Update Artifact

INSTALL

@@ -4,8 +4,8 @@ For MethodName task as an example,
 
 - Clone `SIVAND` from "https://github.com/mdrafiqulrabin/SIVAND". Here, we need DD.py, helper.py, and MyDD.py files.
 - In `helper.py`, update `<g_test_file>` (path to a file that contains all selected inputs) and `<g_deltas_type>` (select token or char type delta for DD).
-    - Then, have to modify "load_model_M()" to load a target model (i.e., code2seq) from `<model_path>`, and "prediction_with_M()" to get the predicted name, score, and loss value with `<model>` for an input `<file_path>`.
-    - Also, need to check whether `<code>` is parsable into "is_parsable()" and load method by language (i.e. Java) from "load_method()".
+    - Then, modify "load_model_M()" to load a target model (i.e., code2seq) from `<model_path>`, and "prediction_with_M()" to get the predicted name, score, and loss value with `<model>` for an input `<file_path>`.
+    - Also, check whether `<code>` is parsable into "is_parsable()" and load method according to language (i.e. Java) from "load_method()".
 - Finally, run `MyDD.py` that will simplify programs one by one and save all simplified traces in the `dd_data/` folder.
 
 

README.md

@@ -27,7 +27,7 @@ This repository contains the code of prediction-preserving simplification, and t
 |<img src="./save/sivand.png" alt="Workflow in SIVAND"/>|
 :-------------------------:
 
-[Delta Debugging (DD)](https://www.st.cs.uni-saarland.de/dd/) was implemented with Python 2. We have modified the core modules ([DD.py](https://www.st.cs.uni-saarland.de/dd/DD.py), [MyDD.py](https://www.st.cs.uni-saarland.de/dd/MyDD.py)) to run in [Python 3 (we use Python 3.7.3)](https://github.com/mdrafiqulrabin/dd-py3), and then adopted the DD modules for prediction-preserving program simplification using different models. The approach, SIVAND, is model-agnostic and can be applied to any model by loading a model and making a prediction with the model for a task.
+[Delta Debugging (DD)](https://www.st.cs.uni-saarland.de/dd/) was implemented with Python 2. We have modified the core modules ([DD.py](https://www.st.cs.uni-saarland.de/dd/DD.py), [MyDD.py](https://www.st.cs.uni-saarland.de/dd/MyDD.py)) to run in [Python 3](https://github.com/mdrafiqulrabin/dd-py3) (i.e., Python 3.7.3), and then adopted the DD modules for prediction-preserving program simplification using different models. The approach, SIVAND, is model-agnostic and can be applied to any model by loading a model and making a prediction with the model for a task.
 
 **How to Start**: 
 To apply SIVAND (for MethodName task as an example), first update `<g_test_file>` (path to a file that contains all selected inputs) and `<g_deltas_type>` (select token or char type delta for DD) in `helper.py`. 

helper.py

@@ -58,8 +58,7 @@ def deltas_to_code(d):
 
 def is_parsable(code):
     try:
-        # TODO: check whether <code> is parsable
-        #  [this one is for JAVA program]
+        # Example: check whether <code> (JAVA program) is parsable
         tree = javalang.parse.parse("class Test { " + code + " }")
         assert tree is not None
     except Exception:
@@ -84,15 +83,15 @@ def get_json_data(time, score, loss, code, tokens=None, n_pass=None):
 def load_model_M(model_path=""):
     model = None
     # TODO: load target model from <model_path>
-    #  Example: check <others/DD-Models/dd-M/dd_M.py>
+    #  Example: check <models/dd-M/dd_M.py>
     return model
 
 
 def prediction_with_M(model, file_path):
     pred, score, loss = None, None, None
     # TODO: preprocess <file_path> and evaluate with <model>
     #  and get predicted name, score, and loss
-    #  Example: check <others/DD-Models/dd-M/sm_helper.py>
+    #  Example: check <models/dd-M/sm_helper.py>
     return pred, score, loss
 
 
@@ -101,8 +100,7 @@ def prediction_with_M(model, file_path):
 
 def load_method(file_path):
     try:
-        # TODO: extract name and body from method
-        #  [this one is for JAVA program]
+        # Example: extract name and body from method of JAVA program.
         cmd = ['java', '-jar', JAR_LOAD_JAVA_METHOD, file_path]
         contents = subprocess.check_output(cmd, encoding="utf-8", close_fds=True)
         contents = contents.split()