Computational Intelligence for Software Engineering Lab

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

Recent Updates

New Preprint about Hyundai Motors Collaboration

A new arXiv preprint about the collaboration between COINSE, Chalmers, and Hyundai Motors Company is available. [more...]

Three new ICSE 2020 workshop papers from COINSE

Three new papers have been accepted into collocated workshops at ICSE 2020. [more...]

Doctoral Symposium Presentations from COINSE members at ICSE 2020

Two PhD candidates from COINSE will be presenting their research at the doctoral symposium at ICSE 2020. [more...]

NAVER PhD Fellowship Awards 2019

Jeongju Sohn and Jinhan Kim, both PhD candidates at COINSE, were awarded NAVER PhD Fellowship Award. [more...]

COINSE is looking for MSc Students for Spring 2020

We are recruiting highly motivated MSc students out of those who got admitted into MSc programme starting from Spring 2020. [more...]

Paper accepted at JSS

A COINSE paper has been accepted at Journal of Systems and Software. [more...]

Latest Publications

  1. Kim, J., Ju, J., Feldt, R. and Yoo, S., Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving. Proceedings of ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE Industry Track). [bibtex]
      @inproceedings{Kim2020zg,
      author = {Kim, Jinhan and Ju, Jeongil and Feldt, Robert and Yoo, Shin},
      booktitle = {Proceedings of ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE Industry Track)},
      date-added = {2020-11-03 18:37:54 +0900},
      date-modified = {2020-11-03 18:37:54 +0900},
      series = {ESEC/FSE 2020},
      title = {Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving},
      year = {2020},
      bdsk-file-1 = {YnBsaXN0MDDSAQIDBFxyZWxhdGl2ZVBhdGhZYWxpYXNEYXRhXxApLi4vLi4vLi4vLi4vRHJvcGJveC9QYXBlcnMvS2ltMjAyMHpnYS5wZGZPEQFSAAAAAAFSAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAAAAAAAAQkQAAf////8OS2ltMjAyMHpnYS5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/////wAAAABQREYgAAAAAAAEAAMAAAogY3UAAAAAAAAAAAAAAAAABlBhcGVycwACAC0vOlVzZXJzOm50cm9sbHM6RHJvcGJveDpQYXBlcnM6S2ltMjAyMHpnYS5wZGYAAA4AHgAOAEsAaQBtADIAMAAyADAAegBnAGEALgBwAGQAZgAPABoADABNAGEAYwBpAG4AdABvAHMAaAAgAEgARAASACtVc2Vycy9udHJvbGxzL0Ryb3Bib3gvUGFwZXJzL0tpbTIwMjB6Z2EucGRmAAATAAEvAAAVAAIADv//AAAACAANABoAJABQAAAAAAAAAgEAAAAAAAAABQAAAAAAAAAAAAAAAAAAAaY=}
    }
    
    
  2. Ralph, P. et al., Pandemic programming. Empirical Software Engineering. 25, 6, 4927–4961. [bibtex]
      @article{Ralph2020pd,
      author = {Ralph, Paul and Baltes, Sebastian and Adisaputri, Gianisa and Torkar, Richard and Kovalenko, Vladimir and Kalinowski, Marcos and Novielli, Nicole and Yoo, Shin and Devroey, Xavier and Tan, Xin and Zhou, Minghui and Turhan, Burak and Hoda, Rashina and Hata, Hideaki and Robles, Gregorio and Fard, Amin Milani and Alkadhi, Ran},
      da = {2020/11/01},
      date-added = {2020-11-03 18:34:31 +0900},
      date-modified = {2020-11-03 18:34:31 +0900},
      isbn = {1573-7616},
      journal = {Empirical Software Engineering},
      number = {6},
      pages = {4927--4961},
      title = {Pandemic programming},
      volume = {25},
      year = {2020},
      bdsk-url-1 = {https://doi.org/10.1007/s10664-020-09875-y}
    }
    
    
  3. Feldt, R. and Yoo, S., Flexible Probabilistic Modeling for Search Based Test Data Generation. Proceedings of the International Workshop on Search Based Software Testing (SBST 2020). [pdf] [bibtex]
      @inproceedings{Feldt2020aa,
      author = {Feldt, Robert and Yoo, Shin},
      booktitle = {Proceedings of the International Workshop on Search Based Software Testing (SBST 2020)},
      date-added = {2020-02-29 16:42:47 +0900},
      date-modified = {2020-02-29 16:43:23 +0900},
      title = {Flexible Probabilistic Modeling for Search Based Test Data Generation},
      year = {2020}
    }
    
    
  4. Kang, S., Feldt, R. and Yoo, S., SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation. Proceedings of the International Workshop on Search Based Software Testing (SBST 2020). [pdf] [bibtex]
      @inproceedings{Kang2020aa,
      author = {Kang, Sungmin and Feldt, Robert and Yoo, Shin},
      booktitle = {Proceedings of the International Workshop on Search Based Software Testing (SBST 2020)},
      date-added = {2020-02-29 16:41:30 +0900},
      date-modified = {2020-02-29 16:42:22 +0900},
      title = {SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation},
      year = {2020}
    }
    
    
  5. Kim, S. and Yoo, S., Evaluating Surprise Adequacy for Question Answering. Proceedings of The 2nd International Workshop on Testing for Deep Learning and Deep Learning for Testing (DeepTest 2020). [pdf] [bibtex]
      @inproceedings{Kim2020aa,
      author = {Kim, Seah and Yoo, Shin},
      booktitle = {Proceedings of The 2nd International Workshop on Testing for Deep Learning and Deep Learning for Testing (DeepTest 2020)},
      date-added = {2020-02-29 16:32:19 +0900},
      date-modified = {2020-02-29 16:40:42 +0900},
      title = {Evaluating Surprise Adequacy for Question Answering},
      year = {2020}
    }
    
    
  6. Lee, S., Binkley, D., Gold, N., Islam, S., Krinke, J. and Yoo, S., Evaluating lexical approximation of program dependence. Journal of Systems and Software. 160, 110459. [pdf] [bibtex]
      @article{Lee2019aa,
      author = {Lee, Seongmin and Binkley, David and Gold, Nicolas and Islam, Syed and Krinke, Jens and Yoo, Shin},
      doi = {https://doi.org/10.1016/j.jss.2019.110459},
      issn = {0164-1212},
      journal = {Journal of Systems and Software},
      keywords = {ORBS, Program slicing, Lexical analysis},
      pages = {110459},
      title = {Evaluating lexical approximation of program dependence},
      url = {http://www.sciencedirect.com/science/article/pii/S016412121930233X},
      volume = {160},
      year = {2020},
      bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S016412121930233X},
      bdsk-url-2 = {https://doi.org/10.1016/j.jss.2019.110459}
    }
    
    
  7. Kim, Y., Mun, S., Yoo, S. and Kim, M., Precise Learn-to-Rank Fault Localization Using Dynamic and Static Features of Target Programs. ACM Trans. Softw. Eng. Methodol. 28, 4, 23:1–23:34. [pdf] [bibtex]
      @article{Kim2019ab,
      acmid = {3345628},
      address = {New York, NY, USA},
      articleno = {23},
      author = {Kim, Yunho and Mun, Seokhyeon and Yoo, Shin and Kim, Moonzoo},
      doi = {10.1145/3345628},
      issn = {1049-331X},
      issue_date = {October 2019},
      journal = {ACM Trans. Softw. Eng. Methodol.},
      keywords = {Fault localization, machine learning, mutation analysis, source file characteristics},
      month = oct,
      number = {4},
      numpages = {34},
      pages = {23:1--23:34},
      publisher = {ACM},
      title = {Precise Learn-to-Rank Fault Localization Using Dynamic and Static Features of Target Programs},
      url = {http://doi.acm.org/10.1145/3345628},
      volume = {28},
      year = {2019},
      bdsk-url-1 = {http://doi.acm.org/10.1145/3345628},
      bdsk-url-2 = {https://doi.org/10.1145/3345628}
    }