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import os, sys,string,re,glob |
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import json |
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import csv |
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import copy |
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import pathlib |
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import time |
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folder = str(pathlib.Path(__file__).parent.resolve()) |
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func_num_dic = { |
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"riscv" : 568, |
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"pulp" : 698, |
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"xcore" : 188 |
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} |
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wrong_lis_all = [] |
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wrong_stmt = [] |
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err_def_dic = {} |
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def get_wrong_list(): |
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global wrong_stmt |
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global wrong_lis_all |
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global err_def_dic |
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with open(folder+"/wrong_func_list_def.csv", 'r', encoding='utf-8') as fcsv: |
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reader = csv.reader(fcsv) |
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for row in reader: |
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if row[0] == "idx": |
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continue |
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wrong_stmt.append(row[0].strip().lower() + " " + row[1].strip().lower() + " " + row[2].strip().lower()) |
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wrong_lis_all.append(" ".join(row)) |
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if " ".join([row[2], row[3]]) not in err_def_dic.keys(): |
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err_def_dic[" ".join([row[2], row[3]])]= 1 |
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else: |
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err_def_dic[" ".join([row[2], row[3]])] += 1 |
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def calculate_accuracy(): |
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func_res = {} |
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stable_stmt_dic = {} |
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all_func_lis = [] |
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global wrong_stmt |
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global wrong_lis_all |
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global err_def_dic |
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total_dic = {} |
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wrong_dic = {} |
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asm_file = [] |
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for line in open(folder+"/result.jsonl", 'r', encoding="utf-8"): |
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dic = json.loads(line) |
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all_func_lis.append(dic["File"].strip().lower() + " " + dic["Func"].strip().lower() + " " + dic["Target"].strip().lower()) |
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if int(dic["vega_pre"]) == 1: |
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if " ".join([dic["Target"], dic["Module"], dic["File"], dic["Func"]]) not in func_res.keys(): |
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func_res[" ".join([dic["Target"], dic["Module"], dic["File"], dic["Func"]])] = [dic["vega_code"].replace("zmtarzm", dic["Target"])] |
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else: |
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func_res[" ".join([dic["Target"], dic["Module"], dic["File"], dic["Func"]])].append(dic["vega_code"].replace("zmtarzm", dic["Target"])) |
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if " ".join([dic["Target"], dic["Module"]]) not in stable_stmt_dic.keys(): |
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stable_stmt_dic[" ".join([dic["Target"], dic["Module"]])] = [0, 0] |
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if dic["Stable"].lower() == "true": |
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stable_stmt_dic[" ".join([dic["Target"], dic["Module"]])][0] += 1 |
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stable_stmt_dic[" ".join([dic["Target"], dic["Module"]])][1] += 1 |
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else: |
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stable_stmt_dic[" ".join([dic["Target"], dic["Module"]])][1] += 1 |
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if dic["Target"] + " " + dic["Module"] not in total_dic.keys(): |
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total_dic[dic["Target"] + " " + dic["Module"]] = [dic["File"].strip() + " " + dic["Func"].strip() + " " + dic["Target"].strip()] |
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wrong_dic[dic["Target"] + " " + dic["Module"]] = [] |
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else: |
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total_dic[dic["Target"] + " " + dic["Module"]].append(dic["File"].strip() + " " + dic["Func"].strip() + " " + dic["Target"].strip()) |
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if dic["File"].strip().lower() + " " + dic["Func"].strip().lower() + " " + dic["Target"].strip().lower() in wrong_stmt: |
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wrong_dic[dic["Target"] + " " + dic["Module"]].append(dic["File"].strip() + " " + dic["Func"].strip() + " " + dic["Target"].strip()) |
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if dic["ans_code"].replace(" ", "") != dic["vega_code"].replace(" ", "") or dic["ans_pre"] != dic["vega_pre"]: |
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wrong_dic[dic["Target"] + " " + dic["Module"]].append(dic["File"].strip() + " " + dic["Func"].strip() + " " + dic["Target"].strip()) |
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if dic["ans_code"] != dic["vega_code"]: |
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wrong_lis_all.append(" ".join([dic["File"], dic["Func"], dic["Target"], dic["Module"], "Err_V"])) |
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if dic["ans_pre"] != dic["vega_pre"]: |
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wrong_lis_all.append(" ".join([dic["File"], dic["Func"], dic["Target"], dic["Module"], "Err_CS"])) |
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all_func_lis = list(set(all_func_lis)) |
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with open(folder+"/Fig8_Acc.csv", 'a', encoding='utf-8', newline="") as f: |
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f_csv = csv.writer(f) |
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avg_dic = {} |
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all_dic = {} |
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for k in total_dic.keys(): |
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Correct_Func_Num = len(list(set(total_dic[k])))-len(list(set(wrong_dic[k]))) |
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Total_Func_Num = len(list(set(total_dic[k]))) |
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Accuracy_Func = 1-round(len(list(set(wrong_dic[k]))) * 1.0 / len(list(set(total_dic[k]))), 3) |
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Wrong_Func_Percentage = round(len(list(set(wrong_dic[k]))) * 1.0 / len(list(set(total_dic[k]))), 3) |
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Pre_Equal_1_Stmt_Percentage = round(stable_stmt_dic[k][0]/stable_stmt_dic[k][1], 3) |
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Pre_Less_1_Stmt_Percentage = 1 - round(stable_stmt_dic[k][0]/stable_stmt_dic[k][1], 3) |
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if k.split(" ")[0] not in avg_dic.keys(): |
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avg_dic[k.split(" ")[0]] = Accuracy_Func |
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all_dic[k.split(" ")[0]] = Correct_Func_Num |
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else: |
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avg_dic[k.split(" ")[0]] += Accuracy_Func |
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all_dic[k.split(" ")[0]] += Correct_Func_Num |
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tem_k = k.replace("PULP", "RI5CY") |
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f_csv.writerow(tem_k.split(" ") + [Correct_Func_Num, Total_Func_Num, Accuracy_Func, Wrong_Func_Percentage, Pre_Equal_1_Stmt_Percentage, Pre_Less_1_Stmt_Percentage]) |
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for k in avg_dic: |
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if k.lower() == "riscv": |
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f_csv.writerow([k, "AVG", round(avg_dic[k] / 7.0, 3)]) |
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f_csv.writerow([k, "ALL", round(all_dic[k] / func_num_dic[k.lower()], 3)]) |
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elif k.lower() == "pulp": |
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f_csv.writerow(["RI5CY", "AVG", round(avg_dic[k] / 7.0, 3)]) |
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f_csv.writerow(["RI5CY", "ALL", round(all_dic[k] / func_num_dic[k.lower()], 3)]) |
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else: |
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f_csv.writerow([k, "AVG", round(avg_dic[k] / 6.0, 3)]) |
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f_csv.writerow([k, "ALL", round(all_dic[k] / func_num_dic[k.lower()], 3)]) |
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for k in func_res.keys(): |
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Tar_Path = folder + "/../ForkFlow/VEGA_Code/" + "/".join(k.split(" ")) + ".cpp" |
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Tar_Path = Tar_Path.replace("enum/NodeType", "enum NodeType") |
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Tar_Path = Tar_Path.replace("enum/CondCode", "enum CondCode") |
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Tar_Path = Tar_Path.replace("ExpandSSRInsts/ExpandPseudo", "ExpandSSRInsts/ExpandSSRInsts") |
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if os.path.exists(Tar_Path): |
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with open(Tar_Path, 'w') as file: |
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for idx, l in enumerate(func_res[k]): |
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if idx < len(func_res[k])-1: |
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file.write(l) |
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file.write("\n") |
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else: |
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file.write(l) |
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else: |
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print(Tar_Path) |
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return total_dic |
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if __name__ == '__main__': |
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get_wrong_list() |
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with open(folder+"/Fig8_Acc.csv", 'w', encoding='utf-8', newline="") as f: |
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f_csv = csv.writer(f) |
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f_csv.writerow(["Target", "Module", "Correct", "Total", "Accurate", "Inaccurate", "Confidence Score≈1.00", "Confidence Score in [0.50, 1.00)"]) |
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total_dic = calculate_accuracy() |
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wrong_lis_all = list(set(wrong_lis_all)) |
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with open(folder+"/wrong_list_all.csv", 'w', encoding='utf-8', newline="") as f: |
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f_csv = csv.writer(f) |
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for err in wrong_lis_all: |
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f_csv.writerow(err.split(" ")) |
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with open(folder+"/../ForkFlow/wrong_list_all.csv", 'w', encoding='utf-8', newline="") as f: |
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f_csv = csv.writer(f) |
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for err in wrong_lis_all: |
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f_csv.writerow(err.split(" ")) |
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wrong_dic = {} |
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with open(folder+"/wrong_list_all.csv", 'r', encoding='utf-8') as f: |
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f_csv = csv.reader(f) |
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for row in f_csv: |
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if " ".join([row[-3].lower(), row[-1].lower()]) not in wrong_dic.keys(): |
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wrong_dic[" ".join([row[-3].lower(), row[-1].lower()])] = 1 |
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else: |
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wrong_dic[" ".join([row[-3].lower(), row[-1].lower()])] += 1 |
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target_func_num_dic = {} |
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for k in total_dic: |
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if k.split(" ")[0].lower() not in target_func_num_dic: |
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target_func_num_dic[k.split(" ")[0].lower()] = len(list(set(total_dic[k]))) |
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else: |
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target_func_num_dic[k.split(" ")[0].lower()] += len(list(set(total_dic[k]))) |
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with open(folder+"/Table2.csv", 'w', encoding='utf-8', newline = "") as f: |
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f_csv = csv.writer(f) |
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for k in target_func_num_dic: |
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for err_type in ["err_v", "err_cs", "err_def"]: |
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if k + " " + err_type in wrong_dic.keys(): |
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f_csv.writerow([k.replace("pulp", "ri5cy"), err_type, round(float(wrong_dic[k + " " + err_type]) / float(target_func_num_dic[k]), 3)]) |
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else: |
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f_csv.writerow([k.replace("pulp", "ri5cy"), err_type, 0]) |
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