How to use gdb_report method in autotest

Best Python code snippet using autotest_python Github


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...43 f.write(data)44 finally:45 f.close()46 if report:47 gdb_report(filename)48 return filename49def get_results_dir_list(pid, core_dir_basename):50 """51 Get all valid output directories for the core file and the report. It works52 by inspecting files created by each test on /tmp and verifying if the53 PID of the process that crashed is a child or grandchild of the autotest54 test process. If it can't find any relationship (maybe a daemon that died55 during a test execution), it will write the core file to the debug dirs56 of all tests currently being executed. If there are no active autotest57 tests at a particular moment, it will return a list with ['/tmp'].58 @param pid: PID for the process that generated the core59 @param core_dir_basename: Basename for the directory that will hold both60 the core dump and the crash report.61 """62 pid_dir_dict = {}63 for debugdir_file in glob.glob("/tmp/autotest_results_dir.*"):64 a_pid = os.path.splitext(debugdir_file)[1]65 results_dir = open(debugdir_file).read().strip()66 pid_dir_dict[a_pid] = os.path.join(results_dir, core_dir_basename)67 results_dir_list = []68 # If a bug occurs and we can't grab the PID for the process that died, just69 # return all directories available and write to all of them.70 if pid is not None:71 while pid > 1:72 if pid in pid_dir_dict:73 results_dir_list.append(pid_dir_dict[pid])74 pid = get_parent_pid(pid)75 else:76 results_dir_list = pid_dir_dict.values()77 return (results_dir_list or78 pid_dir_dict.values() or79 [os.path.join("/tmp", core_dir_basename)])80def get_info_from_core(path):81 """82 Reads a core file and extracts a dictionary with useful core information.83 Right now, the only information extracted is the full executable name.84 @param path: Path to core file.85 """86 full_exe_path = None87 output = commands.getoutput('gdb -c %s batch' % path)88 path_pattern = re.compile("Core was generated by `([^\0]+)'", re.IGNORECASE)89 match = re.findall(path_pattern, output)90 for m in match:91 # Sometimes the command line args come with the core, so get rid of them92 m = m.split(" ")[0]93 if os.path.isfile(m):94 full_exe_path = m95 break96 if full_exe_path is None:97 syslog.syslog("Could not determine from which application core file %s "98 "is from" % path)99 return {'full_exe_path': full_exe_path}100def gdb_report(path):101 """102 Use GDB to produce a report with information about a given core.103 @param path: Path to core file.104 """105 # Get full command path106 exe_path = get_info_from_core(path)['full_exe_path']107 basedir = os.path.dirname(path)108 gdb_command_path = os.path.join(basedir, 'gdb_cmd')109 if exe_path is not None:110 # Write a command file for GDB111 gdb_command = 'bt full\n'112 write_to_file(gdb_command_path, gdb_command)113 # Take a backtrace from the running program114 gdb_cmd = ('gdb -e %s -c %s -x %s -n -batch -quiet' %...

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1#!/usr/bin/env python2# coding: utf-83# In[88]:4import numpy as np5import pandas as pd6import json7import requests8# import sys9# import io10# ## set the http-headers11# In[89]:12ua="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36" 13headers={"User-Agent":ua} #environment setting14# 15# In[90]:16reviews=[]17scores=[]18scoresd={}19def get_review_content(baseurl,start,end):20 21 for i in range(start,end,10):22 url=baseurl + "&start={}".format(i)23 resp = requests.get(url,headers=headers)24 jsnStr=json.loads(resp.text)25 if(len(jsnStr['reviews'])==0):26 print("no more comments, stopping")27 break28 for review in jsnStr['reviews']:29 text=review['comment']['text']30 score=review["rating"]31 #print(json.dumps(review,indent=2))32 text=text.replace("&#39;","_").replace("<br>","")33 reviews.append(text)34 scores.append(score)35 if(score not in scoresd):36 scoresd[score]=037 scoresd[score]+=138 return 'crawling finished'39# In[91]:40burl=""41burl2=""42get_review_content(burl2,0,1000)43# In[92]:44# # take a look on example data45example_index=046print("review: {}".format(reviews[example_index]))47print("score: {}".format(scores[example_index]))48print(scoresd) # see how the score distributed49# In[108]:50from sklearn.feature_extraction.text import CountVectorizer51from sklearn.feature_extraction.text import TfidfVectorizer52# vectorizer = CountVectorizer(max_df=0.8,min_df=0.15)53vectorizer = TfidfVectorizer(max_df=0.8,min_df=0.15) in sparse matrix56# In[94]:57y=[] # if score less or equal to 3, we will assign it to 0 which mean bad review,otherwise we will assign it to 1 which means good review58for i in range(len(scores)):59 if(scores[i]<=3):60 y.append(0)61 else:62 y.append(1)63# In[95]:64print(X.todense()[0])65print('----')66print(X[0])67print(X.shape)68print(reviews[0])69print(len(reviews))70print(len(reviews[0]))71print(vectorizer.get_feature_names())72print(len(y))73# In[96]:74from sklearn.model_selection import train_test_split75X_train,X_test,y_train,y_test = train_test_split(X.todense(),y,test_size=0.21,random_state=42) #split the data to train set and test set76print(X_train.shape)77print(X_test.shape)78print(y_test)79# In[97]:80from sklearn.linear_model import LogisticRegression81model = LogisticRegression(), y_train)83print("training done.")84# In[98]:85y_pred = model.predict(X_test)86print(y_pred)87# In[99]:88from sklearn.metrics import classification_report89r = classification_report(y_test,y_pred)90print(r)91# In[100]:92from sklearn import svm93from sklearn.preprocessing import StandardScaler94#regularization95sc = StandardScaler()96xtrain = sc.fit_transform(X_train)97xtest = sc.transform(X_test)98# In[101]:99model = svm.SVC(), y_train)101y_pred = model.predict(xtest)102print(y_pred)103r = classification_report(y_test,y_pred)104print(r)105# In[102]:106from sklearn import tree107tr = tree.DecisionTreeClassifier(), y_train)109print("training done.")110# In[103]:111tree_predict = tr.predict(X_test)112print(tree_predict)113rc = classification_report(y_test,tree_predict)114print(rc)115# In[104]:116from sklearn.ensemble import GradientBoostingClassifier117gdb = GradientBoostingClassifier() #取长补短, y_train)119print("training done.")120# In[105]:121gdb_pred = gdb.predict(X_test)122gdb_report = classification_report(y_test,gdb_pred)123print(gdb_report)124# In[106]:125import lightgbm as lgb126rng = lgb.LGBMClassifier(), y_train)128print('training done')129# In[107]:130rng_pred = rng.predict(X_test)131rng_report = classification_report(y_test,rng_pred)132print(rng_report)...

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