Computational Intelligence for Software Engineering Lab

MSc Recruiting Autumn 2022: We are looking for new members!


Our research focuses on the exciting intersection between software engineering and machine intelligence.

Recent Updates

Our new paper "FDG: A Precise Measurement of Fault Diagnosability Gain of Test Cases" is accepted at ISSTA 2022

A COINSE paper about fault diagnosability gain has been accepted at ISSTA 2022. [more...]

Congratulations, Dr. Seongmin Lee!

Seongmin Lee successfully defended his PhD thesis, the second from from COINSE group. [more...]

A new paper about automatically augmenting equivalent mutant dataset has been accepted at MUTATION 2022

We present an automated technique to augment equivalent mutant dataset. [more...]

Our new paper "Automatically Identifying Shared Root Causes of Test Breakages in SAP HANA" is accepted to ICSE-SEIP 2022

We present a technique for identifying shared root causes of test breakages by combining multiple information sources associated with the failing tests. [more...]

Our paper "Predictive Mutation Analysis via Natural Language Channel in Source Code" is accepted to TOSEM

This paper aims to predict a full kill matrix resulted from mutation analysis by leveraging Natural Language channel in source and test code. [more...]

A new paper about GUI smoke test repairing technique has been accepted at ICST 2022 Industry Track

We present a new repair technique for View Identification Failures (VIF) in GUI tests from a collaboration work between COINSE and Samsung Research. [more...]

Latest Publications

  1. Kim, J., Feldt, R. and Yoo, S., Evaluating Surprise Adequacy for Deep Learning System Testing. ACM Transactions on Software Engineering and Methodology. to appear, (2022). [pdf] [bibtex]
      @article{Kim2022ap,
      address = {New York, NY, USA},
      author = {Kim, Jinhan and Feldt, Robert and Yoo, Shin},
      journal = {{ACM} Transactions on Software Engineering and Methodology},
      month = jun,
      publisher = {Association for Computing Machinery},
      series = {TOSEM},
      title = {Evaluating Surprise Adequacy for Deep Learning System Testing},
      volume = {to appear},
      year = {2022}
    }
    
    
  2. Chung, S. and Yoo, S., Augmenting Equivalent Mutant Dataset Using Symbolic Execution. Proceedings of the 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) 150–159. [bibtex]
      @inproceedings{Chung2022uz,
      author = {Chung, Seungjun and Yoo, Shin},
      booktitle = {Proceedings of the 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)},
      date-added = {2022-07-27 09:24:33 -0400},
      date-modified = {2022-07-27 09:24:44 -0400},
      doi = {10.1109/ICSTW55395.2022.00038},
      issn = {2159-4848},
      month = apr,
      pages = {150-159},
      publisher = {IEEE Computer Society},
      title = {Augmenting Equivalent Mutant Dataset Using Symbolic Execution},
      url = {https://doi.ieeecomputersociety.org/10.1109/ICSTW55395.2022.00038},
      year = {2022},
      bdsk-url-1 = {https://doi.ieeecomputersociety.org/10.1109/ICSTW55395.2022.00038},
      bdsk-url-2 = {https://doi.org/10.1109/ICSTW55395.2022.00038}
    }
    
    
  3. Kang, S. and Yoo, S., Language Models Can Prioritize Patches for Practical Program Patching. Proceedings of the 3rd International Workshop on Automated Proigram Repair 8–15. [pdf] [bibtex]
      @inproceedings{Kang2022kl,
      author = {Kang, Sungmin and Yoo, Shin},
      booktitle = {Proceedings of the 3rd International Workshop on Automated Proigram Repair},
      date-added = {2022-07-27 09:22:29 -0400},
      date-modified = {2022-07-27 09:22:29 -0400},
      pages = {8--15},
      series = {APR 2022},
      title = {Language Models Can Prioritize Patches for Practical Program Patching},
      year = {2022}
    }
    
    
  4. An, G. and Yoo, S., FDG: A Precise Measurement of Fault Diagnosability Gain of Test Cases. Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis 14–26. [pdf] [bibtex]
      @inproceedings{An2022pb,
      author = {An, Gabin and Yoo, Shin},
      booktitle = {Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis},
      date-added = {2022-07-26 22:19:47 -0400},
      date-modified = {2022-07-26 22:19:47 -0400},
      pages = {14--26},
      series = {ISSTA 2022},
      title = {{FDG}: A Precise Measurement of Fault Diagnosability Gain of Test Cases},
      year = {2022}
    }
    
    
  5. An, G., Yoon, J., Sohn, J., Hong, J., Hwang, D. and Yoo, S., Automatically Identifying Shared Root Causes of Test Breakages in SAP HANA. Proceedings of the 44th IEEE/ACM International Conference on Software Engineering - Software Engineering In Practice Track 65–74. [pdf] [bibtex]
      @inproceedings{An2022qe,
      author = {An, Gabin and Yoon, Juyeon and Sohn, Jeongju and Hong, Jingun and Hwang, Dongwon and Yoo, Shin},
      booktitle = {Proceedings of the 44th IEEE/ACM International Conference on Software Engineering - Software Engineering In Practice Track},
      date-added = {2022-07-26 22:18:11 -0400},
      date-modified = {2022-07-26 22:18:11 -0400},
      pages = {65--74},
      series = {ICSE SEIP 2022},
      title = {Automatically Identifying Shared Root Causes of Test Breakages in SAP HANA},
      year = {2022}
    }
    
    
  6. Kim, J., Jeon, J., Hong, S. and Yoo, S., Predictive Mutation Analysis via Natural Language Channel in Source Code. ACM Transactions on Software Engineering and Methodology. 31, 4 (2022), 1–27. [pdf] [bibtex]
      @article{Kim2022xy,
      author = {Kim, Jinhan and Jeon, Juyoung and Hong, Shin and Yoo, Shin},
      date-added = {2022-07-26 22:16:52 -0400},
      date-modified = {2022-07-26 22:16:52 -0400},
      journal = {{ACM} Transactions on Software Engineering and Methodology},
      number = {4},
      pages = {1--27},
      title = {Predictive Mutation Analysis via Natural Language Channel in Source Code},
      volume = {31},
      year = {2022}
    }
    
    
  7. Chung, S. and Yoo, S., Augmenting Equivalent Mutant Dataset Using Symbolic Execution. Proceedings of the 17th International Workshop on Mutation Analysis. [pdf] [bibtex]
      @inproceedings{Chung2022ae,
      author = {Chung, Seungjoon and Yoo, Shin},
      booktitle = {Proceedings of the 17th International Workshop on Mutation Analysis},
      series = {Mutation 2022},
      title = {Augmenting Equivalent Mutant Dataset Using Symbolic Execution},
      year = {2022}
    }