ARISE Lab

We study the principles of computing and the phenomena of software developments to understand how unreliable and insecure software is produced and threatening in our society. We aim to resolve these problems by developing techniques, tools, methodologies and pedagogies that enhance the ability of developers in testing and debugging software artifacts.

Alumni

Hanyoung Yoo (유한영), now in SAP Labs Korea

• Master's Degree, Mar 2020-Feb 2022

• Thesis: Improving Greybox Fuzzing Techniques for Continuous Unit-level Testing (BK 21 Master's Thesis Award) 

Jeewoong Kim (김지웅), now in ARISE lab, Handong Global University 

• Master's Degree, Mar 2018-Aug 2021

• Thesis: Extracting Fine-grained Traceability Links Between API Document Comments and JUnit Test Code Lines

Juyoung Jeon (전주영), now in Hyundai MOBIS

• Master's Degree, Mar 2019-Feb 2021

• Thesis: Improving Mutation Based Fault Localization for Omission Faults (BK 21 Master's  Thesis Award)

Hansol Choe (최한솔), now in Suresoft Technologies Inc.

• Master's Degree, Mar 2018-Feb 2021

• Thesis: Concolic Testing Search Strategies for Mitigating Path-space Local Search Problem (Best Master's Thesis Award)

Publications [more]

[ICST'23-Poster] BugOss: A Regression Bug Benchmark for Empirical Study of Regression Fuzzing Techniques 

[STVR'22] Learning-based Mutant Reduction using Fine-grained Mutation Operators

[ICSME-NIER'22] Inferring Fine-grained Traceability Links between Javadoc Comment and JUnit Test Code

[TOSEM'22] Predictive Mutation Analysis via Natural Language Channel in Source Code 

[ICST-Experience'22] Repairing Fragile GUI Test Cases Using Word and Layout Embedding

[ASE-Industry'21] Improving Configurability of Unit-level Continuous Fuzzing: An Industrial Case Study with SAP HANA

[ASE-LBR'21] Improving Mutation-Based Fault Localization with Plausible-code Generating Mutation Operators

[SSBSE-RENE'21] Empirical Study of Effectiveness of EvoSuite on the SBST 2020 Tool Competition Benchmark

[STVR'21] DEMINER: test generation for high test coverage through mutant exploration

[ICSE-NIER'20] Threats to Validity in Evaluating Mutation-based Fault Localization

[ESEC/FSE'19] Target-driven Compositional Concolic Testing with Function Summary Refinement for Effective Bug Detection

[ICST-Experience'19] Classifying False Positive Static Checker Alarms In Continuous Integration Using Convolutional Neural Networks 

[ICST'18] Invasive Software Testing: Mutating Target Programs to Diversify Test Exploration for High Test Coverage (distinguished paper award)

[IST'17] Mutation-Based Fault Localization for Real-World Multilingual Programs

Research Projects

Center for Software Disaster Research, Excellent Research Center (ERC), NRF, 2021-2028 (led by Prof. Yunja Choi at KNU, in collaboration with KAIST, Korea University, UNIST, POSTECH, Hanyang University) (on going)

Development of automatic software error repair technology that combines code analysis and error mining, IITP (in collaboration with SuresoftTech Inc., Arcyl Inc., Prof. Shin Yoo at KAIST and Prof. Jooyong Yi at UNIST), 2021-2024 (on going)

Fuzz Testing for Effective Continuous Testing on Evolving Software, NRF, 2020-2023

Design of Online Model Training and Prediction Environment for Reproducible Machine Learning Artifact Archiving (in collaboration with Prof. Charmgil Hong at Handong University), KISTI, 2020-2021

Establishing Code Change Traceability Using Source Code Embedding (in direction of Prof. Shin Yoo at KAIST), Samsung Research, 2020, 2021 

Fuzzing for Improving Unit-level Testing, SAP Labs Korea, 2020, 2021

Intelligent Automation Techniques for Fullstack Software Debugging, Next-Generation Information Computing Development Program Funded by NRF, 2017-2020

Automated Method of Assessing Test Artifacts Quality (in direction of Prof. Shin Yoo at KAIST), Samsung Research, 2019

Developing Automated Software Test Generation Techniques Using Data-driven Analyses, Young Researcher Program Funded by NRF, 2017-2019

More ...