@InProceedings{10.1145/3387940.3392250,
  author    = {Rahman, Karishma and Kahanda, Indika and Kanewala, Upulee},
  booktitle = {Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops},
  title     = {MRpredT: Using Text Mining for Metamorphic Relation Prediction},
  doi       = {10.1145/3387940.3392250},
  isbn      = {9781450379632},
  location  = {Seoul, Republic of Korea},
  pages     = {420–424},
  publisher = {Association for Computing Machinery},
  series    = {ICSEW'20},
  url       = {https://doi.org/10.1145/3387940.3392250},
  abstract  = {Metamorphic relations (MRs) are an essential component of metamorphic testing (MT) that highly affects its fault detection effectiveness. MRs are usually identified with the help of a domain expert, which is a labor-intensive task. In this work, we explore the feasibility of a text classification-based machine learning approach to predict MRs using their program documentation as the sole input. We compare our method to our previously developed graph kernelbased machine learning approach and demonstrate that textual features extracted from program documentation are highly effective for predicting metamorphic relations for matrix calculation programs.},
  address   = {New York, NY, USA},
  keywords  = {Metamorphic testing, Text classification, Metamorphic relations},
  numpages  = {5},
  year      = {2020},
}

{"_id":"DBofwntJhiCk2P6z7","bibbaseid":"rahman-kahanda-kanewala-mrpredtusingtextminingformetamorphicrelationprediction-2020","author_short":["Rahman, K.","Kahanda, I.","Kanewala, U."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"propositions":[],"lastnames":["Rahman"],"firstnames":["Karishma"],"suffixes":[]},{"propositions":[],"lastnames":["Kahanda"],"firstnames":["Indika"],"suffixes":[]},{"propositions":[],"lastnames":["Kanewala"],"firstnames":["Upulee"],"suffixes":[]}],"booktitle":"Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops","title":"MRpredT: Using Text Mining for Metamorphic Relation Prediction","doi":"10.1145/3387940.3392250","isbn":"9781450379632","location":"Seoul, Republic of Korea","pages":"420–424","publisher":"Association for Computing Machinery","series":"ICSEW'20","url":"https://doi.org/10.1145/3387940.3392250","abstract":"Metamorphic relations (MRs) are an essential component of metamorphic testing (MT) that highly affects its fault detection effectiveness. MRs are usually identified with the help of a domain expert, which is a labor-intensive task. In this work, we explore the feasibility of a text classification-based machine learning approach to predict MRs using their program documentation as the sole input. We compare our method to our previously developed graph kernelbased machine learning approach and demonstrate that textual features extracted from program documentation are highly effective for predicting metamorphic relations for matrix calculation programs.","address":"New York, NY, USA","keywords":"Metamorphic testing, Text classification, Metamorphic relations","numpages":"5","year":"2020","bibtex":"@InProceedings{10.1145/3387940.3392250,\n author = {Rahman, Karishma and Kahanda, Indika and Kanewala, Upulee},\n booktitle = {Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops},\n title = {MRpredT: Using Text Mining for Metamorphic Relation Prediction},\n doi = {10.1145/3387940.3392250},\n isbn = {9781450379632},\n location = {Seoul, Republic of Korea},\n pages = {420–424},\n publisher = {Association for Computing Machinery},\n series = {ICSEW'20},\n url = {https://doi.org/10.1145/3387940.3392250},\n abstract = {Metamorphic relations (MRs) are an essential component of metamorphic testing (MT) that highly affects its fault detection effectiveness. MRs are usually identified with the help of a domain expert, which is a labor-intensive task. In this work, we explore the feasibility of a text classification-based machine learning approach to predict MRs using their program documentation as the sole input. We compare our method to our previously developed graph kernelbased machine learning approach and demonstrate that textual features extracted from program documentation are highly effective for predicting metamorphic relations for matrix calculation programs.},\n address = {New York, NY, USA},\n keywords = {Metamorphic testing, Text classification, Metamorphic relations},\n numpages = {5},\n year = {2020},\n}\n\n","author_short":["Rahman, K.","Kahanda, I.","Kanewala, U."],"key":"10.1145/3387940.3392250","id":"10.1145/3387940.3392250","bibbaseid":"rahman-kahanda-kanewala-mrpredtusingtextminingformetamorphicrelationprediction-2020","role":"author","urls":{"Paper":"https://doi.org/10.1145/3387940.3392250"},"keyword":["Metamorphic testing","Text classification","Metamorphic relations"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/network/files/efHxiSdHcByWYrtAF","dataSources":["sY8SCkRETugqZgTDF","rtMQfZTvNbLxgaZMD"],"keywords":["metamorphic testing","text classification","metamorphic relations"],"search_terms":["mrpredt","using","text","mining","metamorphic","relation","prediction","rahman","kahanda","kanewala"],"title":"MRpredT: Using Text Mining for Metamorphic Relation Prediction","year":2020}