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Using Semantic Similarity for Input Topic Identification in Crawling-based Web Application Testing

Statistics

results.ods: The statistics

forms.csv: A list of the subject forms

easy-forms.txt: A list of the 35 simple forms used in the second experiment, with actions to submit the forms

Programs

train_and_test.py: For running the proposed natural-language method

rule_match.py: For running the rule-based method

combine.py: For getting results for the RB+NL-n, RB+NL-m, and RB+NL-b methods

executor.py: A web driver for running the second expeirment

preprocess.py: A library for feature extraction

Corpus and the labeled topics

forms: Offline cache of the subject forms

corpus: Preprocessed corpus from forms

label-all-corpus.json: Labeled topics of all 985 input fields from the 100 subject forms used in the experiments

Experimental results

trial/NUM-training.zip: The first experiment, using NUM% as training data

trial/experiment2.zip: The second experiment, using 7 (20%) of the 35 forms as training data

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Experimental data of the ASE 2016 paper

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