Hello I need serious help and i will give a like and good fe

Hello, I need serious help and i will give a like and good feedback in return.

My question is : Test data on artifical intellgience current research?

To break down my question, e,g,why do researchers need to do test data? what the industry if facing? why the lack of data is a issue? - if could summarise in a brief summary would be great!

I do not have any knowledge on this topic but i need to give a speech to a audience that have little knowledge of AI so i\'d hope they understand the info on test data.

Solution

First the Artificial Intelligence Means

Artificial Intelligence:-

Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence (AI) is defined as the ability of computer software and hardware to do those things that we, as humans, recognize as intelligent behavior. These include activities as: • Searching: finding “good” material after having been provided only limited direction, especially from a large quantity of available data. • Surmounting constraints: finding ways that something will fit into a confined space, taking apart or building a complex object, or moving through a difficult maze. • Recognizing patterns: finding items with similar characteristics, or identifying an entity when not all its characteristics are stated or available. • Making logical inferences: drawing conclusions based upon understood reasoning methods such as deduction and induction.

{ In software devolepment -Testing plays an important role .In every software devolapment we need to test the each and every data that software contain .}

What is Test Data :-

  Test data is the data that is used in tests of a software system.

In order to test a software application you need to enter some data for testing most of the features. Any such specifically identified data which is used in tests is known as test data.

Some test data is used to confirm the expected result, i.e. When test data is entered the expected result should come and some test data is used to verify the software behavior to invalid input data.

Test data is generated by testers or by automation tools which support testing. Most of the times in regression testing the test data is re-used, it is always a good practice to verify the test data before re-using it in any kind of test.

Why we use Test Data or the Advantages of test data :-

1.To Fast

As manual testing consumes a great deal of time in both the process of software development as well as during the software application testing, automated tools are a faster option as long as the scripts which need to be done are standard and non complex.

2.To Reliability

Automation of test script execution eliminates the possibility of human error when the same sequence of actions is repeated again and again. Remember this can be really important as you would be astonished to learn just how many test defects raised are in fact caused by tester error. This particularly happens when the same boring test scripts have to be run over and over again as well as when, at the opposite spectrum, really complex testing has to be done.

3. To Comprehensive

Automated testers might contain a suite of tests that would help in testing each and every feature in the application. This means that chance of missing out key parts of testing is unlikely to occur. You might think this is unlikely to happen in reality, but I have managed a project where in fact a key part of functionality was overlooked by the test team.

4. To Reusability

The test cases can be used in various versions of the software. Not only will your project management stakeholders be very grateful for the reduced project time and cost, but it will certainly help you when estimating project costs.

5. To Programmable

One can program the test automation software to pull out elements of the software developed which otherwise may not have been uncovered. Hence this should make your testing even more thorough, something you may not be so keen on when defect after defect is raised as a result!

Test Data Challenges:-

1) Testing the complete application:
Is it possible? I think impossible. There are millions of test combinations. It’s not possible to test each and every combination both in manual as well as in automation testing. If you try all these combinations you will never ship the product ;-)

2) Misunderstanding of company processes:
Some times you just don’t pay proper attention what the company-defined processes are and these are for what purposes. There are some myths in testers that they should only go with company processes even these processes are not applicable for their current testing scenario. This results in incomplete and inappropriate application testing.

3) Relationship with developers:
Big challenge. Requires very skilled tester to handle this relation positively and even by completing the work in testers way. There are simply hundreds of excuses developers or testers can make when they are not agree with some points. For this tester also requires good communication, troubleshooting and analyzing skill.

4) Regression testing:
When project goes on expanding the regression testing work simply becomes uncontrolled. Pressure to handle the current functionality changes, previous working functionality checks and bug tracking.

5) Lack of skilled testers:
I will call this as ‘wrong management decision’ while selecting or training testers for their project task in hand. These unskilled fellows may add more chaos than simplifying the testing work. This results into incomplete, insufficient and ad-hoc testing throughout the testing life cycle.

6) Testing always under time constraint:
Hey tester, we want to ship this product by this weekend, are you ready for completion? When this order comes from boss, tester simply focuses on task completion and not on the test coverage and quality of work. There is huge list of tasks that you need to complete within specified time. This includes writing, executing, automating and reviewing the test cases.

7) Which tests to execute first?
If you are facing the challenge stated in point no 6, then how will you take decision which test cases should be executed and with what priority? Which tests are important over others? This requires good experience to work under pressure.

8 ) Understanding the requirements:
Some times testers are responsible for communicating with customers for understanding the requirements. What if tester fails to understand the requirements? Will he be able to test the application properly? Definitely No! Testers require good listening and understanding capabilities.

9) Automation testing:
Many sub challenges – Should automate the testing work? Till what level automation should be done? Do you have sufficient and skilled resources for automation? Is time permissible for automating the test cases? Decision of automation or manual testing will need to address the pros and cons of each process.

10) Decision to stop the testing:
When to stop testing? Very difficult decision. Requires core judgment of testing processes and importance of each process. Also requires ‘on the fly’ decision ability.

11) One test team under multiple projects:
Challenging to keep track of each task. Communication challenges. Many times results in failure of one or both the projects.

12) Reuse of Test scripts:
Application development methods are changing rapidly, making it difficult to manage the test tools and test scripts. Test script migration or reuse is very essential but difficult task.

13) Testers focusing on finding easy bugs:
If organization is rewarding testers based on number of bugs (very bad approach to judge testers performance) then some testers only concentrate on finding easy bugs those don’t require deep understanding and testing. A hard or subtle bug remains unnoticed in such testing approach.

14) To cope with attrition:
Increasing salaries and benefits making many employees leave the company at very short career intervals. Managements are facing hard problems to cope with attrition rate. Challenges – New testers require project training from the beginning, complex projects are difficult to understand, delay in shipping date!

Key Features of the Test Data :-

Sensitive data discovery and classification

Automate the identification of sensitive data locations for consistent masking within and across databases.

Test data generation

Create synthetic data to fill gaps where production data cannot be used or doesn’t exist.

Data connectivity

Connect to a wide range of distributed and mainframe data sources from a single management platform.

Monitoring and compliance reporting

Engage compliance, risk, and audit teams to ensure ongoing alignment with data governance initiatives.

Data masking

Leverage a pre-defined, customizable library of masking functions to protect sensitive data elements including PII, PHI and PCI.

Data subset

Provision smaller datasets based on test plan requirements. Minimize infrastructure requirements and speed performance.

Pre-built application accelerators

Support for packaged applications to ensure application integrity and speed deployments.