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NapluesNaplues
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revised version 2022-04-28
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‎main.py

+15-5
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@@ -21,7 +21,7 @@
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# ========================= Run RQ1 experiments =================================
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def run_cross_release_predict(prediction_model):
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def run_cross_release_predict(prediction_model, save_time=False):
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# time
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release_name, build_time_list, pred_time_list = [], [], []
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for project, releases in get_project_releases_dict().items():
@@ -37,16 +37,16 @@ def run_cross_release_predict(prediction_model):
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# ####### Pred time #######
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t_start = time.time()
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model.file_level_prediction()
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# model.analyze_file_level_result()
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model.analyze_file_level_result()
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model.line_level_prediction()
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# model.analyze_line_level_result()
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model.analyze_line_level_result()
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t_end = time.time()
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pred_time_list.append(t_end - t_start)
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release_name.append(releases[i + 1])
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data = {'release_name': release_name, 'build_time': build_time_list, 'pred_time': pred_time_list}
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data = pd.DataFrame(data, columns=['release_name', 'build_time', 'pred_time'])
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data.to_csv(model.time_file, index=False)
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data.to_csv(model.execution_time_file, index=False) if save_time else None
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def run_default():
@@ -56,12 +56,22 @@ def run_default():
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# ======= NLP-based approaches ======= NGram, NGram_C
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# ======= Glance-XX approaches ======= Glance_MD, Glance_EA, Glance_LR
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run_cross_release_predict(Glance_LR)
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# run_cross_release_predict(NGram)
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# run_cross_release_predict(NGram_C)
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# run_cross_release_predict(TMI_LR)
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# run_cross_release_predict(TMI_MNB)
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# run_cross_release_predict(TMI_RF)
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# run_cross_release_predict(TMI_DT)
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# run_cross_release_predict(TMI_SVM)
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# run_cross_release_predict(Glance_LR)
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# run_cross_release_predict(Glance_EA)
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# run_cross_release_predict(Glance_MD)
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# run_cross_release_predict(LineDP)
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run_cross_release_predict(PMD,save_time=True)
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# run_cross_release_predict(CheckStyle, save_time=True)
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pass
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def parse_args():
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# If there is no additional parameters in the command line, run the default models.
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if len(sys.argv) == 1:

‎online_only_material.docx

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‎result/CaseStudy/CC.csv

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‎result/CaseStudy/CC.png

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‎result/CaseStudy/CE_1.csv

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‎result/CaseStudy/CE_1.png

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