Have you ever had fuss testing the audio functionality of your mobile covering? Capturing, injecting, and comparing audio has historically be a sly area in mobile automation testing. Even essay to validate something as simple as checking if the correct audio play during a prescribed action can be a daunting labor. HeadSpin ’ s Audio APIs simplify these operations and supply developer with a convenient RESTful interface. This post walks through a high-level overview of each API as good as showcases how to mix it into your script.

What are these APIs? How do I use them?

In full, HeadSpin has seven different APIs regarding sound on mobile devices. This post will primarily focus on three real popular endpoints, which include uploading sound to our server, capturing sound on a yield roving twist in the, and comparing two audio file to see if they are a match.

Uploading

The primary use of this endpoint is to permit the developer to upload a citation file that is expend afterward to compare against a trial sound file captured from the mobile device during your automation test. One thing to observe is that the audio format for this endpoint is .wav. The reaction from this endpoint is a JSON object notifying if the upload was successful, and if it were, it would provide you with a unparalleled audio id.

Example Code

defupload_reference_file (api_token):
api_endpoint = & # x27; https: //api-dev.headspin.io/v0/audio/upload & # x27;
data = open (& # x27; reference_audio.wav & # x27;, & # x27; rb & # x27;)
r = requests.post (api_endpoint,
headers= {& # x27; Authorization & # x27;: & # x27; Bearer {} & # x27; .format (api_token)},
data=data)
answer = json.loads (r.text)
   returnanswer [& # x27; audio_id & # x27;]

Capturing

You conjure this endpoint when you want to start capturing the sound output from a given device, and the API also allows you to end the session in a twosome of different ways. When you make the initial capture call to the server, you can specify how long you would like it to run, or you can store the response, which includes a proletarian id and use it to poll for status updates and kibosh the capture as well.

After it has stop from the device, it is uploaded to a storage system that allows anyone in your organization access it. In the answer from the capture termination, the sound id is passed back, which is essential to store for reference later.

Pro tip: Tools like SUSA can handle this autonomously — upload your app and get results without writing a single test script.

Example Code

defcapture_audio (device_address, duration, api_token):
api_endpoint = & # x27; https: //api-dev.headspin.io/v0/audio/capture/start & # x27;
   data = {}
data [& # x27; device_address & # x27;] = device_address
data [& # x27; max_duration & # x27;] = continuance
data = json.dumps (data)
r = requests.post (api_endpoint,
headers= {& # x27; Authorization & # x27;: & # x27; Bearer {} & # x27; .format (api_token)},
data=data)
reply = json.loads (r.text)
   returnanswer [& # x27; audio_id & # x27;]

Analysis

The analysis or lucifer API allows you to legislate in two audio files, a reference, and a test, and so influence if the mention audio is present in the test sound. We delineate the reference file to be the original sound source while the test audio is a more extended captured audio that contains the reference.

The use case here is to find exact audio lucifer as well as locate the citation audio inside of the test audio. Additionally, it can also equate the audio quality of the test congenator to the reference.

The answer from this API includes multiple result parameters from the analysis. The key takeaways for me in this reaction is if it was a success first and foremost. Success does not designate if the sound match but instead signal that the algorithm was able to run aright. Along with success, there are two objects in the form of parameters and results inside our reply. Parameters described the threshold for values used during the analysis, e.g. the sample rate. The most significant behind a successful analysis is, of course, the results. The issue object give us the undermentioned stats:

• Match: Full, Partial, No, or Error
• Quality of Match
• Start and End clip of reference in damage of test

Example Code

defcompare_audio (test_id, reference_id, api_token):
api_endpoint = & # x27; https: //api-dev.headspin.io/v0/audio/analysis/match & # x27;
   data = {}
data [& # x27; test_audio_id & # x27;] = test_id
data [& # x27; ref_audio_id & # x27;] = reference_id
datum = json.dumps (data)
r = requests.post (api_endpoint,
headers= {& # x27; Authorization & # x27;: & # x27; Bearer {} & # x27; .format (api_token)},
data=data)
response = json.loads (r.text)
   ifreply [& # x27; success & # x27;] ==True:
Return response [& # x27; solution & # x27;]

How can we tie all of these together?

To showcase how we can incorporate all of these endpoint into a single mechanisation run, we are going to look at a client use case. The goal of this client was to verify that the correct automatize response was give when a user of their service get a call with no balance on their sim card.

Given the proper reference audio, this is made relatively straightforward with the use of HeadSpin & # x27; s audio APIs. We executed this test in the following steps:

1. Upload a credit file to HeadSpin ’ s storage scheme using the upload endpoint and store the audio id for ulterior use.
2. Launch the Android/iOS device in HeadSpin & # x27; s roving device cloud, which has audio enable.
3. Navigate to the device ’ s earpiece application employ the Appium framework.
4. Enter a given number and lay the call using a sim card provided by the client.
5. Once the call connects, hit HeadSpin & # x27; s capture endpoint with a max duration of 20 seconds to enter the automated response and fund the audio id in the response for late use.
6. Send both the citation and test audio ids to HeadSpin & # x27; s analysis API to verify they are a match.

FAQs

1. What is the indispensable demand for writing Appium trial?

The all-important prerequisites for creating Appium tests are:

• Driver Command - Appium support writing tryout by providing a set of bidding.
• Appium Session - As Appium test are executed in sessions, it is essential to initialise an Appium session before performing tests.
• Desired Capabilities: This refers to the parameters defining the test automation need from the Appium server. The version of the program, gimmick, and network environment are among the desired capabilities.
• Driver Client Library- Client library aids in creating Appium test and wraps the procedures required to send them to the Appium server via HTTP.

2. What is XPATH, and how is it used to find constituent?

Generally, XPATH enables testers to browse the XML structure of any papers, including XML and HTML file. XPATH gives a unique syntax for locate any element. It is a String-based elements identifier. Appium has a & # x27; FindBy & # x27; map where the XPATH string can be passed, and the elements are returned.

3. What are Implicit and Explicit waits in Appium?

There are second when certain elements do not charge on the app screen during the testing phase. These circumstances necessitate a hold or hold for a specific test on the element in question. In Appium, implicit hold refers to a holdup for a particular length. On the other hand, expressed hold refers to the delay added in response to a specific precondition.

4. What type of file is require for test automation in Android habituate Appium?

.apk file are needed for using Appium.

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