HMLLDB is a collection of LLDB commands to assist in the debugging of iOS apps.

Overview

HMLLDB is a collection of LLDB commands to assist in the debugging of iOS apps.
中文介绍

Features

  • Non-intrusive. Your iOS project does not need to be modified
  • All commands support real device and simulator
  • All commands support Objective-C and Swift project
  • Some commands provide interactive UI within the APP

Requirements

  • Xcode 12.3
  • 64-bit simulator or real device, iOS 9.0+
  • Debug configuration (or Optimization Level set [-O0]/[-Onone])

Installation

  1. Download all the files. I recommend cloning the repository.
  2. Open up (or create) ~/.lldbinit file, and append the following lines to the end of the file:
command script import /path/to/HMLLDB.py

For example, the command in my computer:
command script import /Users/pal/Desktop/gitProjects/HMLLDB/commands/HMLLDB.py

  1. Restart Xcode, run your own iOS project, click Pause program execution to enter the LLDB debugging mode, enter the command help, if you see the commands described below, the installation is successful.

Commands

Command Description
deletefile Delete the specified file in the sandbox
pbundlepath Print the path of the main bundle
phomedirectory Print the path of the home directory("~")
fclass Find the class containing the input name(Case insensitive)
fsubclass Find the subclass of the input
fsuperclass Find the superclass of the input
fmethod Find the specified method in the method list, you can also find the method list of the specified class
methods Execute [inputClass _methodDescription] or [inputClass _shortMethodDescription]
properties Execute [inputClass _propertyDescription]
ivars Execute [instance _ivarDescription]
plifecycle Print life cycle of UIViewController
redirect Redirect stdout/stderr
push Find UINavigationController in keyWindow then push a specified UIViewController
showhud Display the debug HUD on the key window, it is showing the memory usage, CPU utilization and FPS of the main thread
sandbox Presenting a sandbox browser that can share and delete files
inspect Inspect UIView
environment Show diagnostic environment.
...

All commands in the table can use help <command> to view the syntax and examples. For example, the output of help fmethod:

(lldb) help fmethod
     Find the method.  Expects 'raw' input (see 'help raw-input'.)

Syntax: fmethod

    Syntax:
        fmethod <methodName>  (Case insensitive.)
        fmethod [--class] <className>

    Options:
        --class/-c; Find all method in the class

    Examples:
        (lldb) fmethod viewdid
        (lldb) fmethod viewDidLayoutSubviews
        (lldb) fmethod -c UITableViewController

    This command is implemented in HMClassInfoCommands.py

Example

Some examples use the demo in the Kingfisher project.
It is recommended to click Pause program execution to enter the LLDB debugging mode to execute commands, instead of executing commands by hitting breakpoints.

deletefile

It is recommended to re-run the project after executing the command, because some data is still in the memory.

# Delete all file in the sandbox
(lldb) deletefile -a

# Delete the "~/Documents" directory
(lldb) deletefile -d

# Delete the "~/Library" directory
(lldb) deletefile -l

# Delete the "~/tmp" directory
(lldb) deletefile -t

# Delete the "~/Library/Caches" directory
(lldb) deletefile -c

# Delete the "~Library/Preferences" directory
(lldb) deletefile -p

# Delete the specified file or directory
(lldb) deletefile -f path/to/fileOrDirectory

pbundlepath & phomedirectory

# Print the path of the main bundle
(lldb) pbundlepath
[HMLLDB] /Users/pal/Library/Developer/CoreSimulator/Devices/D90D74C6-DBDF-4976-8BEF-E7BA549F8A89/data/Containers/Bundle/Application/84AE808C-6703-488D-86A2-C90004434D3A/Kingfisher-Demo.app

# Print the path of the home directory
(lldb) phomedirectory
[HMLLDB] /Users/pal/Library/Developer/CoreSimulator/Devices/D90D74C6-DBDF-4976-8BEF-E7BA549F8A89/data/Containers/Data/Application/3F3DF0CD-7B57-4E69-9F15-EB4CCA7C4DD8

# If it is running on the simulator, you can add the -o option to open path with Finder
(lldb) pbundlepath -o
(lldb) phomedirectory -o

fclass & fsubclass & fsuperclass & fmethod

These commands are optimized for Swift, and the namespace can be omitted when entering the Swift class.

fclass: Find all class names that contain the specified string.

(lldb) fclass NormalLoadingViewController
[HMLLDB] Waiting...
[HMLLDB] Count: 1 
Kingfisher_Demo.NormalLoadingViewController (0x102148fa8)

# Case insensitive
(lldb) fclass Kingfisher_Demo.im
[HMLLDB] Waiting...
[HMLLDB] Count: 2 
Kingfisher_Demo.ImageDataProviderCollectionViewController (0x102149a18)
Kingfisher_Demo.ImageCollectionViewCell (0x1021498e8)

fsubclass: Find all subclasses of a class.

(lldb) fsubclass UICollectionViewController
[HMLLDB] Waiting...
[HMLLDB] Subclass count: 10 
Kingfisher_Demo.InfinityCollectionViewController
Kingfisher_Demo.HighResolutionCollectionViewController
...

fsuperclass: Find the super class of a class.

(lldb) fsuperclass UIButton
[HMLLDB] UIButton : UIControl : UIView : UIResponder : NSObject

(lldb) fsuperclass KingfisherManager
[HMLLDB] Kingfisher.KingfisherManager : Swift._SwiftObject

fmethod: Find the specified method in the method list, you can also find the method list of the specified class.

# Find the specified method in the method list. Case insensitive.
(lldb) fmethod viewdidload
[HMLLDB] Waiting...
[HMLLDB] Methods count: 158 
(-) playbackControlsViewDidLoad:
	Type encoding:[email protected]:[email protected]
	Class:AVPlaybackControlsController
(-) turboModePlaybackControlsPlaceholderViewDidLoad:
	Type encoding:[email protected]:[email protected]
	Class:AVPlaybackControlsController
(-) viewDidLoad
	Type encoding:[email protected]:8
	Class:AVNewsWidgetPlayerBehaviorContext
...

# Option -c: Find the method list of the specified class. Case sensitive.
(lldb) fmethod -c ImageCache
[HMLLDB] Waiting...
[HMLLDB] Class: Kingfisher.ImageCache (0x10264c6f8)
Instance methods count: 3. Class method count: 0.
(-) cleanExpiredDiskCache
	Type encoding:[email protected]:8
(-) backgroundCleanExpiredDiskCache
	Type encoding:[email protected]:8
(-) clearMemoryCache
	Type encoding:[email protected]:8

methods & properties & ivars

methods: Execute [inputClass _methodDescription] or [inputClass _shortMethodDescription] properties: Execute [inputClass _propertyDescription] ivars: Execute [instance _ivarDescription]

These commands are optimized for Swift, and the namespace can be omitted when entering the Swift class.

# Syntax
methods [--short] <className/classInstance>
properties <className/classInstance>
ivars <Instance>

(lldb) methods NormalLoadingViewController
[HMLLDB] <Kingfisher_Demo.NormalLoadingViewController: 0x10d55ffa8>:
in Kingfisher_Demo.NormalLoadingViewController:
	Instance Methods:
		- (id) collectionView:(id)arg1 cellForItemAtIndexPath:(id)arg2; (0x10d523f30)
		- (long) collectionView:(id)arg1 numberOfItemsInSection:(long)arg2; (0x10d522a20)
		- (void) collectionView:(id)arg1 willDisplayCell:(id)arg2 forItemAtIndexPath:(id)arg3; (0x10d523af0)
		- (void) collectionView:(id)arg1 didEndDisplayingCell:(id)arg2 forItemAtIndexPath:(id)arg3; (0x10d522cb0)
		- (id) initWithCoder:(id)arg1; (0x10d522960)
...

# These commands can only be used for subclasses of NSObject
(lldb) methods KingfisherManager
[HMLLDB] KingfisherManager is not a subclass of NSObject

plifecycle

Used to print the life cycle of UIViewController.
In a non-intrusive way, and Xcode can set the console font color to make it clearer. It has become one of my favorite commands.

Usage:

  1. Create a Symbolic Breakpoint, and then add the method to be printed in the Symbol line.(e.g. -[UIViewController viewDidAppear:])
  2. Add a Action(Debugger Command), enter the plifecycle command
  3. Check the option: Automatically continue after evaluating actions

I usually use the -i option to ignore some system-generated UIViewController. img1

I often Enable viewDidAppear: and dealloc methods, and the other methods are set to Disable and started on demand, as shown below: img2

Output in Xcode: img3

It should be noted that there are two problems with this command.

  1. Cause UIViewController switching lag.
  2. The following warning may be triggered when starting the APP. You need to click Continue program execution in Xcode to let the APP continue to run.
Warning: hit breakpoint while running function, skipping commands and conditions to prevent recursion.

BTW, the source code provides other ways to use LLDB to print the life cycle.

redirect

Redirect stdout/stder.

# You can redirect the output of Xcode to Terminal if you use the simulator
# Open the terminal, enter the "tty" command, you can get the path: /dev/ttys000
(lldb) redirect both /dev/ttys000
[HMLLDB] redirect stdout successful
[HMLLDB] redirect stderr successful

push

Find UINavigationController in keyWindow then push a specified UIViewController.
Notice: push MyViewController needs to execute [[MyViewController alloc] init] first. If the initializer of the class requires parameters, or the class needs to pass parameters after initialization, this command may cause errors.
The GIF demo didn't use Kingfisher because the UIViewController in the demo depends on the storyboard.
img4

showhud

Display the debug HUD on the keyWindow, it is showing the memory usage, CPU utilization and FPS of the main thread. img5

Tapping the debug HUD will present a new view controller, and its function will be introduced later. img6

sandbox

Presenting a sandbox browser that can share and delete files.
It takes a few seconds to call the command for the first time. img7

inspect

Inspect UIView of the current page.
img8

environment

Show diagnostic environment.
You can see that one of items is [Git commit hash], which is one of the reasons why clone repository is recommended.

(lldb) environment
[HMLLDB] [Python version] 3.8.2 (default, Nov  4 2020, 21:23:28) 
		[Clang 12.0.0 (clang-1200.0.32.28)]
[HMLLDB] [LLDB version] lldb-1200.0.44.2
		Apple Swift version 5.3.2 (swiftlang-1200.0.45 clang-1200.0.32.28)
[HMLLDB] [Target triple] x86_64h-apple-ios-simulator
[HMLLDB] [Git commit hash] 088f654cb158ffb16019b2deca5dce36256837ad
[HMLLDB] [Optimized] False: 28  True: 0
[HMLLDB] [Xcode version] 1230
[HMLLDB] [Xcode build version] 12C33
[HMLLDB] [Model identifier] x86_64
[HMLLDB] [System version] iOS 13.0

If an error occurs

Just-in-time compilation via LLDB is not stable. If an error occurs, please check in order according to the following steps.

  1. pull the latest code. Check the Xcode version, HMLLDB generally only adapts to the latest Xcode version.
  2. Open the ~/.lldbinit file and make sure to import the HMLLDB.py at the end of the file so that its commands are not overwritten.
  3. After launching the APP, click Pause program execution to enter the LLDB debugging mode to execute commands, instead of executing commands by hitting breakpoints. (In general, you can execute commands by hitting breakpoints)
  4. Restart Xcode can solve most problems.
  5. Restart the computer.
  6. After completing the above steps, the command still fails. Please copy the error and post it to Issue, and execute the environment command, its output should also be posted to Issue.

License

HMLLDB is released under the MIT license. See LICENSE for details.

Owner
mao2020
iOS Developer
mao2020
Deep Reinforcement Learning with pytorch & visdom

Deep Reinforcement Learning with pytorch & visdom Sample testings of trained agents (DQN on Breakout, A3C on Pong, DoubleDQN on CartPole, continuous A

Jingwei Zhang 783 Jan 04, 2023
N-Person-Check-Checker-Splitter - A calculator app use to divide checks

N-Person-Check-Checker-Splitter This is my from-scratch programmed calculator ap

2 Feb 15, 2022
Implementation of Kaneko et al.'s MaskCycleGAN-VC model for non-parallel voice conversion.

MaskCycleGAN-VC Unofficial PyTorch implementation of Kaneko et al.'s MaskCycleGAN-VC (2021) for non-parallel voice conversion. MaskCycleGAN-VC is the

86 Dec 25, 2022
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis

Bilateral Denoising Diffusion Models (BDDMs) This is the official PyTorch implementation of the following paper: BDDM: BILATERAL DENOISING DIFFUSION M

172 Dec 23, 2022
CPPE - 5 (Medical Personal Protective Equipment) is a new challenging object detection dataset

CPPE - 5 CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal to allow the study of subordinate categorization

Rishit Dagli 53 Dec 17, 2022
Hydra Lightning Template for Structured Configs

Hydra Lightning Template for Structured Configs Template for creating projects with pytorch-lightning and hydra. How to use this template? Create your

Model-driven Machine Learning 4 Jul 19, 2022
Code for NeurIPS 2020 article "Contrastive learning of global and local features for medical image segmentation with limited annotations"

Contrastive learning of global and local features for medical image segmentation with limited annotations The code is for the article "Contrastive lea

Krishna Chaitanya 152 Dec 22, 2022
Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

taganomaly Anomaly detection labeling tool, specifically for multiple time series (one time series per category). Taganomaly is a tool for creating la

Microsoft 272 Dec 17, 2022
The Python ensemble sampling toolkit for affine-invariant MCMC

emcee The Python ensemble sampling toolkit for affine-invariant MCMC emcee is a stable, well tested Python implementation of the affine-invariant ense

Dan Foreman-Mackey 1.3k Dec 31, 2022
Creating predictive checklists from data using integer programming.

Learning Optimal Predictive Checklists A Python package to learn simple predictive checklists from data subject to customizable constraints. For more

Healthy ML 5 Apr 19, 2022
PyTorch implementation of DirectCLR from paper Understanding Dimensional Collapse in Contrastive Self-supervised Learning

DirectCLR DirectCLR is a simple contrastive learning model for visual representation learning. It does not require a trainable projector as SimCLR. It

Meta Research 49 Dec 21, 2022
A customisable game where you have to quickly click on black tiles in order of appearance while avoiding clicking on white squares.

W.I.P-Aim-Memory-Game A customisable game where you have to quickly click on black tiles in order of appearance while avoiding clicking on white squar

dE_soot 1 Dec 08, 2021
本项目是一个带有前端界面的垃圾分类项目,加载了训练好的模型参数,模型为efficientnetb4,暂时为40分类问题。

说明 本项目是一个带有前端界面的垃圾分类项目,加载了训练好的模型参数,模型为efficientnetb4,暂时为40分类问题。 python依赖 tf2.3 、cv2、numpy、pyqt5 pyqt5安装 pip install PyQt5 pip install PyQt5-tools 使用 程

4 May 04, 2022
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting

Pytorch Pedestrian Attribute Recognition: A strong PyTorch baseline of pedestrian attribute recognition and multi-label classification.

Jian 79 Dec 18, 2022
Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems"

Code Artifacts Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driv

Andrea Stocco 2 Aug 24, 2022
基于PaddleOCR搭建的OCR server... 离线部署用

开头说明 DangoOCR 是基于大家的 CPU处理器 来运行的,CPU处理器 的好坏会直接影响其速度, 但不会影响识别的精度 ,目前此版本识别速度可能在 0.5-3秒之间,具体取决于大家机器的配置,可以的话尽量不要在运行时开其他太多东西。需要配合团子翻译器 Ver3.6 及其以上的版本才可以使用!

胖次团子 131 Dec 25, 2022
Boosted CVaR Classification (NeurIPS 2021)

Boosted CVaR Classification Runtian Zhai, Chen Dan, Arun Sai Suggala, Zico Kolter, Pradeep Ravikumar NeurIPS 2021 Table of Contents Quick Start Train

Runtian Zhai 4 Feb 15, 2022
tf2onnx - Convert TensorFlow, Keras and Tflite models to ONNX.

tf2onnx converts TensorFlow (tf-1.x or tf-2.x), tf.keras and tflite models to ONNX via command line or python api.

Open Neural Network Exchange 1.8k Jan 08, 2023
Group Activity Recognition with Clustered Spatial Temporal Transformer

GroupFormer Group Activity Recognition with Clustered Spatial-TemporalTransformer Backbone Style Action Acc Activity Acc Config Download Inv3+flow+pos

28 Dec 12, 2022
PyTorch Implementation of Small Lesion Segmentation in Brain MRIs with Subpixel Embedding (ORAL, MICCAIW 2021)

Small Lesion Segmentation in Brain MRIs with Subpixel Embedding PyTorch implementation of Small Lesion Segmentation in Brain MRIs with Subpixel Embedd

22 Oct 21, 2022