Libraries tagged by test case

Extension for reviewing passed tests, with the display of actual and correct answers.

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A simple way to test how many files are into a given dir based on extension

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An extension for TAO to create test booklets (publishable in MS-Word and PDF along with Answer Sheets)

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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Convert laravel view to arabic html (pdf) by using ArPHP and dompdf with arabic fonts and text. Forked from abalselwi/laravel-arabic-html to Support Laravel 11 and 12.

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CryptoManana is a cryptography framework for boosting your project's security.

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This library provides an API to perform binary search operations on a sorted index. The index can be a XML document, a CSV document, or an arbitrary text file where the key has a fixed position. You can easily implement your own index. This API comes handy on any sorted data structure where realtime search operations are necessary without the detour of a DBS import.

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Specific classes to integrate Codeception-based test framework with Report Portal (http://reportportal.io/).

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Magento-based functionality for the Magium test library

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Production environment is located at `https://api.moje.uctoplus.sk/production/`. Sandbox environment is located at `https://api.moje.uctoplus.sk/sandbox/`. All communication with API is encoded in UTF-8. This REST API is based on Open API v3 standard. For help with implementation or access to the test environment please contact our helpdesk

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Base Test for your Laravel feature tests.

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Generates tests for use with PHPunit based on an existing composer.json

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Расширение возможностей платформы 1С-Битрикс. Минимальная версия 16.5.0, рекомендуется использовать с версии 17.5.2

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A set of tools for testing your REST calls based on the Swagger or OpenAPI documentation using PHPUnit

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Laravel ATU Base on annotation's and laravel phpunit.一个基于注解和laravel单元测试的 自动化测试包

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