +4

Flaky tests are defined as tests that manifest non-deterministic behaviour by passing and failing intermittently for the same version of the code. These tests cripple continuous in…

cs.SE2022★ 2 cited

Using Evolutionary Coupling to Establish Relevance Links Between Tests and Code Units. A case study on fault localization

Jeongju Sohn, Mike Papadakis

Many software engineering techniques, such as fault localization, operate based on relevance relationships between tests and code. These relationships are often inferred through th…

cs.LG2019

Arachne: Search Based Repair of Deep Neural Networks

Jeongju Sohn, Sungmin Kang, Shin Yoo

The rapid and widespread adoption of Deep Neural Networks (DNNs) has called for ways to test their behaviour, and many testing approaches have successfully revealed misbehaviour of…