Software Testing is the Key to Success
According to a study by the Economist Intelligence Unit,
“Software is the most important driver of productivity in an organization.”
This means that if you want your business to grow and be successful, you need a software that will allow you to grow with ease.
Software should always be flexible enough to change with the needs of an organization. Understanding a software’s flexibility involves testing how robust and efficient in keeping pace with innovation and new industry trends.
Development teams that follow strict testing principles and recognize these latest market changes can ensure the software still functions effectively and efficiently.
Testing helps organizations manage the risks associated with new releases, by providing early warning signals about potential defects or failures before they affect customers or end-users.
Software testing is an integral part of the development lifecycle, with fewer disruptions, more efficiency and greater cost savings, ensuring organizations can ensure they stay competitive.
The wide range of QA and software testing services and quality-first engineering in the iData toolkit have proven to limit the number of unknowingly existing bugs and defects introduced into a system, increasing customer satisfaction in the long-run.
This involves a range of requirements, but will typically include:
- One-time testing
- Functional testing
- Compatibility testing
- Localization testing
- Performance & load testing
- Usability testing
- Security testing.
In a production or pre-production environment, our methodology demands a focus on delivering end-to-end data accuracy and expertise tailored to either complex or highly regulated sectors from higher education to legal services organizations.
Combined with the full suite of tools in the iData toolkit, synthetic or obfuscated data can be generated to support multi-sector organizations with the need for added rigor to testing services.
Don't Let your Test Data Hold you Back
Test data must be managed appropriately by data handlers to avoid catastrophic failure and breaches in legislation. As an ISO9001 and ISO27001-certified organization, the importance of managing test data in line with security legislation is paramount at IDS.
We use a no-nonsense test data management approach, 100% of the data is assured at every stage, which is also delivery framework agnostic. It does not matter if you are in an agile environment or using a traditional \ waterfall methodology.
IDS recommend establishing a high-level strategy that captures the main components of a test data management strategy.
This strategy can evolve over the duration of your journey to delivering your test data capabilities. The reason for this is to ensure that your approach is mobile and can be adaptable. By creating your own data, you have an opportunity to increase the testing coverage and reduce business risk.
We focus on four main components at a high-level:
- The purpose of your data
- Data set type selection
- Test data management approach
- Business intelligence for environments.
This process is complex to master and will require input from subject matter experts within the organization who can help provide deeper insight and understanding.
Using Machine Learning to Save Clients Time & Money
As manual testing approaches slow the efficiency and momentum of large-scale digital transformation deployments, this not only costs time and money, but can also deflate staff motivation. Frustrated project leads can cause internal team friction and further issues in the test environment.
Therefore, in 2022, the software industry will see more adoption of codeless automated testing tools to improve efficiency and consistency. With test cases becoming easier to manage, this way, naturally more people will want to adopt these tools.
Intelligent automation of obfuscation transform scripts, as performed for a major healthcare organization, can better simulate human input processes and test software in a way that is simply not possible with human testers.
At IDS, we are able to augment human testing with a suite of machine-learning tools to deliver end-to-end assurance and detailed reporting, in any testing framework, while at the same time, saving clients time and money.
A few ways iData & the Kovenant™ methodology have incorporated machine learning algorithms to deliver value for our clients include:
ETL Testing with iData
ETL - Extract/Transform/Load
This is a further automated process using machine learning capabilities to extract data from source systems, transform the information into a consistent data type, then load the data into a single depository. ETL testing refers to the process of validating, verifying, and qualifying data while preventing duplicate records and data loss.
ETL testing ensures that the transfer of data from various and diverse sources to the central data warehouse occurs with strict adherence to transformation rules and is compliant with all validity checks.
It differs from data reconciliation used in database testing in that ETL testing is applied to data warehouse systems and used to obtain relevant information for analytics and business intelligence.
The Purpose of your Test Data
How Will your Data be Used?
How you use the data will drive how you create and maintain the data. We have several uses for test data, and it is not always for testing purposes, we have experienced a multitude of differing reasons to own and manage test or non-production data effectively. Each use has its own requirements.
Below are a number of key examples;
Training Application: To help support the training of business users on the application they will require access to production-like data to make the training exercises more realistic and meaningful. This use of test data will be on a lower consumption than the more development and test functions.
Development: Developers use test data too! Some unit tests will require data to ensure the functionality being developed is delivering as promised. As with training, this would generally be a lower consumption of data and would be less destructive in its usage.
Test Automation: This is the golden ticket in terms of the beneficiaries of owning a test data management strategy. To deliver efficient test automation, you must have a mechanism for managing test data which supports frequent execution of your tests. Either generation of test data or refreshing your data back to a base state prior to execution. Having a sophisticated approach to test data management will empower your automated testing solution to shift to the next level and support continuous testing,
Continuous Deployment and DevOps Models: Without consideration of test data in this area, you will fail to deliver any of those models. With performance testing, the volume of data is key! Being able to consume huge volumes of data in the right state must be part of the solution.
Continuous Testing as the New Norm
One of the most important aspects of software development is to make sure that it is done in a way that ensures high-quality software. But with automation, it is hard to accurately measure the quality as automated deployment in software testing does not always account for all possible scenarios.
Software testing helps ensure that there are no bugs in the code which might lead to system crashes or data loss. So, it’s important to provide an objective analysis of what’s happening in the software development life cycle (SDLC).
Now that agile practices have matured and DevOps initiatives have entered the corporate agenda, quality engineering practices have evolved. These emergent practices include continuous integration (CI), continuous delivery (CD) and continuous testing (CT) as key catalysts for enabling quality at speed and fill the gaps automation may leave behind.
Continuous testing is - by far - the most difficult, even with automated solutions. Executed correctly, it serves as the agile process’ centerpiece, bringing everything together to automate business intelligence, focus teams on accurate information and align business requirements with technical goals.
All to ensure a single version of the truth in the data for every program and project.
To protect data quality and control the risk associated with these daunting processes, IDS accelerates automated quality engineering for enterprise application testing on SAP, Salesforce, ServiceNow, Oracle, and many other popular enterprise applications.
So that organizations can innovate faster, while reducing business risk, iData’s built-in engineering tools for software testing in non-production environments include:
Obfuscation – This technique of masking and changing critical fields to obscure data is useful for testing purposes and training models. It helps safeguard sensitive information from unauthorized or malicious access. Obfuscation’s applications in software testing include protecting the software’s code, securing confidential communications and ensuring privacy of data.
IDS achieved a successful testing project with a major healthcare organization by applying diverse levels of obfuscation rapidly to save on time and cost.
Synthetic Data Generation – Rather than masking data, brand new data is created that looks and acts like real data. iData shows users the original values versus the synthetic tagged values in different tables. However, no characteristics can be identified, meaning no human error can be applied.
It is through these rigorous processes and end-to-end testing that IDS delivers 100% data certainty, through 100% of the journey, 100% of the time.