Adding generation and evaluation code along with setup

.gitattributes

@@ -53,3 +53,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+src/evaluation/tree-sitter/build/my-languages_c++.so filter=lfs diff=lfs merge=lfs -text
+src/evaluation/tree-sitter/build/my-languages_kotlin.so filter=lfs diff=lfs merge=lfs -text

README.md

@@ -23,4 +23,39 @@ We propose a new benchmark NoFunEval to evaluate code LMs on non-functional requ
 
 Arxiv Link: https://arxiv.org/pdf/2401.15963.pdf
 
-Work on code release is under progress.
+[Work on code release is under progress.]
+
+# Generation
+### NoFunEdit
+```console
+python3 src/nofunedit_generation.py --data_subset <subset from nofunedit: eg-latency> --model_path <model name from HF: eg-WizardLM/WizardCoder-15B-V1.0> --temperature <temperature to be set for model generation: eg-0> --max_new_tokens <maximum number of new tokens to be generated: eg-5192> --prompt <type of prompt to use from our dataset: eg-base_prompt> --num_samples <number of samples to be generated: eg-1> --precision <floating point format: eg-fp16> --batch_size <number of examples to send to llm engine at once: eg-1>
+```
+### Classification
+```console
+python3 src/classification_generation.py --data_subset <subset from non_func or humanevalclassify: eg-latency> --model <model name from HF: eg-WizardLM/WizardCoder-15B-V1.0> --temperature <temperature to be set for model generation: eg-0> --max_new_tokens <maximum number of new tokens to be generated: eg-5192> --prompt <type of prompt to use from our dataset: eg-base_prompt> --precision <floating point format: eg-fp16> --batch_size <number of examples to send to llm engine at once: eg-1>
+```
+# Evaluation
+```console
+python3 src/evaluation.py --data_subset <subset from nofunedit: eg-latency> --model_path <model name from HF: eg-WizardLM/WizardCoder-15B-V1.0> --prompt <type of prompt to use from our dataset: eg-base_prompt> --num_samples <number of samples to be generated: eg-1> --score_k <K values for score@k: eg-1,5,10,20> --metric <eval_metric to be used: eg-diffbleu>
+```
+
+## Parameters
+
+| Parameter                      | Description                                 |
+| ----------------------------- | ---------------------------------------- |
+| `data_subset`                     | The subset of data to use. Options: `latency`, `resource_util`, `maintainability`, `security`, `runtime_efficiency` for nofunedit. Additionally `humanevalclassify` for classification.|
+| `model_path` | The path of the model from HF. Example: `WizardLM/WizardCoder-15B-V1.0`.
+| `prompt`      | Prompt to use. Options: `base_prompt`, `one-shot`, `chain_of_thought`, `coding_concepts`. |
+| `num_samples` | Number of samples to generate. Example: `1` (We used  `1` for greedy and `20` for higher temperature). **[NoFunEdit - Generation only]**|
+| `max_new_tokens` | Budget for new token generation for a model. Example: `1200` (NoFunEdit: We used `1200` for runtime_efficiency and security for all prompts than CoT where `1500` was used. For others, we used `5192` or max possible limit. Classification: We used `4` for all generations).|
+| `temperature` | Temperature for model generation. Example: `0` (We used `0` for greedy and `0.8` for higher samples) |
+| `score_k` |K vales for Score@K. Example: `1,5,10,20` (Should not be greater than num_samples and is comma separated)  **[Eval only]** |
+| `metric` | Metric to be used for evaluation. Option:  `diffbleu`, `codeql`, `codeql-diffbleu` (to be run after first two params are run), `classification`, `runtime` **[Eval only]**|
+
+#### VLLM Parameters (for generation)
+| Parameter                      | Description                                 |
+| ----------------------------- | ---------------------------------------- |
+| `batch-size` | Batch size. Default: `1`|
+| `precision` | Floating point format: Default: `fp16` |
+|  `tensor_parallel_size` | Default: `1` |
+| `swap_space` | The size (GiB) of CPU memory per GPU to use as swap space: Default: `4` |

eval_setup.sh

@@ -1,18 +1,28 @@
 #!/bin/bash
-mkdir $HOME/codeql-home
+mkdir codeql-home
  
-wget https://github.com/github/codeql-cli-binaries/releases/download/v2.5.0/codeql.zip -P $HOME/codeql-home/
-unzip $HOME/codeql-home/codeql.zip -d $HOME/codeql-home/
+wget https://github.com/github/codeql-cli-binaries/releases/download/v2.5.0/codeql.zip -P codeql-home/
+unzip codeql-home/codeql.zip -d codeql-home/
  
-git clone https://github.com/github/codeql.git $HOME/codeql-home/codeql-repo
-cd $HOME/codeql-home/codeql-repo
+git clone https://github.com/github/codeql.git codeql-home/codeql-repo
+cd codeql-home/codeql-repo
 git checkout 20416ae0342c66aa05bc099af8e5a020b018a978
- 
-echo 'export PATH="$HOME/codeql-home/codeql:$PATH"' >> ~/.bashrc 
-source ~/.bashrc
- 
-codeql resolve languages
-codeql resolve qlpacks
+  
+codeql-home/codeql/codeql resolve languages
+codeql-home/codeql/codeql resolve qlpacks
+
+cd ../../
+
+mv  -v src/evaluation/qls_for_security/cpp/* codeql-home/codeql-repo/cpp/ql/src/
+mv  -v src/evaluation/qls_for_security/python/* codeql-home/codeql-repo/python/ql/src/
+
+cd src/evaluation/tree-sitter/
 
-mv  -v ~/src/evaluation/qls_for_security/cpp/* ~/codeql-home/codeql-repo/cpp/ql/src/
-mv  -v ~/src/evaluation/qls_for_security/python/* ~/codeql-home/codeql-repo/python/ql/src/
+git clone https://github.com/tree-sitter/tree-sitter-cpp.git 
+git clone https://github.com/tree-sitter/tree-sitter-c.git 
+git clone https://github.com/tree-sitter/tree-sitter-python.git 
+git clone https://github.com/tree-sitter/tree-sitter-java.git 
+git clone https://github.com/tree-sitter/tree-sitter-javascript.git 
+git clone https://github.com/tree-sitter/tree-sitter-scala.git 
+git clone https://github.com/jiyee/tree-sitter-objc.git 
+git clone https://github.com/fwcd/tree-sitter-kotlin.git

requirements.txt

@@ -1,23 +1,15 @@
-os
-sys
-re
-json
-pandas
+datasets
 argparse
-csv
-tqdm
 vllm
 nltk
 scipy
 transformers
 jinja2
-datasets
 datetime
 jsonlines
 statistics
-tempfile
-subprocess
 tree_sitter
+tiktoken
 joblib == 1.1.0
 numpy == 1.23.1
 pandas == 1.4.4

src/classification_generation.py

@@ -6,12 +6,12 @@ from transformers import AutoTokenizer
 import jsonlines
 from tqdm import tqdm
 from vllm import LLM, SamplingParams
-
+ 
 #Input all the arguments
 parser = argparse.ArgumentParser()
 parser.add_argument("--data_subset", type = str, default = "latency", help = "type of non-func requirement")
 parser.add_argument("--temperature", type = float, default = 0.0, help = "temperature")
-parser.add_argument("--max_new_tokens", type = int, default = 5192, help = "max length of tokens")
+parser.add_argument("--max_new_tokens", type = int, default = 8, help = "max length of tokens")
 parser.add_argument("--top_p", type = float, default = 0.95, help = "top_p")
 parser.add_argument("--prompt", type = str, default = "base_prompt", help = "type of prompt")
 parser.add_argument("--num_samples", type = int, default = 1, help = "number of samples")
@@ -24,75 +24,72 @@ parser.add_argument("--swap_space", type = int, default = 4, help = "The size (G
 parser.add_argument("--batch_size", type = int, default = 1, help = "Number of examples to send to llm engine at once.")
 args = parser.parse_args()
 argsdict = vars(args)
-
+ 
 def extract_single_predictions(input_string):
-
+ 
     if input_string.strip().split()[0].lower() == "A".lower():
         return "A"
     elif input_string.strip().split()[0].lower() == "B".lower():
         return "B"
     return None
-
-def model_query(all_messages, batch_size = 1):
-
-    llm_tokenizer = AutoTokenizer.from_pretrained(
+ 
+model_basename = args.model_path.split("/")[-1]
+ 
+llm_tokenizer = AutoTokenizer.from_pretrained(
         args.model_path,
-        truncation_side = "left",
-        padding_side = "right",  # padding on the right is needed to cut off padding in `complete_code`
+        truncation_side="left",
+        padding_side="right",  # padding on the right is needed to cut off padding in `complete_code`
+        trust_remote_code=True,
     )
-    if args.num_samples == 1:
-        GREEDY = True
-    else:
-        GREEDY = False
-    assert args.num_samples % batch_size == 0, "num_samples must be divisible by batch_size"
-    sampling_params = SamplingParams(
-                                    n=batch_size, # for multisamples we sample multiple times
-                                    temperature=args.temperature if not GREEDY else 0.0, 
-                                    top_p=args.top_p if not GREEDY else 1.0,
-                                    top_k=50 if not GREEDY else -1,
-                                    max_tokens=args.max_new_tokens,
-                                    stop_token_ids=[llm_tokenizer.eos_token_id])
-    llm = LLM(model=args.model_path,
-            tensor_parallel_size=args.tensor_parallel_size,
-            swap_space=args.swap_space,
-            trust_remote_code=True)
-            # tokenizer="hf-internal-testing/llama-tokenizer" if 'llama' in args.checkpoint_path.lower() else None,)
-    llm_outputs = llm.generate(left_prompts, sampling_params)
-    predictions = [extract_single_predictions(output.outputs[0].text) for output in llm_outputs]
-    return predictions
-
+GREEDY = True
+sampling_params = SamplingParams(
+                                n=1, # for multisamples we sample multiple times
+                                temperature=args.temperature if not GREEDY else 0.0,
+                                top_p=args.top_p if not GREEDY else 1.0,
+                                top_k=50 if not GREEDY else -1,
+                                max_tokens=args.max_new_tokens,
+                                stop_token_ids=[llm_tokenizer.eos_token_id])
+llm = LLM(model=args.model_path,
+        tensor_parallel_size=args.tensor_parallel_size,
+        swap_space=args.swap_space,
+        trust_remote_code=True)
+ 
 dataset_path = os.path.join("datasets",f"{args.data_subset}.jsonl")
-
+ 
+args.num_samples = 1
+ 
 max_tokens=[]
 generations=[]
 left_prompts = []
 right_prompts = []
 data=[]
-
+ 
 with jsonlines.open(dataset_path) as data_file:
     for data_item in data_file:
         data.append(data_item)
         left_prompts.append(data_item["classification_left_prompt"])
         right_prompts.append(data_item["classification_right_prompt"])
-
+ 
 print("Starting model inference...")
-left_predictions = model_query(all_messages=left_prompts, batch_size=args.batch_size)
-right_predictions = model_query(all_messages=right_prompts, batch_size=args.batch_size)
-
+left_llm_outputs = llm.generate(left_prompts, sampling_params)
+left_predictions = [extract_single_predictions(output.outputs[0].text) for output in left_llm_outputs]
+right_llm_outputs = llm.generate(right_prompts, sampling_params)
+right_predictions = [extract_single_predictions(output.outputs[0].text) for output in right_llm_outputs]
+ 
 generations = []
 for i, data_item in tqdm(enumerate(left_predictions)):
     #Model Inference
     curr_sample = data[i]
-    curr_sample["left_output"] = left_predictions[i] 
-    curr_sample["right_output"] = right_predictions[i] 
+    curr_sample["left_output"] = left_predictions[i]
+    curr_sample["right_output"] = right_predictions[i]
     for prompt in ["base_prompt", "coding_concepts","chain_of_thought","one_shot","classification_left_prompt","classification_right_prompt"]:
         if(prompt in curr_sample):
             del curr_sample[prompt]
-    generations.append(curr_sample)   
-    
+    generations.append(curr_sample)  
+   
 generations = pd.DataFrame(generations)
-path = os.path.join("generations","classification",os.path.split(args.model_path)[1],args.data_subset,args.prompt,f"{args.num_samples}_samples")
+path = os.path.join("generations","classification",args.data_subset,os.path.split(args.model_path)[1],args.prompt,f"{args.num_samples}_samples")
 if not os.path.exists(path):
     os.makedirs(path)
 path=os.path.join(path, "generated_outputs.jsonl")
-generations.to_json(path, orient="records", lines=True)
+generations.to_json(path, orient="records", lines=True)

src/evaluation.py

@@ -4,12 +4,9 @@ import jsonlines
 import pandas as pd
 import time 
 from jinja2 import Environment, FileSystemLoader
-import json
 import csv
 from statistics import mean
 from utils import pass_at_k_continuous_vals, diff_bleu, post_process_generations, statistical_significance_test, remove_comments, remove_blank_lines,get_files_with_syntax_errors
-from datasets import load_dataset
-
 
 parser = argparse.ArgumentParser()
 parser.add_argument("--data_subset", type=str, default="latency", help="latency/resource_util/runtime_efficiency/maintenance/security")
@@ -21,12 +18,52 @@ parser.add_argument("--score_k", type=str, default="1,5,10,20", help="K value fo
 parser.add_argument("--metric", type=str, default="runtime", help="runtime/diffbleu/codeql-diffbleu")
 args = parser.parse_args()
 
-generations_path = os.path.join("generations",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples","generated_outputs.jsonl")
-
 args.model = args.model_path.split("/")[-1]
 
+generations_path = os.path.join("generations","edit",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples","generated_outputs.jsonl")
+
+if(args.metric == "classification"):
+    
+    generations_path = os.path.join("generations","classification",args.data_subset,os.path.split(args.model_path)[1],args.prompt,f"{args.num_samples}_samples","generated_outputs.jsonl")
+
+    left_predictions=[]
+    right_predictions=[]
+    left_labels=[]
+    right_labels=[]
+
+    with jsonlines.open(generations_path) as reader:
+        for generation in reader:
+            left_predictions.append(generation['left_output']) 
+            left_labels.append(generation['classification_left_label'])       
+            right_predictions.append(generation['right_output'])
+            right_labels.append(generation['classification_right_label'])       
+
+    left_accuracy = sum([1 if prediction == label else 0 for prediction, label in zip(left_predictions, left_labels)]) / len(left_labels)
+    left_consistency = sum([1 if prediction is not None else 0 for prediction in left_predictions]) / len(left_predictions)
+    right_accuracy = sum([1 if prediction == label else 0 for prediction, label in zip(right_predictions, right_labels)]) / len(right_labels)
+    right_consistency = sum([1 if prediction is not None else 0 for prediction in right_predictions]) / len(right_predictions)
+
+    joint_accuracy = [1 if left_prediction == left_label and 
+                        right_prediction == right_label else 0 
+                        for left_prediction, left_label, right_prediction, right_label 
+                        in zip(left_predictions, left_labels, right_predictions, right_labels)]
+
+    joint_accuracy = sum(joint_accuracy) / len(joint_accuracy)
+
+    result_string = {"Model":args.model, "left_accuracy":round((left_accuracy*100),1), "right_accuracy":round((right_accuracy*100),1), "joint_accuracy":round((joint_accuracy*100),1), "left_consistency":round((left_consistency*100),1), "right_consistency":round((right_consistency*100),1)}
+
+    output_path = os.path.join("evaluation_results","classification",args.data_subset,os.path.split(args.model_path)[1],args.prompt,f"{args.num_samples}_samples")
+    
+    if not os.path.exists(output_path):
+        os.makedirs(output_path)
+    
+    samples = pd.DataFrame([result_string])
+    samples.to_json(os.path.join(output_path,"results.jsonl"), orient="records", lines=True) 
+    print("{}".format(result_string))
+
+
 # To calculate runtimes(Applicable for non-func runtime_efficiency)
-if args.metric == "runtime":
+elif args.metric == "runtime":
 
     start_time = time.time()
     
@@ -40,21 +77,21 @@ if args.metric == "runtime":
 
             for l in range(args.num_samples):
 
-                generated_answers = post_process_generations(generated_answers=generation['generated_answers'][l],model = args.model,prompt = args.prompt,pl = "Python")[1]
+                generated_answers = post_process_generations(generated_answers=generation['generated_answers'][l],model = args.model,prompt = args.prompt,pl = generation['pl'])[1]
                 parsed_generations.append(generated_answers)
 
-            samples.append(dict(problem_id = generation['problem_id'],submission_id_v0 = generation['submission_id_v0'],cpu_time_v0 = generation['cpu_time_v0'],cpu_time_v1 = generation['cpu_time_v1'],input=generation['input'],target=generation['target'],
+            samples.append(dict(problem_id = generation['problem_id'],submission_id_v0 = generation['submission_id_v0'],cpu_time_v0 = generation['cpu_time_v0'],cpu_time_v1 = generation['cpu_time_v1'],input=generation['source_code'],target=generation['target_code'],
             generated_answers=parsed_generations, inference_time=generation['inference_time']))
     
         samples = pd.DataFrame(samples)
-        path = os.path.join("evaluation","pie-perf","generated_outputs.jsonl")
+        path = os.path.join("src","evaluation","pie-perf","generated_outputs.jsonl")
         samples.to_json(path, orient="records", lines = True) 
     
-    env = Environment(loader = FileSystemLoader(os.path.join("evaluation","pie-perf","data","sample")))
+    env = Environment(loader = FileSystemLoader(os.path.join("src","evaluation","pie-perf","data","sample")))
     template = env.get_template('sample_eval_config_template.yaml')
-    output_path = os.path.join("evaluation_results",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples","generated_outputs.report")
+    output_path = os.path.join("evaluation_results","edit",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples","generated_outputs.report")
     rendered_yaml = template.render(output_path = output_path)
-    config_file_path = os.path.join("evaluation","pie-perf","data","sample","sample_eval_config.yaml")
+    config_file_path = os.path.join("src","evaluation","pie-perf","data","sample","sample_eval_config.yaml")
     f=open(config_file_path,"w")
     f.write(rendered_yaml)
     f.close()
@@ -63,7 +100,7 @@ if args.metric == "runtime":
     if not os.path.exists(path):
         os.makedirs(path)
     
-    run_file = os.path.join("evaluation","pie-perf","src","codenet_eval","run_eval.py")
+    run_file = os.path.join("src","evaluation","pie-perf","src","codenet_eval","run_eval.py")
     
     os.system(f'python3 {run_file} --eval_config {config_file_path}')
     k_values = list(map(int,args.score_k.split(",")))
@@ -79,11 +116,10 @@ if args.metric == "runtime":
     
     samples = pd.DataFrame([results])
     samples.to_json(os.path.join(path,"results.jsonl"), orient="records", lines=True) 
-    print(",".join(list(map(str,scores))))
+    print("{}".format(results))
 
 # To calculate diffbleu(Applicable for all splits)
 elif args.metric=="diffbleu":
-    generations_path="final_generations/android/starcoder/base_prompt/1_samples/2023-11-06/generated_outputs.jsonl"
 
     k_values = list(map(int,args.score_k.split(",")))
     overall_score={}
@@ -103,9 +139,9 @@ elif args.metric=="diffbleu":
 
             for l in range(args.num_samples):
 
-                generated_answers = post_process_generations(generated_answers = generation['generated_answers'][l],model = args.model,prompt = args.prompt,pl = "Python")
+                generated_answers = post_process_generations(generated_answers = generation['generated_answers'][l],model = args.model,prompt = args.prompt,pl = generation['pl'])
                 passed += generated_answers[0]
-                diff_score_bleu = diff_bleu(source_code = generation['source_code'],target = generation['target'],generated_answers = generated_answers[1],pl = "Python")
+                diff_score_bleu = diff_bleu(source_code = generation['source_code'],target = generation['target_code'],generated_answers = generated_answers[1],pl = generation['pl'])
                 scores.append(diff_score_bleu)
 
             scores.sort(reverse = True)
@@ -125,15 +161,14 @@ elif args.metric=="diffbleu":
 
     results["Passed"] = (passed*100)/(count*args.num_samples)
     samples = pd.DataFrame([results])
-    path = os.path.join("evaluation_results",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
+    path = os.path.join("evaluation_results","edit",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
     if not os.path.exists(path):
         os.makedirs(path)
-    samples.to_json(os.path.join(path,"results.jsonl"), orient="records", lines=True) 
+    samples.to_json(os.path.join(path,"results_{}.jsonl".format(args.metric)), orient="records", lines=True) 
     print("Pass Rate: {}, DiffBleu Score: {}".format(scores[0],scores[1]))
 
 # To run codeql(Applicable for security and maintenance)
 elif args.metric=="codeql":
-    generations_path="final_generations/security/starcoder/base_prompt/1_samples/2023-11-06/generated_outputs.jsonl"
 
     all_check_paths={}
     query_lang = {}
@@ -144,12 +179,12 @@ elif args.metric=="codeql":
 
             query = generation['codeql_check'].split("/")[-1].split(".ql")[0]
 
-            try:
-                all_check_paths[query].append(generation['codeql_check'])
-            except:
-                all_check_paths[query]=generation['codeql_check']
+            # try:
+            #     all_check_paths[query].append(generation['codeql_check'])
+            # except:
+            all_check_paths[query]=generation['codeql_check']
 
-            code_path="evaluation_results/{}/{}/{}/{}_samples/generated_code/{}/".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
+            code_path="evaluation_results/edit/{}/{}/{}/{}_samples/generated_code/{}/".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
 
             if not os.path.exists(code_path):
                 os.makedirs(code_path)
@@ -165,7 +200,7 @@ elif args.metric=="codeql":
 
             for index in range(len(generation['generated_answers'])):
 
-                code_path_indexed = code_path + "{}_{}{}".format(generation['code_file_path'].split("/")[-2]+"_"+generation['code_file_path'].split("/")[-1].split(ext)[0],index,ext)
+                code_path_indexed = code_path + "{}_{}{}".format(generation['file_path'].split("/")[-2]+"_"+generation['file_path'].split("/")[-1].split(ext)[0],index,ext)
             
                 f=open(code_path_indexed,"w+")
 
@@ -188,13 +223,13 @@ elif args.metric=="codeql":
 
     for query in all_check_paths.keys():
         
-        code_path_generations="evaluation_results/{}/{}/{}/{}_samples/generated_code/".format(args.data_subset,args.model,args.prompt,args.num_samples)
+        code_path_generations="evaluation_results/edit/{}/{}/{}/{}_samples/generated_code/".format(args.data_subset,args.model,args.prompt,args.num_samples)
 
-        code_path_db="evaluation_results/{}/{}/{}/{}_samples/generated_code_db/".format(args.data_subset,args.model,args.prompt,args.num_samples)
+        code_path_db="evaluation_results/edit/{}/{}/{}/{}_samples/generated_code_db/".format(args.data_subset,args.model,args.prompt,args.num_samples)
         if not os.path.exists(code_path_db):
             os.makedirs(code_path_db)
 
-        code_path_results="evaluation_results/{}/{}/{}/{}_samples/generated_code_results/".format(args.data_subset,args.model,args.prompt,args.num_samples)
+        code_path_results="evaluation_results/edit/{}/{}/{}/{}_samples/generated_code_results/".format(args.data_subset,args.model,args.prompt,args.num_samples)
         if not os.path.exists(code_path_results):
             os.makedirs(code_path_results)
 
@@ -217,11 +252,11 @@ elif args.metric=="codeql":
         for generation in reader:
             query = generation['codeql_check'].split("/")[-1].split(".ql")[0]
                 
-            code_path="evaluation_results/{}/{}/{}/{}_samples/generated_code/{}/".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
+            code_path="evaluation_results/edit/{}/{}/{}/{}_samples/generated_code/{}/".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
             scores=[]
-            code_path_results="evaluation_results/{}/{}/{}/{}_samples/generated_code_results/{}.csv".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
-            code_path_generations="evaluation_results/{}/{}/{}/{}_samples/generated_code/{}/".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
-            code_path_db="evaluation_results/{}/{}/{}/{}_samples/generated_code_db/".format(args.data_subset,args.model,args.prompt,args.num_samples)
+            code_path_results="evaluation_results/edit/{}/{}/{}/{}_samples/generated_code_results/{}.csv".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
+            code_path_generations="evaluation_results/edit/{}/{}/{}/{}_samples/generated_code/{}/".format(args.data_subset,args.model,args.prompt,args.num_samples,query)
+            code_path_db="evaluation_results/edit/{}/{}/{}/{}_samples/generated_code_db/".format(args.data_subset,args.model,args.prompt,args.num_samples)
 
             errors=[]
 
@@ -253,7 +288,7 @@ elif args.metric=="codeql":
                     ext=".c"
                     pl="C"
 
-                filename = "{}_{}{}".format(generation['code_file_path'].split("/")[-2]+"_"+generation['code_file_path'].split("/")[-1].split(ext)[0],index,ext)
+                filename = "{}_{}{}".format(generation['file_path'].split("/")[-2]+"_"+generation['file_path'].split("/")[-1].split(ext)[0],index,ext)
 
                 index+=1
 
@@ -267,10 +302,10 @@ elif args.metric=="codeql":
             for k in k_values:
 
                 overall_score[k].append(pass_at_k_continuous_vals(n = args.num_samples,k = k,vals = scores))
-            print(scores)
+
             scores_dump.append(scores)
             scores=[]
-    path = os.path.join("evaluation_results",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
+    path = os.path.join("evaluation_results","edit",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
     f = open(os.path.join(path,"results.txt"),'w')
     f.write(str(scores_dump))
     f.close()
@@ -283,9 +318,9 @@ elif args.metric=="codeql":
     results["syntax_errors"] = syntax_errors
     results["no_of_syntax"] = len(syn_errors)
     samples = pd.DataFrame([results])
-    path = os.path.join("evaluation_results",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
-    samples.to_json(os.path.join(path,"results.jsonl"), orient="records", lines=True) 
-    print(",".join(list(map(str,scores))))
+    path = os.path.join("evaluation_results","edit",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
+    samples.to_json(os.path.join(path,"results_{}.jsonl".format(args.metric)), orient="records", lines=True) 
+    print("{}".format(results))
     
  
 
@@ -295,23 +330,24 @@ elif args.metric == "codeql-diffbleu":
     overall_score={}
     for k in k_values:
         overall_score[k]=[]
-    generations_path = os.path.join("generations",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples","generated_outputs.jsonl")
+    generations_path = os.path.join("generations","edit",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples","generated_outputs.jsonl")
     passed = 0
     count = 0       
     with jsonlines.open(generations_path) as reader:
-        res_path = os.path.split(args.file)[0].split('/')
+
+        res_path = os.path.split(generations_path)[0].split('/')
         res_path.insert(1,"evaluation_results")
-        res_path = os.path.join("/".join(res_path),"results.txt")
+        res_path = os.path.join("/".join(res_path[1:]),"results.txt")
         codeql_results = eval(open(res_path).read())
         for generation,res in zip(reader,codeql_results):
             scores=[]
-
+            count += 1
 
             for l in range(len(generation['generated_answers'])):
                 generated_answers=post_process_generations(generated_answers=generation['generated_answers'][l],model=args.model,prompt=args.prompt,pl=generation['pl'])
-                count += generated_answers[0]
+                passed += generated_answers[0]
 
-                diff_score_bleu=res[l]*diff_bleu(source_code=generation['source_code'],target=generation['target'],generated_answers=generated_answers[1],pl=generation['pl'])
+                diff_score_bleu=res[l]*diff_bleu(source_code=generation['source_code'],target=generation['target_code'],generated_answers=generated_answers[1],pl=generation['pl'])
 
                 scores.append(diff_score_bleu)
 
@@ -327,6 +363,6 @@ elif args.metric == "codeql-diffbleu":
     results["Passed"] = (passed*100)/(count*args.num_samples)
     scores.append((passed*100)/(count*args.num_samples))
     samples = pd.DataFrame([results])
-    path = os.path.join("evaluation_results",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
-    samples.to_json(os.path.join(path,"results.jsonl"), orient="records", lines=True) 
-    print(",".join(list(map(str,scores))))
+    path = os.path.join("evaluation_results","edit",args.data_subset,args.model,args.prompt,f"{args.num_samples}_samples")
+    samples.to_json(os.path.join(path,"results_{}.jsonl".format(args.metric)), orient="records", lines=True) 
+    print("{}".format(results))

src/evaluation/qls_for_security/cpp/cwe_125_mitre-eg-1.ql

@@ -0,0 +1,26 @@
+/**
+ * @name Check for CWE-125 in mitre-eg-1
+ * @description 
+ * @kind problem
+ * @problem.severity warning
+ * @id custom-cpp/cwe-125-mitre-eg-1
+ */
+
+import cpp
+import semmle.code.cpp.controlflow.Guards
+import semmle.code.cpp.rangeanalysis.SimpleRangeAnalysis
+
+from ArrayExpr ae
+
+where
+
+//choose expressions that represent index and size
+ae.getArrayOffset().toString() = "index"
+
+and not ( exists( GuardCondition gc, Expr e, Expr expr | 
+    e.toString() = "index" 
+    and expr.toString() = "size" 
+    and gc.ensuresLt(e, expr, 0, ae.getBasicBlock(), true) ) 
+    and lowerBound(ae.getArrayOffset()) >= 0 )
+
+select ae, "cwe_125 found in"+ae.getFile().toString()

src/evaluation/tree-sitter/build/my-languages_c++.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f34864d8b75a065ec3ba7de234be6881e02c40c19d06e5ca8da55dcf6de69a6
+size 3340728

src/evaluation/tree-sitter/build/my-languages_kotlin.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:858db301394b79240bc75b136ec70c3dc2c96e0d6faae2d6d844183506eaddde
+size 12724728

src/utils.py

@@ -255,47 +255,47 @@ def check_syntax(code,language):
     code += '\n'
     
     if(language.lower()  == "java"):
-        path = 'tree-sitter/tree-sitter-java'
+        path = 'src/evaluation/tree-sitter/tree-sitter-java'
     elif(language.lower() == "python"):
-        path = 'tree-sitter/tree-sitter-python'
+        path = 'src/evaluation/tree-sitter/tree-sitter-python'
     elif(language.lower() == "scala"):
-        path = 'tree-sitter/tree-sitter-scala'
+        path = 'src/evaluation/tree-sitter/tree-sitter-scala'
     elif(language.lower() == "c"):
-        path = 'tree-sitter/tree-sitter-c'
+        path = 'src/evaluation/tree-sitter/tree-sitter-c'
     elif(language.lower() == "c++"):
-        path = 'tree-sitter/tree-sitter-cpp'
+        path = 'src/evaluation/tree-sitter/tree-sitter-cpp'
     elif(language.lower() == "objectivec"):
-        path = 'tree-sitter/tree-sitter-objc'
+        path = 'src/evaluation/tree-sitter/tree-sitter-objc'
     elif(language.lower() == "javascript"):
-        path = 'tree-sitter/tree-sitter-javascript'
+        path = 'src/evaluation/tree-sitter/tree-sitter-javascript'
     elif(language.lower() == "kotlin"):
-        path = 'tree-sitter/tree-sitter-kotlin'
+        path = 'src/evaluation/tree-sitter/tree-sitter-kotlin'
     else:
         return(False)
 
     Language.build_library(
-    'build/my-languages_{}.so'.format(language.lower()),
+    'src/evaluation/tree-sitter/build/my-languages_{}.so'.format(language.lower()),
     [
         path
     ]
     )
 
     if(language.lower() == "java"):
-        LANGUAGE = Language('build/my-languages_java.so', 'java')
+        LANGUAGE = Language('src/evaluation/tree-sitter/build/my-languages_java.so', 'java')
     elif(language.lower() == "python"):
-        LANGUAGE = Language('build/my-languages_python.so', 'python')
+        LANGUAGE = Language('src/evaluation/tree-sitter/build/my-languages_python.so', 'python')
     elif(language.lower() == "scala"):
-        LANGUAGE = Language('build/my-languages_scala.so', 'scala')
+        LANGUAGE = Language('src/evaluation/tree-sitter/build/my-languages_scala.so', 'scala')
     elif(language.lower() == "c"):
-        LANGUAGE = Language('build/my-languages_c.so', 'c')
+        LANGUAGE = Language('src/evaluation/tree-sitter/build/my-languages_c.so', 'c')
     elif(language.lower() == "c++"):
-        LANGUAGE = Language('build/my-languages_c++.so', 'cpp')
+        LANGUAGE = Language('src/evaluation/tree-sitter/build/my-languages_c++.so', 'cpp')
     elif(language.lower() == "objectivec"):
-        LANGUAGE = Language('build/my-languages_objectivec.so', 'objc')
+        LANGUAGE = Language('src/evaluation/tree-sitter/build/my-languages_objectivec.so', 'objc')
     elif(language.lower() == "javascript"):
-        LANGUAGE = Language('build/my-languages_javascript.so', 'javascript')
+        LANGUAGE = Language('src/evaluation/tree-sitter/build/my-languages_javascript.so', 'javascript')
     elif(language.lower() == "kotlin"):
-        LANGUAGE = Language('build/my-languages_kotlin.so', 'kotlin')
+        LANGUAGE = Language('src/evaluation/tree-sitter/build/my-languages_kotlin.so', 'kotlin')
 
     parser = Parser()
     parser.set_language(LANGUAGE)