NTU FYP AY2017-18: Deep Learning for Android UI Testing
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README.md

UI Testing - Deep Learning

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

The following are required for the entire project to be deployed:

  • Android SDK (the following shows sdkmanager --list)
Path Version
build-tools;26.0.1 26.0.1
emulator 27.1.12
extras;intel;Hardware_Accelerated_Execution_Manager 6.2.1
patcher;v4 1
platform-tools 27.0.1
system-images;android-26;google_apis;x86 8
tools 26.1.1
  • Python 3.6 (currently Python 3.6.4 is used)
  • OS must be able to run virtual machines.

Deployment of crawler

  1. Install android SDK You can get android SDK by installing Android Studio or by doing it the manual way.

    The manual way of installation is as follow:

    wget https://dl.google.com/android/repository/sdk-tools-darwin-3859397.zip -P /tmp/;
    unzip /tmp/sdk-tools-darwin-3859397.zip -d ~/android-sdk;
    cd ~/android-sdk/tools/bin;
    yes | ./sdkmanager --licenses;
    ./sdkmanager --update;
    ./sdkmanager "build-tools;26.0.1";
  2. Setup the environment for adb, emulator Add the environment variable ANDROID_HOME by finding out where the android sdk home is located.

    export ANDROID_HOME=~/android-sdk
  3. run pip install requirements

    pip install -r requirements.txt
  4. Create emulators Find the sdkmanager within $ANDROID_HOME/tools/bin

    $ANDROID_HOME/tools/bin/sdkmanager "system-images;android-26;google_apis;x86"

    This is done to install the relevant package image which is used to set up the Android emulator. Do note that you can use your own preferred image in this case. To list the available images, just run

    $ANDROID_HOME/tools/bin/sdkmanager --list

    The next step is to create an Android Virtual Device (AVD) for the emulator using the preset image.

    echo no | $ANDROID_HOME/tools/bin/avdmanager create avd -n avd0 -b x86 -k "system-images;android-26;google_apis;x86" --abi google_apis/x86 

    In our case, we name it avd0. Do take note of the name as we will be using it later on.

    Try running the emulator to see if it works.

    $ANDROID_HOME/emulator/emulator -avd avd0

    If the following error occurs: PANIC: Broken AVD system path. Check your ANDROID_SDK_ROOT value, check that in your android-sdk folder, there contains the following directories: emulator, platforms, platform-tools, system-images. If any of the following doesn't exist, just make an empty directory. More information can be found here.

  5. Running the Python program Do note that sudo permissions might be needed for running all of the following due to the usage of kvm for the emulator in the case that no screen is provided, so install the following within the sudo environment.

    cd crawler && export PYTHONPATH=..; python3 main.py emulator-5554 ../../apk/apk-0 ../../apk2/ avd0 

Extracting useful data for learning

Prior to running any learning models, it is vital for the data collected to be parsed into its respective format so that learning can be done. There are several areas where we could parse data from to obtain important information that will be used later during the learning of model.

  1. Extracting data from database

    sh dataparsing/extract_db.sh #(number of db) && mv clickable*.json ../data/serverdata

    Due to the nature of work, the data collected which is stored into the database will get exponentially slower over time as the database collection gets filled up. Thus, we have decided to change the database collection every now and then to improve the efficiency of collection. Running the extract_db.sh shell file will export and dump the required clickablen.json files into the current folder. These files are required to be located in the data/serverdata folder for further parsing.

  2. Extracting features from PlayStore_Full_2016_01_NoDescription_CSV.csv

    python3 feature_extract.py 

    This will extract all important features from the .csv file containing the application category into a category.txt file within the data/serverdata folder. the .csv file must be located within the data/serverdata folder as well. This will be used for running classification using a logistic regression or a wide and deep model.

  3. Extracting image dimension of the screenshots

    python3 img_dimension_extract.py && cp img_dimension_extract.txt ../data/serverdata

    This extracts all image dimension from the screenshot taken during the testing. The image dimension will be further used in determining the position of the clickable elements and will be used in either the logistic regrssion or wide and deep model.

  4. Extracting sequences

    find . -name 'seqq*.txt' -exec cp {} folder \; 
    python3 sequence_extract.py folder && cp sequence*.txt ../data/serverdata

    Copy and extract all the sequence text files into a single folder then use dataparsing/sequence_extract.py to extract these sequences into two files, sequence-combination-wnd.txt and sequence-combination.txt. These sequences will be used for RNN model and wide and deep model, and should be stored in the data/serverdata folder.

  5. Parsing data from database

    export PYTHONPATH=..; python3 parseJson ed

    All database files will be collected and parsed through using the 'e' argument. It will then be split into positive and negative data based on the user's requirement. 'n': normal sequence tree where 'd': double sequence tree 'r': relaxed version of double sequence tree
    The eventual result will be stored into pdata.txt and ndata.txt which would be used for fastText implementation.

Running the learning model

There are several options which the user could use in running deep learning:

  1. fastText from Facebook We have implemented parseJson.py which allows for the data to be parsed into the format required for running text classification or sentiment analysis using the fastText implementation.

    python3.6 parseJson.py f

    The required data will be in the data folder, containing the files fastTextTrain.txt and fastTextTest.txt. To run the sentiment analysis:

    fasttext supervised -input ./fastTextTrain.txt -output model -lr 0.05 -dim 10 -epoch 10 -minCount 1 && fasttext test model.bin ./fastTextTest.txt 1
  2. Recurrent Neural Network (RNN), LSTM using Tensorflow The next implementation uses LSTM from Tensorflow. To train the model, we will have to first parse the sequence data.

    sh gen_tt.sh 1 9

    This will run generate_traintest.py in parallel for n-grams stemming from 1-gram to 9-gram.

Limitations

The crawler is unable to test certain APKs like those of other languages which contain characters that are non-ASCII, or those like the application 'Power Me Off' since it might shut down the entire emulator. There are also cases of flash games which do not contain any element with the variable clickable:True, and applications requiring login and registration before one could proceed crawling the application.

Examples of such APKs

  • at.alladin.rmbt.android_20214.apk - no clickable buttons

Updates

13 March 2018

  • Fixed issue with argparse for wide learning method
  • Added metavar for several argparse methods
  • Added argparse for parseJson.py
  • Added window_option for crawler
  • Ensure deep and wideanddeep model requires IW IN to be set.

12 March 2018

  • Fixed issue with wide learning method.

11 March 2018

  • Updated README.md to make it look neater.
  • Removed irrelevant files.
  • Added run_wnd.sh to facilitate running of training_model
  • Added argparse for widenrnn.py
  • Fixed issue with setting suffix

9 March 2018

  • Edited gen_tt.sh to run concurrently

8 March 2018

  • Randomized selection of train data and increased training epochs.
  • Added wide and deep implementation to widenrnn.py to find out reason for low accuracy rate
  • Fixed issue with low accuracy rate (due to double softmax)

7 March 2018

  • Added NA for btnclasses that aren't within the field.
  • Fixed learning for wide n deep model.
  • Edited max seq length to be grams if not using iw
  • Fixing little bugs for wide n deep model to run proper.

6 March 2018

  • Changes to the way idslabel are being formulated to improve accuracy.
  • Added function to save and load np so as to reduce debugging time

5 March 2018

  • Minor updates

1 March 2018

  • Fixed issue with gen_embedding.py
  • Fixed issue on the training batch

28 February 2018

  • Solved the issue of wide and RNN model. Now tweaking to find the best possible accuracy.

7 February 2018

  • Improved generation of data set for wide model to match with deep model.
  • Started on doing wide logistic regression model using lower level method.
  • Changed positioning to 3:5 or 5:3 depending on whether it is 480:800 or 800:480. Also, if image size is any different, consider the old method of 3:3.

6 February 2018

  • Added implementation to gather button state from sequences as well.
  • Dataset 12
  • Changed implemenetion for newline in sequence extraction to _NEWLINE_ for easier parsing
  • Changed name for wnd-test.txt in the usage of wide model to just w-test.txt and w-train.txt, saving the naming for wnd-train.txt for wide and deep model instead.

1 February 2018

  • Logistic regression trained.
  • Added sys arg support for easier usage.
  • Added testing of model for RNN and returning the accuracy onto a file
  • Added implementation for RAND_BUTTON, BACK, SCROLL UP, SCROLL DOWN, FLING HORIZONTAL in the case of gen_embedding
  • Added function for turning all null sequence to invalid

30 January 2018

  • Added positional conversion.
  • Added RNN split as individual words using space as delimiter without taking into account punctuations

29 January 2018

  • Reorganized the directory into respective packages.
  • Added a ./run.sh file to make things simpler
  • dataset11
  • added img dimension extract file

24 January 2018

  • Used Gensim to create word embedding of the dataset

22 January 2018

  • Minor changes to sequence_extract.py

17 January 2018

  • Added method to prepare sequence data for forming of word embeddings

16 January 2018

  • Prep data for wide and deep model

14 January 2018

  • Changed directory for screenshot files
  • Changed directory for seqq files
  • Changed to database 8
  • Check if screenshot/dump is present, if present, dont re-dump again

12 January 2018

  • Added in a RELAXED version for DST
  • Added in categoristic addition of feature
  • Added script for finding max F1 in fasttext classification

11 January 2018

  • Edited parsing method for double NST and single NST

4 January 2018

  • Changed data collection method, include sequential
  • Initialize score to -1 instead of 1 since we are not using score for exploration
  • Using dataset6 now for database
  • Changed to dataset7 b/c of mistake in appending 1/-1 to initial score

14 December 2017

  • Using totally random decision of clicking buttons for exploration
  • Increased the iterations to make deeper exploration

13 December 2017

  • Realized there's a lot of None in next_transition_state. Might have to retweak the code a bit

11 December 2017

  • Tweaked the parseJson.py for text.

16 November 2017

  • added storing of data for text. The method of getting state remains unchanged.
  • Stopped closing the android keyboard for it is causing a different state everytime and is non-deterministic.
  • added discriminator for buttons leading to outside the apk

15 November 2017

  • emulator is restarted after 50 counts instead.
  • Started data parsing of JSON format from mongodump

10 November 2017

  • Added functionalities to restart emulator after 5 counts of app testing.

4 November 2017

  • Socket timeout issue persists. Changed TimeoutError back to BaseException, added signal alarm at btn_to_info method and set it to 5 seconds.

3 November 2017

  • Added catch error for fail to click

2 November 2017

  • Taken care of socket timeout error. Restart the entire APK if socket timed out.
  • Fixed issue regarding repeated horizontal scrolls
  • Reduction of rerolling random click button tries
  • Changed state_info for scrolling decision from None to APP_STATE.SCROLLING
  • Fixed bug of not adding increment to counter when trying to get another random btn to click
  • Fixed issue with NoneType error

30 October 2017

  • Added enum states for app crashes
  • Fixed issue with APK that does not have androidmanifest.xml
  • Added timeout for inactivity and for clicking of a single button
  • Solved issue with local variable 'state_info' referenced before assignment

28 October 2017

  • Fixed issue regarding horizontal panes

27 October 2017

  • Added timeout function of 400 seconds for overall testing

26 October 2017

  • Discovered several issues related to why UIautomator stops. They are:
    1. crashes
    2. d(clickable=‘true’) UIautomator instrumentation fails to return a list
    3. no clickable buttons to proceed
    4. login page (random string so can’t enter)
    5. Page loading, but the UIAutomator doesn't wait. causing Key/index error
  • Changed catching of initial error and added logging to information txt
  • Changed catching of monkey error and added logigng

25 October 2017

  • Edited issue with editText
  • Added a signal handler in case of being stuck for too long.

22 October 2017

  • Patched an issue resulting in KeyError

18 October 2017

  • Added check for ASCII name
  • Catch IndexError if no buttons clickable in new state

16 October 2017

  • Added init file for shell

12 October 2017

  • Main error now is with KeyError...
  • Changed commands to allow for multiple emulators
  • Created preprocessing.py for selecting of APK filename into separate text files.

11 October 2017

  • Reason for screenshot being half taken at resolution 480x320 is because the skin is not chosen properly. Prior to this, the command used for creating the avd is: android create avd -n avd1 -b x86 -k "system-images;android-26;google_apis;x86" but this defaults to an avd that is skinless, causing error to arise.

    Available Android Virtual Devices:
    Name: Nexus_5X_API_26
    Device: Nexus 5X (Google)
    Path: /Users/hkoh006/.android/avd/Nexus_5X_API_26.avd
    Target: Google Play (Google Inc.)
    Based on: Android API 26 Tag/ABI: google_apis_playstore/x86
    Skin: nexus_5x
    Sdcard: 100M
    ---------
    Name: testAVD
    Path: /Users/hkoh006/.android/avd/testAVD.avd
    Target: Google APIs
    Based on: Android API 26 Tag/ABI: google_apis/x86
    Sdcard: 100M
    
    
  • The top AVD is created from Android Studio's AVD Manager and the subsequent one is created using the command. Thus, we have to add a skin to the testAVD by running the emulator -avd testAVD -skin 1080x1920 command since -skin flag for the android create avd is not found/deprecated.

10 October 2017

  • Fixed bug in 'Issue with clicking back button prematurely' where the app is reopened using the old method instead of monkey method.
  • Issue with screenshot being half taken. Suspicion to be because the buttons are clicked without waiting, causing transitioning to happen too fast and screenshots to be half taken.
  • Reduced probability for scrolling up and down if scrollable exists in page.

9 October 2017

  • Fixed subprocess call in Utilty.py.
  • Fixed bug in sibs and children args in the case that there are no parents.

8 October 2017

  • Added function to start emulator and unlock screen (removed).
  • Changed subprocess calls to fit in android_home.
  • Added logging function into file to prepare for deployment.
  • Added force stop at the end of testing to prevent flooding of opened applications.
  • Change opening of application method into using adb monkey instead.

6 October 2017

  • Added auto adb install/uninstall and inputting of information into information-{datetime}.txt file in preparation for deployment onto server for automatic crawl
  • information-{datetime}.txt file contains package name and application name of those crawled, including if file can be tested or not.
  • Added scrollable for finding of app to start it
  • Changed tester to return -1 if total_score < 0.5 * len(_scores_arr) so as to allow for the case that an activity has many clickables and isn't stopping for far too long.
  • Added counter limit so as to prevent the testing from happening for far too long on a single apk
  • Added next_transition_state to point to self if clickable doesn't lead to new_state.
  • Added probability of scrolling through the activtiy to allow for greater exploration. This is done according to a probability map if widget is scrollable.
  • Added dump for xml and screenshot of each states into /log/{packagename} folder with each file named according to its state.

2 October 2017

  • If sum of score is less than 1, press back to prevent repetition of clicks.
  • Added option if parent could not be found to change children and siblings to None as well

1 October 2017

  • Fixed autocomplete bug by adding TextView widget into conditional check as well
  • Added time delay when an app first started in view of loading
  • If textbox is not empty, don't set the textbox again

30 September 2017

  • Weird behavior when EditText widget is opened and closed. Two different states as a of insertion_handler appearing after closing the keyboard.
  • Fixed bug in parent_map storage
  • Return None if no parents
  • Using bound as key for buttons is bad because the activity might be scrollable, causing changes in bounds as well
  • Fixed bug where str(info['content description])) actually returns None for btn_to_key() and empty string for xml_btn_to_key()

29 September 2017

  • Issue with autocomplete when adding text, causing UItester to crash. Added option to select first option for autocomplete
  • Issue with having only a single button present on UI, causing deadlock. Add in a press back button after counting to 5.
  • Added hash encoding for key of state and state representation using the button type
  • Added in a check to determine if stored button matches with the button being clicked
  • Issue with clickable elements increasing and decreasing in the same state. Will be appending to the dict any elements that appear so as to prevent any unforeseen circumstances
  • Changed getting parent with bound to getting parent with key since there might be buttons with same bound but different text
  • Added mergence of two dicts for parent to key dict since there might be buttons with different keys in same state, causing the issue of KeyError when searching for child using parent

28 September 2017

  • Fixed bug for rec() in click_els = d(clickable='true')
  • Added clickable factor in finding parent
  • Added check for change in buttons, removing and adding them accordingly
  • Fixed bug for repetition of storage of data

27 September 2017

  • Added visited dict to determine if widget is visited or not using weighted probability
  • Fixed bug of repeated activity in app collection
  • Added conditional that if the current activity has different package name, press back

25 September 2017

  • Added dict representation as an option for reducing abstraction of get_state()
  • Changed get_state() representation by inserting packagename to the front.
  • Added activity name to storage
  • Increased len of state to last 30 so as to reduce chance of collision.

22 September 2017

  • Data dump repetition

20 September 2017

  • Fixed a bug of selecting button even if score is 0
  • Added an implementation for app to move back if score array is entirely 0
  • Added periodic database storage/

18 September 2017

  • Implemented database storing system
  • Fixed bugs for showing of siblings
  • Added get_children(), splitting up siblings

17 September 2017

  • Redid the entire Main.py to suit datacollection and storage into mongodb
  • Redid get_siblings and get_parent methods using strings so as to reduce time taken for search

13 September 2017

  • Transferring from local file storage system over to Mongodb so as to optimize storage and search
  • Removed clickable hash that optimizes and reduce time interval between clicks since there might be different possible click objects in a single activity
  • Fixed bugs for loading of json
  • Changed get_state() method with compressed=false for dump, allowing greater robustness.

11 September 2017

  • Optimized improvement for a few seconds
  • Removed text in clickable storage since it isn't representative of an unchangeable unique key
  • Added parent node to clickable data structure

10 September 2017

  • Determine which activity page is more useful. == giving scores to activity based on no. of clickables
  • Added length parameter into data_activity object for faster matching of length of clickables
  • Added score parameter for optimization.

6 September 2017

  • Added mutation for decision of choosing buttons
  • Initialization of scores changed to 1 instead for mutation
  • Removed subtraction of -1 to score if no change states

4 September 2017

  • Added logging into project
  • Fixed bugs
  • Issue with speed after trying it out with calculator
  • New activities are now added to data structure
  • activity_transition of buttons added to data structure

3 September 2017

  • Changed to python3 for latest implementations.
  • Changed key structure for node to score dictionary
  • Addressed the structure of storage
  • Added storing and loading of data structure in json format

30 August 2017

  • Implemented supervised learning and labeling for the clicks done so as to glean for further information to classify them.
  • Word to score ratio is being kept in a dictionary and stored in json format within a file.

21 August 2017

  • get_state() of UI is done with index of xml dump.
  • Added some random clicks and clicks with filling of text.
  • The analysis of data(descriptions) is to be done, and will most likely be done using a database/RL style

17 August 2017

  • Changing to python wrapper for UI automator for the dump method
  • Using dump method of the current UI in order to get_state() of the current representation of the UI
  • Will be doing analysis on words crawled from many APKs so as to do learning on the button descriptions

15 August 2017

  • The UI Testing automation is done using Appium framework
  • Currently able to obtain all relevant elements within the Android application using just the APK file to run.
  • A basic test is implemented
    • Lacking a good way to get_state() of current activity.
    • The current method is to form a list of resourceID for all elements present on the activity and compare it with the previous state.

TODO:

  • get_state() of activity so that a Dynamic Activity Transition Graph could be formed.

  • Attempt using SCanDroid to obtain the Static Activity Transition Graph so that accurate activity coverage could be measured

  • Implement activity and method coverage