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The Source Analyst library is a powerful tool designed to streamline and expedite the tracking of traffic sources for mobile applications. This versatile library is aptly named "Source Analyst" and is an invaluable asset for app developers and marketers seeking to gain deeper insights into the performance of their advertising campaigns. With just one simple function call, Source Analyst empowers you to efficiently investigate the effectiveness of various advertising sources. Key Features: Effortless Tracking: Source Analyst simplifies the complex task of tracking the origins of traffic for your mobile app. No need for convoluted setups or extensive coding – one function is all it takes. Comprehensive Insights: Gain a comprehensive understanding of where your app's users are coming from. Whether it's through social media, search engines, referral links, or other channels, Source Analyst provides you with clear data on traffic sources. Performance Evaluation: Evaluate the performance of your advertising campaigns with precision. Discover which sources are driving the most valuable users to your app, helping you optimize your marketing efforts effectively. Time-Saving: Say goodbye to hours spent on manual data collection and analysis. Source Analyst automates the tracking process, freeing up your time to focus on making data-driven decisions. Customization: Tailor Source Analyst to your specific needs. Customize the library to track the metrics that matter most to your app's success. Real-time Data: Access real-time data, ensuring that you always have up-to-date insights into your traffic sources. Integration-Friendly: Seamlessly integrate Source Analyst into your existing mobile app infrastructure, whether you're developing for Android or iOS. User-Friendly: Source Analyst is designed with user-friendliness in mind. Its intuitive interface and straightforward documentation make it accessible to developers of all levels of expertise. How It Works: Using Source Analyst is as easy as calling a single function within your code. Simply integrate the library into your app, and you can begin tracking traffic sources immediately. From there, Source Analyst compiles and presents the data in a clear and organized manner, allowing you to make data-driven decisions with ease. In a world where understanding the origins of your app's traffic is essential for marketing success, Source Analyst is the go-to solution. Say goodbye to the complexity of tracking sources and embrace the simplicity and effectiveness of Source Analyst for your mobile app. Harness the power of Source Analyst and unlock a new level of insight into your app's performance today!

InfoScope Library: Simplifying Privacy Policy Display with WebView The InfoScope Library is a versatile tool designed to enhance the seamless presentation of privacy policies through WebView integration. Privacy policies play a crucial role in maintaining transparency and trust between users and applications, and the InfoScope Library streamlines this process by offering a range of convenient features. At its core, the library provides the SimpleAutoWebView, a WebView component equipped with fundamental settings for optimal privacy policy display. This WebView component is tailored to effortlessly load and present privacy policy content to users, ensuring a smooth and user-friendly experience. To further enhance the functionality and customization options, the InfoScope Library includes two essential components: SimpleAutoWebViewClient and SimpleAutoWebChromeClient. These components enable developers to quickly establish and configure the basic WebView behavior and appearance. The SimpleAutoWebViewClient is designed to facilitate the interaction between the WebView and the application. It streamlines the process of handling various events, such as page loading, error handling, and navigation. With this component, developers can swiftly create a WebViewClient that aligns with their application's requirements, promoting a consistent and intuitive user journey. Complementing the WebView functionality, the SimpleAutoWebChromeClient focuses on managing the visual aspects of WebView content, including alert dialogs, JavaScript dialogs, and UI interactions. This component empowers developers to define the behavior and appearance of these elements, ensuring a polished and integrated presentation of the privacy policy content. In summary, the InfoScope Library offers a comprehensive toolkit for developers to seamlessly integrate privacy policy display using WebView. By providing the SimpleAutoWebView, SimpleAutoWebViewClient, and SimpleAutoWebChromeClient components, the library enables swift development and easy customization, fostering transparency and trust between users and applications. Embrace the power of the InfoScope Library to elevate your privacy policy presentation and enhance your user experience.

Introducing "PerScope" Library: Simplifying Privacy Policy Event Handling for Android Apps "PerScope" is a cutting-edge library designed to streamline the processing of privacy policy events within regions where compliance with local legislation is crucial. Specifically crafted for Android applications, this library addresses the intricate task of managing privacy policy-related events while adhering to the legal requirements of the country in which the app is deployed. In today's digital landscape, ensuring user privacy and data protection is of paramount importance. Different countries have varying legal frameworks dictating how user data should be handled, necessitating robust mechanisms to accommodate these differences seamlessly. This is where the "PerScope" library shines. The key feature that sets "PerScope" apart is its incredible simplicity. With just a single function call, developers can integrate the library into their Android applications and gain immediate access to a comprehensive suite of tools for managing privacy policy events. Whether it's presenting privacy-related notifications, tracking user consents, or adapting the app's behavior based on regional requirements, "PerScope" handles it all efficiently and effectively. Here's a glimpse of what "PerScope" brings to the table: Localized Compliance: "PerScope" empowers developers to align their apps with the privacy laws of each region. By intelligently detecting the user's location, the library ensures that the app's behavior remains compliant with the specific privacy regulations of that area. Event Handling Made Easy: Instead of grappling with complex event management code, developers can integrate the "PerScope" function, drastically reducing development time and effort. The library takes care of the intricate event handling process seamlessly. Dynamic Adaptation: With the ability to dynamically adapt the app's features based on the user's consent and the local legal requirements, "PerScope" ensures a personalized and compliant user experience. Notification Presentation: "PerScope" assists in presenting privacy-related notifications to users, making it easier to inform them about data collection practices and obtain necessary consents. Smooth Integration: The library is designed to be easily integrated into existing Android applications, minimizing disruptions to the development process. In a nutshell, "PerScope" is a developer's go-to solution for managing privacy policy events within Android apps. Its single-function approach, combined with its capacity to handle a complex and critical aspect of app development, makes it an indispensable tool for app creators aiming to provide a user-centric, privacy-respecting experience while complying with regional legislation. Stay on the right side of the law and prioritize user privacy with the power of "PerScope."

Pact consumer ============= Pact Consumer is used by projects that are consumers of an API. Most projects will want to use pact-consumer via one of the test framework specific projects. If your favourite framework is not implemented, this module should give you all the hooks you need. Provides a DSL for use with Java to build consumer pacts. ## Dependency The library is available on maven central using: * group-id = `au.com.dius` * artifact-id = `pact-jvm-consumer_2.11` ## DSL Usage Example in a JUnit test: ```java import au.com.dius.pact.model.MockProviderConfig; import au.com.dius.pact.model.PactFragment; import org.junit.Test; import java.io.IOException; import java.util.HashMap; import java.util.Map; import static org.junit.Assert.assertEquals; public class PactTest { @Test public void testPact() { PactFragment pactFragment = ConsumerPactBuilder .consumer("Some Consumer") .hasPactWith("Some Provider") .uponReceiving("a request to say Hello") .path("/hello") .method("POST") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") .toFragment(); MockProviderConfig config = MockProviderConfig.createDefault(); VerificationResult result = pactFragment.runConsumer(config, new TestRun() { @Override public void run(MockProviderConfig config) { Map expectedResponse = new HashMap(); expectedResponse.put("hello", "harry"); try { assertEquals(new ProviderClient(config.url()).hello("{\"name\": \"harry\"}"), expectedResponse); } catch (IOException e) {} } }); if (result instanceof PactError) { throw new RuntimeException(((PactError)result).error()); } assertEquals(ConsumerPactTest.PACT_VERIFIED, result); } } ``` The DSL has the following pattern: ```java .consumer("Some Consumer") .hasPactWith("Some Provider") .given("a certain state on the provider") .uponReceiving("a request for something") .path("/hello") .method("POST") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") .uponReceiving("another request for something") .path("/hello") .method("POST") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") . . . .toFragment() ``` You can define as many interactions as required. Each interaction starts with `uponReceiving` followed by `willRespondWith`. The test state setup with `given` is a mechanism to describe what the state of the provider should be in before the provider is verified. It is only recorded in the consumer tests and used by the provider verification tasks. ### Building JSON bodies with PactDslJsonBody DSL The body method of the ConsumerPactBuilder can accept a PactDslJsonBody, which can construct a JSON body as well as define regex and type matchers. For example: ```java PactDslJsonBody body = new PactDslJsonBody() .stringType("name") .booleanType("happy") .hexValue("hexCode") .id() .ipAddress("localAddress") .numberValue("age", 100) .timestamp(); ``` #### DSL Matching methods The following matching methods are provided with the DSL. In most cases, they take an optional value parameter which will be used to generate example values (i.e. when returning a mock response). If no example value is given, a random one will be generated. | method | description | |--------|-------------| | string, stringValue | Match a string value (using string equality) | | number, numberValue | Match a number value (using Number.equals)\* | | booleanValue | Match a boolean value (using equality) | | stringType | Will match all Strings | | numberType | Will match all numbers\* | | integerType | Will match all numbers that are integers (both ints and longs)\* | | decimalType | Will match all real numbers (floating point and decimal)\* | | booleanType | Will match all boolean values (true and false) | | stringMatcher | Will match strings using the provided regular expression | | timestamp | Will match string containing timestamps. If a timestamp format is not given, will match an ISO timestamp format | | date | Will match string containing dates. If a date format is not given, will match an ISO date format | | time | Will match string containing times. If a time format is not given, will match an ISO time format | | ipAddress | Will match string containing IP4 formatted address. | | id | Will match all numbers by type | | hexValue | Will match all hexadecimal encoded strings | | uuid | Will match strings containing UUIDs | _\* Note:_ JSON only supports double precision floating point values. Depending on the language implementation, they may parsed as integer, floating point or decimal numbers. #### Ensuring all items in a list match an example (2.2.0+) Lots of the time you might not know the number of items that will be in a list, but you want to ensure that the list has a minimum or maximum size and that each item in the list matches a given example. You can do this with the `arrayLike`, `minArrayLike` and `maxArrayLike` functions. | function | description | |----------|-------------| | `eachLike` | Ensure that each item in the list matches the provided example | | `maxArrayLike` | Ensure that each item in the list matches the provided example and the list is no bigger than the provided max | | `minArrayLike` | Ensure that each item in the list matches the provided example and the list is no smaller than the provided min | For example: ```java DslPart body = new PactDslJsonBody() .minArrayLike("users") .id() .stringType("name") .closeObject() .closeArray(); ``` This will ensure that the users list is never empty and that each user has an identifier that is a number and a name that is a string. #### Matching JSON values at the root (Version 3.2.2/2.4.3+) For cases where you are expecting basic JSON values (strings, numbers, booleans and null) at the root level of the body and need to use matchers, you can use the `PactDslJsonRootValue` class. It has all the DSL matching methods for basic values that you can use. For example: ```java .consumer("Some Consumer") .hasPactWith("Some Provider") .uponReceiving("a request for a basic JSON value") .path("/hello") .willRespondWith() .status(200) .body(PactDslJsonRootValue.integerType()) ``` #### Root level arrays that match all items (version 2.2.11+) If the root of the body is an array, you can create PactDslJsonArray classes with the following methods: | function | description | |----------|-------------| | `arrayEachLike` | Ensure that each item in the list matches the provided example | | `arrayMinLike` | Ensure that each item in the list matches the provided example and the list is no bigger than the provided max | | `arrayMaxLike` | Ensure that each item in the list matches the provided example and the list is no smaller than the provided min | For example: ```java PactDslJsonArray.arrayEachLike() .date("clearedDate", "mm/dd/yyyy", date) .stringType("status", "STATUS") .decimalType("amount", 100.0) .closeObject() ``` This will then match a body like: ```json [ { "clearedDate" : "07/22/2015", "status" : "C", "amount" : 15.0 }, { "clearedDate" : "07/22/2015", "status" : "C", "amount" : 15.0 }, { "clearedDate" : "07/22/2015", "status" : "C", "amount" : 15.0 } ] ``` #### Matching arrays of arrays (version 3.2.12/2.4.14+) For the case where you have arrays of arrays (GeoJSON is an example), the following methods have been provided: | function | description | |----------|-------------| | `eachArrayLike` | Ensure that each item in the array is an array that matches the provided example | | `eachArrayWithMaxLike` | Ensure that each item in the array is an array that matches the provided example and the array is no bigger than the provided max | | `eachArrayWithMinLike` | Ensure that each item in the array is an array that matches the provided example and the array is no smaller than the provided min | For example (with GeoJSON structure): ```java new PactDslJsonBody() .stringType("type","FeatureCollection") .eachLike("features") .stringType("type","Feature") .object("geometry") .stringType("type","Point") .eachArrayLike("coordinates") // coordinates is an array of arrays .decimalType(-7.55717) .decimalType(49.766896) .closeArray() .closeArray() .closeObject() .object("properties") .stringType("prop0","value0") .closeObject() .closeObject() .closeArray() ``` This generated the following JSON: ```json { "features": [ { "geometry": { "coordinates": [[-7.55717, 49.766896]], "type": "Point" }, "type": "Feature", "properties": { "prop0": "value0" } } ], "type": "FeatureCollection" } ``` and will be able to match all coordinates regardless of the number of coordinates. #### Matching any key in a map (3.3.1/2.5.0+) The DSL has been extended for cases where the keys in a map are IDs. For an example of this, see [#313](https://github.com/DiUS/pact-jvm/issues/131). In this case you can use the `eachKeyLike` method, which takes an example key as a parameter. For example: ```java DslPart body = new PactDslJsonBody() .object("one") .eachKeyLike("001", PactDslJsonRootValue.id(12345L)) // key like an id mapped to a matcher .closeObject() .object("two") .eachKeyLike("001-A") // key like an id where the value is matched by the following example .stringType("description", "Some Description") .closeObject() .closeObject() .object("three") .eachKeyMappedToAnArrayLike("001") // key like an id mapped to an array where each item is matched by the following example .id("someId", 23456L) .closeObject() .closeArray() .closeObject(); ``` For an example, have a look at [WildcardKeysTest](src/test/java/au/com/dius/pact/consumer/WildcardKeysTest.java). **NOTE:** The `eachKeyLike` method adds a `*` to the matching path, so the matching definition will be applied to all keys of the map if there is not a more specific matcher defined for a particular key. Having more than one `eachKeyLike` condition applied to a map will result in only one being applied when the pact is verified (probably the last). ### Matching on paths (version 2.1.5+) You can use regular expressions to match incoming requests. The DSL has a `matchPath` method for this. You can provide a real path as a second value to use when generating requests, and if you leave it out it will generate a random one from the regular expression. For example: ```java .given("test state") .uponReceiving("a test interaction") .matchPath("/transaction/[0-9]+") // or .matchPath("/transaction/[0-9]+", "/transaction/1234567890") .method("POST") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") ``` ### Matching on headers (version 2.2.2+) You can use regular expressions to match request and response headers. The DSL has a `matchHeader` method for this. You can provide an example header value to use when generating requests and responses, and if you leave it out it will generate a random one from the regular expression. For example: ```java .given("test state") .uponReceiving("a test interaction") .path("/hello") .method("POST") .matchHeader("testreqheader", "test.*value") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") .matchHeader("Location", ".*/hello/[0-9]+", "/hello/1234") ``` ### Matching on query parameters (version 3.3.7+) You can use regular expressions to match request query parameters. The DSL has a `matchQuery` method for this. You can provide an example value to use when generating requests, and if you leave it out it will generate a random one from the regular expression. For example: ```java .given("test state") .uponReceiving("a test interaction") .path("/hello") .method("POST") .matchQuery("a", "\\d+", "100") .matchQuery("b", "[A-Z]", "X") .body("{\"name\": \"harry\"}") .willRespondWith() .status(200) .body("{\"hello\": \"harry\"}") ```