Computational Intelligence for Software Engineering Lab

MSc Recruiting Spring 2023: We are looking for new members!


Our research focuses on the exciting intersection between software engineering and machine intelligence. We try to improve developer productivity with optimisation and automation. COINSE has world-leading expertise in automated debugging, automated testing, and testing of DNN models.

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. Sohn, J., Kang, S. and Yoo, S., Arachne: Search Based Repair of Deep Neural Networks. ACM Transactions on Software Engineering Methodology. to appear, (2022). [bibtex]
      @article{Sohn2022cr,
      author = {Sohn, Jeongju and Kang, Sungmin and Yoo, Shin},
      date-added = {2022-08-17 11:27:43 +0900},
      date-modified = {2022-08-17 11:29:36 +0900},
      journal = {{ACM} {T}ransactions on {S}oftware {E}ngineering {M}ethodology},
      title = {Arachne: Search Based Repair of Deep Neural Networks},
      volume = {to appear},
      year = {2022}
    }
    
    
  4. 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}
    }
    
    
  5. 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}
    }
    
    
  6. 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}
    }
    
    
  7. 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}
    }