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By enable API call latency profiling for your VTS HIDL HAL test, you are expected to get. trace files that record each API call happened during the test execution with the passed argument values as well as the return values. performance profiling data that contains the latency of each API call which is also displayed in the VTS dashboard if the dashboard feature is used. Add profiler library to VTS. To enable profiling for your HAL testing, we need to add the corresponding profiler library in vts_test_lib_hidl_package_list.
The name of the profiling library follow the pattern as. We will use Nfc Hal as a running example throughout this section, so, the profiler library name is android. Modify Your VTS Test Case. If you have not already, Codelab for Host-Driven Tests gives an overview of how to write a VTS test case. This section assumes you have completed that codelab and have at least one VTS test case either host-side or target-side which you would like to enable profiling.
This subsection describes how to enable profiling for target-side tests. Target-Side Tests. To enable profiling for host-side tests, follow the same steps by replacing target to host everywhere. Copy an existing test directory. Note nfc could be replaced by the name of your HAL and V1_0 could be replaced by the version of your HAL version with format V _.
Then rename the test name from VtsHalNfcV1_0Target to VtsHalNfcV1_0TargetProfiling everywhere. xml file under the target_profiling directory to push the profiler libraries to target. Add the following lines to the corresponding AndroidTest. Note, if the testing hal relies on a dependent hal e. 0 depends on android. 0we need to push the profiler library for the dependent hal as well. xml file under the target_profiling directory to enable profiling for test. An example AndroidTest.
xml file looks like. Schedule the profiling test. Subscribe the notification alert emails. Please check notification page for the detailed instructions. Basically, now it is all set so let s wait for a iq option 5 minutes strategy or so and then visit your VTS Dashboard. At that time, you should be able to add VtsHalNfcV1_0TargetProfiling to your favorite list. That is all you need to do in order to subscribe alert emails which will sent if any notably performance degradations are found by your profiling tests.
Also if you click VtsHalNfcV1_0TargetProfiling in the dashboard main page, the test result page shows up where the top-left side shows the list of APIs which have some measured performance data. Where to find the trace files. All the trace files generated during the tests are by default stored under tmp vts-test-trace. config under the test directory with. To change the directory that store the path file, create a configure file e. add following lines to the corresponding AndroidTest.
Custom profiling points and post-processing. Let s assume you have created a performance benchmark binary which could run independently on device, e. Integrate the benchmark as a VTS test. Add benchmark binary to VTS. To package benchmark binary with VTS, add it to vts_test_bin_package_list. mk after the vts_test_bin_packages variable. Add VTS host side script.
The host side script control the benchmark execution and the processing of the benchmark results. It typically contains the following major steps. Register device controller and invoke shell on the target device. Setup the command to run the benchmark on the device. Where path_to_binary represents the full path of benchmark binary on the target device. The default path is data local tmp my_benchmark_test.
Validate benchmark test results. Parse the benchmark test results and upload the metrics to VTS web dashboard. Depends on the output format of the test results, we need to parse the STDOUT content in the return results into performance data points. Currently, VTS supports processing and displaying two performance data type. One is timestamp sample which records the start and end time stamp for a particular operation. The other is vector data sample which records a list of profiling data along with data labels.
Take the vector data sample as example, let s suppose we have parsed the benchmark results into two vectors. One stores the performance data e. latency of the API callthe other stores the corresponding data labels e. the input size of API call. Call AddProfilingDataLabeledVector to upload the iq option 5 minutes strategy data sample to VTS web as follows. Follow the same instruction in Codelab for Host-Driven Tests Write a VTS Test section to create a host-side VTS test using the host side script created in section 2.
Configure the VTS test. Support for native coverage through VTS depends on a functioning instance of the VTS Dashboard, including integration with a build server and a Gerrit server. See the documentation for VTS Dashboard setup and configuration before proceeding. Building a Device Image. The first step in measuring coverage is creating a device image that is instrumented for gcov coverage collection.
This can be accomplished with a flag in the device manifest and a few build-time flags. Let s add the following code segment to the device. This will have no impact on the device when coverage is disabled at build time but will add a read-only device property in the case when coverage is enabled. Next, we can build a device image. The continuous build server must be configured to execute the build command with a few additional flags NATIVE_COVERAGE, and COVERAGE_PATHS.
The latter specifies the comma-separated paths to the source which should be instrumented for coverage. The former is a global flag to enable or disable coverage instrumentation. As an example, let s propose an example for measuring coverage on the NFC implementation. We can configure the build command as follows. Modifying Your Test for Host-Driven HIDL HAL Tests.
In most cases, no additional test configuration is needed to enable coverage. At last, add the following line to com. By default, coverage processing is enabled on the target if it is coverage instrumented as per the previous section and the test is a target-side binary. Host-driven tests have more flexibility for coverage measurement, as the host.
may request coverage files after each API call, at the end of a test case, or when. all test cases have completed. Measure coverage at the end of an API call. Coverage is available with the result of each API call. To add it to the dashboard. for display, call self. SetCoverageData with the contents of result. For example, in a test of the lights HAL, the following would gather coverage. after an API call to set the light.
Measure coverage at the end of a test case. After a test case has completed, coverage can be gathered independently of an. Coverage can be requested from the device under test dut with the. method GetRawCodeCoverage. For example, at the end of a host-side NFC test case, coverage data is fetched using the call. Measure coverage by pulling all coverage files from the device.
For coarse coverage measurement e. after running all of the testscoverage. can be requested by pulling any coverage-related output files from the device. manually over ADB. The base test class provides a coverage feature to fetch. and process the files. Configuring the Test for Coverage optional. The VTS framework automatically derives the information it needs to process the coverage data emitted from the device after test execution and to query Gerrit for the relevant source code.
For instance, the following two paths would both be identified as the project platform test vts. On the other hand, a project with the path android platform test vts would not be automatically matched with the project by name platform test vts. However, it relies on the assumption that there is a symmetry between git project names and the full Android source tree; specifically, the project name and the path to the project from the Android root may differ by at most one relative node in order for the VTS framework to identify the source code.
In cases when the project name differs significantly from the project s path from the Android root, a manual configuration must be specified in the test configuration JSON file. the module name in the make file for the binary or shared libraryas well as git project name and path for the source code included in the module. We must specify a list of dictionaries, each containing the module name i. For example, add the following to the configuration JSON file.
For the lights HAL, the test configuration file would look like. Running VTS. At test runtime, coverage will automatically be collected and processed by the VTS framework with no additional effort required. The processed coverage data will be uploaded to the VTS Dashboard along with the test results so that the source code can be visualized with a line-level coverage overlay. Note that two external dependencies are necessary to support coverage.
A Gerrit server with REST API must be available and configured to integrate with the VTS Dashboard. See the Dashboard setup for integration directions. A build artifact server with REST API must be configured to integrate with the VTS runner. This will allow the runner to fetch both a build-time coverage artifact from the building step above as well as the source version information for each git project within the repository at build time.
The VTS framework expects an a JSON file named BUILD_INFO which contains a dictionary of source project names to git revisions under the key repo-dict. After completing the build step and the test execution step, the VTS Dashboard should display a test result with a link in the row labeled Coverage. This will display a user interface similar to the one below. Lines executed are highlighted in green, while lines not exercised by the test are highlighted in red.
White lines are not executable lines of code, such as comments and structural components of the coding language. Offline Coverage. If the user would like to measure coverage without integrating with the VTS Dashboard or a build server, offline coverage measurement is also possible. First, we build a local device image. Next, we flash the device with the coverage-instrumented device image and run a VTS test. Note that you must manually force the HALs into same-process mode in order for the framework to extract coverage data.
Finally, we can process the files by pulling the outputted GCDA files from the device using adb and matching them with the source code and GCNO files produced at build time. The file structure is symmetric in the Android source code, out directory, and data partition of the device. Source files located in the Android source tree. cpp GCNO files located in the appropriate out intermediates directory out target product.
gcno GCDA files located on the device after test execution data misc gcov proc self cwd. These three can be combined into a tool such as gcov or lcov to produce a local coverage report. Background and FAQs. To measure coverage, the source file is divided into units called basic. blocks, which may contain one or more lines of code. All code in the same basic. block are accounted for together. Some lines of code i. declarations are not executable and thus belong to no basic block.
of code actually compile to several executable instructions i. conditional operators and belong to more than one basic block. The generated coverage report displays a color-coded source file with numerical. annotations on the left margin. The row fill indicates whether or not a line of. code was executed when the tests were run green means it was covered, red means. The corresponding numbers on the left margin indicate the number of.
times the line was executed. Lines of code that are not colored and have no execution count in the margin are. not executable instructions. Why do some lines have no coverage information. The line of code is not an executable instruction. For example, comments and structural coding language elements do not reflect instructions to the processor. Why are some lines called more than expected.
Since some lines of code may belong to more than one basic block, they may. appear to have been executed more than expected. For example, a line of code with an inline conditional statement may cause the line to belong to two basic blocks. Even if a line of code belongs to only one basic block, it may be display as having been executed more than it actually was.
This may occur if one or more lines of code in the same basic block were executed, causing the execution count of the whole basic block to increase. What does HIDL HAL Interface Fuzzer do. HIDL HAL Interface Fuzzer inteface fuzzer is a fuzzer binary built using LLVM asan, sancov, and libFuzzer. It runs against a user-specified target HIDL HALs.
It calls HAL functions in random order with random inputs until a terminating condition, e. HAL crash, sanitizer violation, timeout. More information about asan, sancov, and libFuzzer. All the code for HIDL HAL interface fuzzer is already carried by android-vts. In other words, no additional test code needs to be written or compiled. Only configuration is needed to run the interface fuzzer against a targeted HAL.
As usual, you need an Android. mk and an AndroidTest. xml to deploy the fuzz test as part of VTS. Assume your test is named VtsHalBluetoothV1_0IfaceFuzzer. Then AndroidTest. xml should look something like this. This should looks fairly standard. The only things to pay attention to are these three lines. This option specifies what files need to be pushed onto the device. Contents of IfaceFuzzerTest. This option specifies bluetooth HAL as our fuzz target. This option specifies the host code used to deploy the fuzzer binary.
To run the fuzzer you need to compile VTS with appropriate asan and sancov build options. From android source root directory do. This will run VtsHalBluetoothV1_0IfaceFuzzer test, print logs to screen, and return back to shell. You will have to rely on logs to identify fuzzer failure. If the fuzzer encounters an error e.
segfault, buffer overflow, etcyou will see something like this in your log. This means that the fuzzer was able to trigger a segfault somewhere in bluetooth HAL implementation. Unfortunately, we don t have a way to symbolize this stack trace yet. However, the log will contain the last call sequence batch that triggered the failure. Let s assume you have got trace files for your test e. by running the tests with profiling enabled, see instructions about HAL API call latency profiling.
The trace files should be stored under test vts-tescase hal-trace. where is the name of your HAL and is the version of your HAL with format V _. We will use Vibrator Hal as a running example throughout this section, so the traces are stored under test vts-tescase hal-trace vibrator V1_0. Create an HIDL HAL replay test. Follow the same instruction in Codelab for Host-Driven Tests to create a host-side VTS test with name VtsHalVibratorV1_0TargetReplay.
Add the following line to the corresponding AndroidTest. xml under the test configuration to use the VTS replay test template. xml under the test configuration to add the trace file for replay. Note, if you want or replay multiple traces within the test, add each trace file using the above configuration. xml under the test configuration to indicate the HIDL HAL package name for the test. xml for a replay test looks like follows. Schedule the replay test.
At that time, you should be able to add VtsHalVibratorV1_0TargetReplay to your favorite list. Visualizing Coverage. It compares the libraries with pre-generated dump files which include symbols and virtual function tables. Generate ABI dump. VtsVndkAbiTest ensures that the ABI of VNDK libraries is compatible with generic system image. Select a generic lunch target and build Compile vndk-vtable-dumper Create a text file which consists of library names.
One library in each line. In this example, the file name is vndk_list_26. txt Run dump_abi. py to generate dump files in the directory. In addition to the text file, the script accepts library names as arguments. By default, the script detects the target CPU architecture and search system lib 64 for the libraries. Environment variables ANDROID_BUILD_TOP ANDROID_PRODUCT_OUT TARGET_PRODUCT Command-line options -d DUMPER_DIR The directory containing bin vndk-vtable-dumper.
-i IMPORT_PATH The root directory of VTS Python modules, vts. In order to configure the VTS Dashboard and notification service, several setup, configuration, and integration steps must be executed. Most parts of the VTS Dashboard are self-contained, but others are not and so they will depend on your own tool configurations. Begin by completing the first two sections first and then proceed to the third section which guides integration with existing services; note that the last step is only needed if the VTS Dashboard will be used to display coverage from test execution time and requires strong domain knowledge of existing internal web services.
To run the script outside Android source, the following environment variables and command-line options must be specified. The code for VTS Dashboard is located under test vts web dashboard in Android O but will be migrated to test vti dashboard moving forward. Configure a Google App Engine project and Deploy the VTS Dashboard. This section should only be done once, when the project is deployed to the cloud.
We will refer to DASHBOARD_TOP as one of these two locations depending on the Android version. If changes are made to the code under DASHBOARD_TOPthen the VTS Dashboard need to be re-deployed step 5 ; otherwise this is a one-time setup. Note that there most likely will only be one VTS Dashboard instance in Google Cloud for an entire company, so one person or group should be selected to own the web project. Create a Google App Engine Project. Decide how many Google Compute Engine machines you d like in your cluster to balance your cost and performance constraints.
Configure the App Engine project on Google Cloud Console. Add an authorized email sender address under App Engine Settings Application Settings Email API authorized senders. This will be the email address used to send emails to users about test failures iq option 5 minutes strategy Create an OAuth 2. 0 client ID under IAM Admin API Credentials Create a service account and key file under IAM Admin Service accounts.
Prepare the Deployment Host. Install some dependencies needed on the host machine that will be deploying the project to the cloud. Install Java 8 Install Google Cloud SDK Run the setup instructions to initialize gcloud and log in to the project you created in step 1. Install Maven. For more information about setting up the host and using Maven, refer to the App Engine documentation. Fill out the project configuration file DASHBOARD_TOP pom.
Specify Project Configurations. xml with parameters from the previous steps. clientID -- oauth 2. 0 client ID for App Engine project identifies the App Engine project as an OAuth 2. 0 client, from step 2. 2 appengine. serviceClientID -- вЂ client_id from service JSON file from step 2. 3 appengine. senderEmail -- email address from which to send alerts from step 2.
1 appengine. emailDomain -- email address domain to which emails will be sent e. com to limit emails to gmail accounts only. Deploy the Project. To test the project locally using the App Engine development server, run the command mvn clean appengine devserver from DASHBOARD_TOP. To deploy to the cloud, run the command mvn clean appengine update from DASHBOARD_TOP.
For additional documentation regarding Google Cloud App Engine setup, refer to the documentation for Java applications. Configure the VTS Runner to Upload Results to the VTS Dashboard Service. After completing the first section, the web service is up and running. Next, the VTS test runner must be configured to upload the data to the correct place. The changes iq option 5 minutes strategy be made locally for a per-machine configuration or can be checked it into the source tree so that everyone running VTS can post data to the Dashboard.
The following changes must be made to the file test vts tools vts-tradefed res default DefaultTestCase. Add a value for key service_key_json_path. This is where the key file from step 2. It should be located in some network drive or a directory on the local machine. Note that these changes are important for any machine running VTS that should report to the web, unlike the following section which only was relevant to the admin of the web service.
VTS needs to read the file when running tests in order to post data to the VTS Dashboard. Add a value for key dashboard_post_command. This should be a command-line for Windows Linux whichever platform is running VTS that will post data to the dashboard. This may be a special command that is needed to pass through a proxy, or it may be a fairly standard command built-in command. The only constraint is that the string must be located somewhere in the command so that the VTS framework can insert the path to the serialized data tempfile into the command.
Below is an example for posting to a website from a Linux host where is the fully specified URL of the VTS Dashboard instance. Results from continuous runs i. Local runs with custom test and or device builds may also be visible for debugging purposes if tests are run on a machine with access to the service key file. To view local runs on the dashboard for debugging purposes, add the following to the end of the URL from the table summary page unfiltered.
This will show all results uploaded to the VTS Dashboard without performing any filtering of the build IDs. Integration For Display of Coverage Data optional. This section is important if the VTS Dashboard will be used to display coverage data from test runs; the Dashboard service does not store source code or artifacts from device builds, so VTS must be configured to integrate with existing services. Completing this process will require knowledge of the other tools used within the company, such as proxies, firewalls, build server APIs, etc.
Fortunately the Gerrit REST API is standard, so minimal configuration is needed to integrate that with VTS Dashboard. On the other hand, the continuous build system is non-standardized so integrating will require domain knowledge. The steps below must be executed by the owner of the VTS Dashboard web service. with integer test and device build IDs run on a machine with access to the service JSON are automatically visible on the dashboard.
Fill out the additional fields in DASHBOARD_TOP pom. xml and repeat step 5 from the first section. uri -- base URL to Gerrit REST API which contains the source compiled to a device image gerrit. scope -- set to the Gerrit Oauth 2. Configure CORS. Configure the Gerrit server to allow CORS cross-origin resource sharing with the VTS Dashboard web service. In order to overlay coverage data on top of the source code, the VTS Dashboard needs to query Gerrit for the source code; the proxy or firewall may block requests from other services unless they are added to a whitelist.
In order to enable this, the Gerrit administrator will likely need the address of VTS Dashboard as configured in step 1 and an OAuth 2. 0 client ID from step 2. If there aren t any limitations on CORS or if the services are hosted on the same domain, then no changes will be needed to allow communication between the services. Configure the VTS Runner.
Now that the web services are configured, the VTS runner must be configured on every machine running VTS to access the continuous build server. The following additional keys must be provided with values in test vts tools vts-tradefed res default DefaultTestCase. build_server_scope -- OAuth 2. 0 scope of the REST build server build_server_api_name -- Name of the build server REST API build_server_api_version -- Version of the build server REST API.
The VTS runner will now query the build server on every test run to retrieve two build artifacts. zip -- the ZIP file produced automatically in the out dist directory when building a coverage-instrumented device image BUILD_INFO -- a JSON file describing the device build configuration. At minimum, the file must contain a dictionary of project names to commit identifiers under the key repo-dict. Monitoring optional. Google Stackdriver. The Google App Engine project can be configured with Stackdriver to verify the health of the web service.
Create a Simple Uptime Check. Go to Stackdriver Monitoring console Go to Alerting Uptime Checks in the top menu and then click Add Uptime Check. The New Uptime Check panel will be displayed. Click Test to verify the uptime check is working. Fill out the configuration for notifications and click save policy. Verify Checks and Notifications. To test the check and alert, go to the VM Instances page in Google Compute Engine, select an instance, and click Stop from the top menu.
Wait up to five minutes for the next uptime check to fail. An email notification should be sent as configured in the previous steps notifying the administrator of a service outage. To correct the outage, return to the VM Instances page, select the stopped instance, and click Start from the top menu. The VTS Dashboard supports integration with Google Analytics so that user behavior can be monitored and analyzed by a web administrator.
Provide the tracking ID in the tag analytics. Once the project is re-deployed to Google App Engine, Analytics will begin to track access and usage patterns. Refer to the Stackdriver documentation for more details on setting up a monitoring project. The original plan is to host different patches for android x86 support from open source community. This is a project to port Android open source project to x86 platform, formerly known as patch hosting for android x86 support.
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