Bengaluru-based Mukunda Foods was founded in 2013 by college students Sudeep Sabat and Eshwar K Vikas as a Quick Service Restaurant (QSR) the same year to sell South-Indian food. https://analyticsindiamag.com/tech-behind-kitchen-automation-startup-mukunda-foods/
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The COVID pandemic has massively escalated the surge of cyberattacks and data breaches despite having robust security controls, software, and solutions abundantly available in the market. A lot of this could be attributed to the vulnerability businesses offer the cybercriminals to take advantage of the situation quickly. While the conventional cybersecurity approach has benefited many, having cybersecurity without cyber-intelligence and necessary awareness can put the security professionals off-guarded to more complicated and novel threats.
Furthermore, with limited cybersecurity resources, businesses need to prioritise their efforts to strengthen cyber posture effectively; however, many organisations do not have an anchor point or a guiding principle, to begin with. With cyber-intelligence inputs missing from cybersecurity capabilities like incident management, vulnerability management, risk assessment and brand monitoring, businesses end up running their security practice in silos instead of an integrated approach.
And, thus, in an attempt to revolutionise the cyber threat visibility and intelligence market, CYFIRMA, a cyber analytics startup assists businesses to understand the relevance of the current threat landscape. Not only it provides insights on threat actors and indicators, emerging threats and digital risks, but also automatically applies intelligence into cyber posture management. To dig deeper, Analytics India Magazine got in touch with the chairman and CEO of the company, Kumar Ritesh, to understand how the company uses a predictive intelligence-driven approach to discover cyber threats.
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Python is a versatile language that can be used for various automation tasks. You can use Python for automating file or folder management, generating reports from data stored in a database, monitoring logs on your servers, creating website scrapers, among other things.
If you’re looking for ways to automate tasks with Python, be sure to read the article above for some ideas.
Thorough testing is crucial to the success of a software product. If your software doesn’t work properly, chances are strong that most people won’t buy or use it…at least not for long. But testing to find defects or bugs is time-consuming, expensive, often repetitive, and subject to human error. Automated testing, in which Quality Assurance teams use software tools to run detailed, repetitive, and data-intensive tests automatically, helps teams improve software quality and make the most of their always-limited testing resources.
Use these top tips to ensure that your software testing is successful and you get the maximum return on investment (ROI):
It is impossible to automate all testing, so it is important to determine what test cases should be automated first.
The benefit of automated testing is linked to how many times a given test can be repeated. Tests that are only performed a few times are better left for manual testing. Good test cases for automation are ones that are run frequently and require large amounts of data to perform the same action.
You can get the most benefit out of your automated testing efforts by automating:
Success in test automation requires careful planning and design work. Start out by creating an automation plan. This allows you to identify the initial set of tests to automate and serve as a guide for future tests. First, you should define your goal for automated testing and determine which types of tests to automate. There are a few different types of testing, and each has its place in the testing process. For instance, unit testing is used to test a small part of the intended application. To test a certain piece of the application’s UI, you would use functional or GUI testing.
After determining your goal and which types of tests to automate, you should decide what actions your automated tests will perform. Don’t just create test steps that test various aspects of the application’s behavior at one time. Large, complex automated tests are difficult to edit and debug. It is best to divide your tests into several logical, smaller tests. It makes your test environment more coherent and manageable and allows you to share test code, test data, and processes. You will get more opportunities to update your automated tests just by adding small tests that address new functionality. Test the functionality of your application as you add it, rather than waiting until the whole feature is implemented.
When creating tests, try to keep them small and focused on one objective. For example, separate tests for read-only versus reading/write tests. This allows you to use these individual tests repeatedly without including them in every automated test.
Once you create several simple automated tests, you can group your tests into one, larger automated test. You can organize automated tests by the application’s functional area, major/minor division in the application, common functions, or a base set of test data. If an automated test refers to other tests, you may need to create a test tree, where you can run tests in a specific order.
To get the most out of your automated testing, testing should be started as early as possible and ran as often as needed. The earlier testers get involved in the life cycle of the project the better, and the more you test, the more bugs you find. Automated unit testing can be implemented on day one and then you can gradually build your automated test suite. Bugs detected early are a lot cheaper to fix than those discovered later in production or deployment.
With the shift left movement, developers and advanced testers are now empowered to build and run tests. Tools allow users to run functional UI tests for web and desktop applications from within their favorite IDEs. With support for Visual Studio and Java IDEs such as IntelliJ and Eclipse, developers never have to leave the comfort of their ecosystem to validate application quality meaning teams can quickly and easily shift left to deliver software faster.
Selecting an automated testing tool is essential for test automation. There are a lot of automated testing tools on the market, and it is important to choose the automated testing tool that best suits your overall requirements.
Consider these key points when selecting an automated testing tool:
For detailed information about selecting automated testing tools for automated testing, see Selecting Automated Testing Tools.
Usually, the creation of different tests is based on QA engineers’ skill levels. It is important to identify the level of experience and skills for each of your team members and divide your automated testing efforts accordingly. For instance, writing automated test scripts requires expert knowledge of scripting languages. Thus, in order to perform these tasks, you should have QA engineers that know the script language provided by the automated testing tool.
Some team members may not be versed in writing automated test scripts. These QA engineers may be better at writing test cases. It is better when an automated testing tool has a way to create automated tests that do not require an in-depth knowledge of scripting languages.
You should also collaborate on your automated testing project with other QA engineers in your department. Testing performed by a team is more effective for finding defects and the right automated testing tool allows you to share your projects with several testers.
Good test data is extremely useful for data-driven testing. The data that should be entered into input fields during an automated test is usually stored in an external file. This data might be read from a database or any other data source like text or XML files, Excel sheets, and database tables. A good automated testing tool actually understands the contents of the data files and iterates over the contents in the automated test. Using external data makes your automated tests reusable and easier to maintain. To add different testing scenarios, the data files can be easily extended with new data without needing to edit the actual automated test.
Typically, you create test data manually and then save it to the desired data storage. However, you will find tools that provide you with the Data Generator that assists you in creating Table variables and Excel files that store test data. This approach lets you generate data of the desired type (integer numbers, strings, boolean values, and so on) and automatically save this data to the specified variable or file. Using this feature, you decrease the time spent on preparing test data for data-driven tests.
Creating test data for your automated tests is boring, but you should invest time and effort into creating data that is well structured. With good test data available, writing automated tests becomes a lot easier. The earlier you create good-quality data, the easier it is to extend existing automated tests along with the application’s development.
Automated tests created with scripts or keyword tests are dependent on the application under test. The user interface of the application may change between builds, especially in the early stages. These changes may affect the test results, or your automated tests may no longer work with future versions of the application. The problem is automated testing tools use a series of properties to identify and locate an object. Sometimes a testing tool relies on location coordinates to find the object. For instance, if the control caption or its location has changed, the automated test will no longer be able to find the object when it runs and will fail. To run the automated test successfully, you may need to replace old names with new ones in the entire project, before running the test against the new version of the application. However, if you provide unique names for your controls, it makes your automated tests resistant to these UI changes and ensures that your automated tests work without having to make changes to the text itself. This also eliminates the automated testing tool from relying on location coordinates to find the control, which is less stable and breaks easily.
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According to Research and Markets reports, Artificial Intelligence for speech recognition market in India is anticipated to expand at a compound annual growth rate (CAGR) of ~65.17% during the forecast period (2019-2024) and is expected to reach a value of INR 14.61 Bn by 2024.
The increasing demand for smart speakers and voice-enabled devices, coupled with rising penetration of speech recognition technology in customer care services are driving this growth, further stimulating development and innovation in the space.
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