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How to use gdb_report method in autotest

Best Python code snippet using autotest_python

crash_handler.py

Source:crash_handler.py

...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' %...

whole_code.py

Source:whole_code.py

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="https://www.yelp.com/biz/FH978pIP1TLRuPAH-MbWIQ/review_feed?rl=en&q=&sort_by=rating_asc"41burl2="https://www.yelp.com/biz/EGS6y6WsPkNs8PZ2X6bHOA/review_feed?rl=en&q=&sort_by=relevance_desc"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)54vectorizer.fit(reviews)55X=vectorizer.transform(reviews)#result 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()82model.fit(X_train, 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()100model.fit(xtrain, 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()108tr.fit(X_train, 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() #åé¿è¡¥ç118gdb.fit(X_train, 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()127rng.fit(X_train, 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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