Android问题:Test run failed: Instrumentation run failed due to 'java.lang.RuntimeException'

Android 14.0 开机广播接收慢广播延迟问题分析 在14.0的系统rom定制化开发中,在某些时候在接收开机广播的时候发现,过了一分钟才收到开机广播,接下来就需要分析下 开机广播的相关分发机制,来处理下相关问题 阅读详情

初学Android手机单元测试,遇到了很多问题,其中好多问题都在网上搜索解决了,,但这个问题Test run failed: Instrumentation run failed due to 'java.lang.RuntimeException'  没有解决

 

源码如下:

package hkrt.quickpass.test;


import static org.junit.Assert.*;
import hkrt.quickpass.MainActivity;

import org.junit.After;
import org.junit.AfterClass;
import org.junit.Before;
import org.junit.BeforeClass;
import org.junit.Test;

import android.test.ActivityInstrumentationTestCase2;

public class MainAcvitityTest extends ActivityInstrumentationTestCase2<MainActivity> {

 public MainAcvitityTest(Class<MainActivity> activityClass) {
  super(activityClass);
  // TODO Auto-generated constructor stub
 }

 @BeforeClass
 public static void setUpBeforeClass() throws Exception {
 }

 @AfterClass
 public static void tearDownAfterClass() throws Exception {
 }

 @Before
 public void setUp() throws Exception {
 }

 @After
 public void tearDown() throws Exception {
 }
 
 @Test
 public void testSum(){
  
  MainActivity mainactivity = new MainActivity();
  
  int result = mainactivity.sum(3, 4);
  assertEquals(5,result);
 }

}

 

 

配置文件AndroidManifest.xml源码如下:

<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
    package="hkrt.quickpass.test"
    android:versionCode="1"
    android:versionName="1.0" >

    <uses-sdk android:minSdkVersion="8" />
 <!-- **** JUnit测试专用,正式发布需要移除 *** -->
    <uses-permission android:name="android.permission.RUN_INSTRUMENTATION" />
    <instrumentation
        android:name="android.test.InstrumentationTestRunner"
        android:targetPackage="hkrt.quickpass" />

    <application
        android:icon="@drawable/ic_launcher"
        android:label="@string/app_name" >
        <uses-library android:name="android.test.runner" />
    </application>

</manifest>

 

后来找到是构造函数的事,于是把泛型去掉, 然后用不带参数的构造函数,在构造函数里调用super(“com.xxx.xxx”, Activity.class); 就Ok了。

只要把测试类中构造函数修改成

 public MainActivityTest() {
  super("hkrt.quickpass.MainActivity", MainActivity.class);
 }

问题得以解决

使用 MATLAB/Simulink 构建一个基于PID控制的直流电机速度调节系统模型 直流电机(DC Motor)因其结构简单、成本低廉、易于控制等特点,广泛应用于各种工业自动化和家用电器中。PID控制器作为最常用的控制策略之一,在直流电机的速度调节系统中具有重要应用价值。通过合理调整比例(P)、积分(I)、微分(D)三个参数,可以实现对电机速度的精确控制。本教程将带你使用构建一个基于PID控制的直流电机速度调节系统模型,涵盖电机模型构建、PID控制器设计、系统仿真与调试等多个方面。本文介绍了如何使用构建一个基于PID控制的直流电机速度调节系统模型。 阅读详情

相关推荐

suse ha for sap scale-up性能优化场景安装配置

suse ha for SAP安装配置

jetliu05的博客 2135

CTS 测试方式,CtsMediaTestCases 媒体包放到SD卡本地测试

在跑cts-on-gsi 在配置了翻墙VPN 的情况下还是遇到fail,复现步骤如下: 1、烧写load后,替换GSI ,GSI版本是7919922。 2、执行 run cts-on-gsi -m CtsMediaTestCases 时剩下如下fail。 android.media.cts.MediaCodecCapabilitiesTest#testAvcBaseline12 fail junit.framework.AssertionFailedError: Stream did not p

小陈的博客 5636

Matlab实战:水声信号DEMON谱分析从入门到精通(附完整源码)

本文详细介绍了使用Matlab进行水声信号DEMON谱分析的完整流程,从基础理论到实战应用,涵盖信号预处理、包络提取、频谱分析等关键步骤,并附完整源码。特别针对水声通信中的低信噪比环境,提供了多种优化方案和性能提升技巧,帮助读者快速掌握这一水下目标检测的核心技术。

gamma的博客 295

android CTS和GTS测试FAIL项解决集锦

Fail的一些修改   1、直接设置问题 estUnknownSourcesOffByDefault fail junit.framework.AssertionFailedError: Deviceshould not ship with 'Unknown Sources' enabled by default.expected: butwas: ata

2万+

盘点那些好用的SAP FIORI App(二) -上传凭证Upload Journal Entry

SAP FICO运维问题及解决方案分享

lijinbao171022的博客 1542

Carsim和Matlab/Simulink联合仿真时Carsim匹配不到Matlab的解决方法

      本人使用Carsim和Matlab/Simulink联合仿真做过用机器学习方法解决ACC和简单换道的仿真,曾将8.02 2016 2018三个版本Carsim反复安装N次,有相同方向的同学或安装过程遇到问题的同学可以加本人qq505625869一起交流经验心得。 一.前言       Carsim是一款很好的联合仿真软件,使用Carsim,可以避免搭建很多非常复杂的动力学模型

Kaikai_Liao的博客 8766

Test run failed: Instrumentation run failed due to 'Process crashed.'解析

在使用基于Instrumentation测试框架如robotium时,Test run failed: Instrumentation run failed due to 'Process crashed.'这个报错估计大多数人都遇到过,Android的应用是以Linux进程的概念在运行的,而Instrumentation与被测应用运行在同一个进程中,当被测应用的进程在Instrumentatio

hunterno4的专栏 2万+

Box2D-Lite源码阅读笔记(2)

0.初始化函数 static void InitDemo(int index) { world.Clear(); numBodies = 0; numJoints = 0; bomb = NULL; demoIndex = index; demos[index](bodies, joints); } 在这个初始化函数中,一般会默认执行index=0的函数,这个函数就是一个示例,因为示例比较简单,所以也便于研究,示例效果如下。 就是一个方块。 进入第一个示例函数。(忽略我加的注释)

ARTELE的博客 975

Test run failed: Instrumentation run failed due to 'Process crashed.'

网上的notepad.apk示例,只有apk的情况下自动化 1、从源码打包一个APK 2、对apk重新签名,package name:和Main Acitivyt:(查看附件)   3、将apk安装到手机后,启动测试 报错:Test run failed: Instrumentation run failed due to 'Process crashed.'   网上找了很多都...

jy503160的专栏 733

centos7 python3.12.1 报错 No module named _ssl,坑我几个小时

网上的教程很多,但几乎是无效的,跟着我这教程,可以完美解决。

Amio_的博客 1万+

太速科技-基于DSP TMS320C6678+FPGA XC7V690T的6U VPX信号处理卡

当多块盘并行工作时,存储阵列读写带宽成指数增长,2块盘则≥4GB/s,4块盘则≥8GB/s,以此类推。存储阵列的存储容量由单盘配置决定,目前单块NVME存储盘存储容量可选择512GB、1TB、2TB、4TB、8TB、16TB。硬盘管理通过文件系统,FPGA的PCIeX4与 6U VPX主板互联,操作系统直接映射管理硬盘,也可以通过前面板QSFP+ 光纤导出给其他服务器设备。本方案优势就是集中的进行采集,存储,计算一体化,降低硬件成本、功耗,减小设备体积、重量。信号处理,无线电通信领域。

Orihard_的博客 678

android studio 测试两种测试用例的切换问题

ANDROID STUDIO 1.5使用JUNIT单元测试,以及“TEST RUNNING STARTEDTEST RUNNING FAILED: INSTRUMENTATION RUN FAILED DUE TO 'JAVA.LANG.RUNTIMEEXCEPTION' EMPTY TEST SUITE” 2015 年 11 月 26 日 默默 发表回复 331 浏览

Working_Harder的专栏 6334

基于TKVA与深度学习的量子点微型光谱仪光谱重建改进算法研究

本文提出了一种融合深度学习的改进TKVA算法(TKVA-DL),用于提升量子点微型光谱仪的光谱重建精度。该方法创新性地结合深度先验网络与注意力机制,通过自适应学习光谱隐式先验替代传统固定正则化项,并动态加权探测器响应通道。实验表明,所提方法在仿真和实测数据上均显著优于传统TKVA及主流深度学习算法,RMSE降低35%,PSNR提升4.2 dB,同时保持物理可解释性。完整MATLAB实现验证了方法的有效性与可复现性,为微型光谱仪的高精度重建提供了新思路。 关键词:量子点微型光谱仪;光谱重建;Tikhonov正

AI的内部世界的博客 81

Android单元测试时,报异常 Test run failed: Instrumentation run failed due to 'Process crashed

如果使用的 androidx,那么该异常有可能是由于build.gradle中的 testInstrumentationRunner "android.support.test.runner.androidJUnitRunner" 这句引起的,将其改为 testInstrumentationRunner "androidx.test.runner.androidJUnitRunner" 即可 ...

花间一壶酒的博客 6024

jdk-8u144-linux-x64.tar.gz.

该资源为搭建Linux系统Java环境用,资源的后缀为tar.gz格式的压缩包

怀旧版电脑GBC模拟器

再现原版GBC掌机的界面,电脑运行连游戏界面都一样大小,很怀旧,很真实,(英文版)强烈推荐,很小的文件,很难才找到的。

[Android]运行测试用例时Test running failed: Instrumentation run failed due to 'Process crashed.'

网上找的各种方法跟我的对不上,毕竟我才写了一段 @Test public void onCreate() { onView(withId(R.id.sileOpenBtn)) .check(matches(isDisplayed())); } 运行不动,找了以前可以运行的例子对比后发现: testInstrumentationRunner "android...

weixin_39049564的博客 3223

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

解决Test running failed: Instrumentation run failed due to 'java.lang.IncompatibleClassChangeError'

Test running failed: Instrumentation run failed due to 'java.lang.IncompatibleClassChangeError' 问题解决方法 在 Android Studio执行测试(我是基于Robotium的测试)的时候报错: Test running failed: Instrumentation run failed du

Cheers Li 软件测试博客 1万+

SAP B1中文培訓教材_SDK

公司内部培训资料,对开发工具 ,DIAPI, UIAPI,Add-ON有详细解说

android 单元测试Test run failed: Test run failed to complete. Expected 1 tests, received 0

本人学习android不久, 今天在写弄一个单元测试的时候老是报 Test run failed: Test run failed to complete. Expected 1 tests, received 0错误,我在google中查了很多资料,有的说是得有一个空构造函数,我也这么做了,可是依然没有效果, 我一一检查是不是自己没有功能配置文件中加入: &lt;instrument...

207

android测试出错,android 单元测试出现错误及改正

1.Test run failed:Instrumentation run failed due to 'java.lang.ClassNotFoundException'原因是找不到单元测试的类,后来经过仔细看代码及网上查找终于解决,现在记录下来便于以后查找解决办法,问题简单下次就记得了,但是做的项目多了,问题也多了就易忘记或遗漏,好记性不如烂笔头真的一点不错。从头再来,遇到问题就记录下来。解决...

weixin_42103587的博客 613

Robotium自动化测试:Test run failed to complete. Expected 1 tests, received 0

文章转载来源:http://www.robotium.cn/archives/446 作者: HAO.SHEN 最近经常听到用Robotium进行自动化测试是遇到Test run failed: Test run failed to complete. Expected 1 tests, received 0的问题,今天本人恰好也遇到了此问题: 原因1: 这里说一下我的问题

yunbin_7的专栏 1764

Test run failed: Instrumentation run failed due to 'java.lang.ClassNotFoundException'

没有引入JUnit,查看配置里面是否勾选了JUnit.  Project -> Properties -> Java Build Path -> Order and Export and check JUnit if it is unchecked

坚持自己! 1138

Robotium自动化测试运行遇到问题:Test run failed: Instrumentation run failed due to 'java.lang.ClassNotFoundExcep...

在跑Robotium自动化测试代码时,报错Test run failed: Instrumentation run failed due to 'java.lang.ClassNotFoundException'。采用此网址提供的方法:http://blog.csdn.net/lychbeyond/article/details/39051031 进行操作,还是会报错。 后来发现问题...

weixin_30765475的博客 232

(转)如何进行Android单元测试

如何进行Android单元测试 Menifest.xml中加入: <application>中加入: <uses-library android:name="android.test.runner" /> <application>外面加入: <uses-permission android:name="android.permiss...

weixin_30716141的博客 190

Robotium执行过程中常见crashed问题1:应用不完全关闭导致再次打开应用时crash

执行过程中INSTRUMENTATION_RESULT: shortMsg=Process crashed.导致执行中断, 以下是其中的一种情况,LOG如下 08-02 18:30:48.008 E/HwLauncher( 3466): Launcher dialog dismiss failed : java.lang.IllegalArgumentException: no d

杨小扬的专栏 4400

android权限列表

public static final String BROADCAST_PACKAGE_REMOVED  允许应用程序发出一个应用程序被删除的通知。  常量值: "android.permission.BROADCAST_PACKAGE_REMOVED"  public static final String CALL_PHONE  允许应用程序发起一个电话呼叫而不需要经拨号器用户界面确认。  常量值: "android.permission.CALL_PHONE"  public static fina

fenghome的专栏 1万+

appium android app 刚启动就闪退,appium log 提示 shortMsg=Process crashed

部分手机默认关闭io.appium.uiautomator2.server应用自启权限,导致Android app运行报错:

zy_888的专栏 3602
上一篇: Linux服务器性能评估与优化
下一篇: 基于 JUnit 使用 Feed4JUnit 实现数据与代码分离的测试
lychbeyond
博客等级 码龄22年 168粉丝 12原创
评论 2
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值