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2024

How to Implement Software Test Automation: The QA Leader’s Guide

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When it comes to developing software, testing is not just a phase; it is a mission-critical function. Each line of code written demands meticulous scrutiny to ensure the end product meets user expectations.

Modern software has become far more complex than ever before. Today’s users expect more features and capabilities across multiple devices. There is increased demand for intuitive interfaces, real-time updates, and flawless performance — all necessitating exhaustive testing.

The need for a more efficient approach is apparent. Automation offers a beacon of hope for quality assurance (QA) leaders striving to optimize their processes.

QA Leaders: Today’s Automation Champions

Over the past decade, the role of the quality assurance (QA) leader has undergone a profound transformation. They have become the vanguards of automation, with the responsibility to steer their teams towards a more efficient and impactful testing process. This shift is not just a response to industry trends; it is a strategic move driven by the escalating complexity of modern software.

Of course, while automation enhances efficiency, it does not replace the critical role of human QA professionals. In fact, testing jobs in the US alone are predicted to increase by 25% in the next decade. Testers’ unique ability to think creatively, design complex test scenarios, and apply domain knowledge ensures that QA professionals will always play a key role in maintaining the overall quality of software products.

The Automation Advantage

When software development is moving at breakneck speed, with top companies deploying software multiple times per day, automation is the key to keeping up with the pace of innovation. Here are just some of the reasons why automation is so important for software testing:

  1. Speed and efficiency
  2. Consistency in performance
  3. Reusability of test scripts
  4. Improved test coverage
  5. Early detection of defects
  6. Resource savings
  7. Parallel testing
  8. Continuous testing

Automation in software testing is not just a trend, but a necessity.

Download the 30-60-90 day plan for qa leaders

Overcoming Common Implementation Hurdles

Despite the clear benefits of automation, there is still hesitation for some organizations to take the leap and automate their testing. This reluctance often stems from misconceptions about complexity and resource requirements.

Busting Complexity Myths

Contrary to popular belief, implementing test automation doesn’t require testers to be seasoned programmers. In fact, there’s an array of low-code and no-code tools specifically designed to empower testers to create effective test scripts without delving into complex coding.

The key lies in understanding that automation is not a barrier but a gateway to enhanced testing capabilities.

Unlocking the Door to Stakeholder Support

Another common hurdle for QA leaders is gaining stakeholder support for automation initiatives. Stakeholders often overlook the value of investing in software testing. However, research shows that organizations can achieve a net present value (NPV) of $4.69 million and an impressive return on investment (ROI) of 162% by leveraging the right automation tool.

When implemented thoughtfully, QA leaders can showcase improved efficiency, reduce time-to-market, and see tangible returns on investment within 9 months.

A Roadmap to Automation Success

Implementing automation need not be an arduous journey. When executed with precision, automation brings about transformative results. QA leaders can spearhead this transformation by focusing on three key aspects:

1. Accessible Automation Tools:

  • Explore user-friendly tools that require minimal coding expertise.
  • Leverage low code/no code platforms to empower testers without extensive programming backgrounds.
  • Opt for tools that offer UI testing capabilities, streamlining the process for non-technical team members.

2. Training and Support:

  • Invest in training programs to upskill the existing team on automation tools.
  • Provide continuous support and mentorship to ease the transition from manual to automated testing.
  • Foster a collaborative environment where knowledge sharing is encouraged.

3. Strategic Planning and Evaluation:

  • Develop a comprehensive 30-60-90 day plan outlining automation milestones.
  • Regularly evaluate progress and make necessary adjustments to ensure alignment with organizational goals.
  • Showcase tangible results and ROI metrics within the 90-day timeframe to secure future investment.

The Path Forward

For those intrigued by the prospect of transforming their testing processes, the eBook, A 30-60-90-Day Plan for QA Leaders, serves as a comprehensive guide. Your automation plan awaits, offering a blueprint for success in the world of test automation.

Author

Anna McCowan – Software Marketing Engineer

Anna McCowan is a software marketing manager at Keysight Technologies who joined the company as a technician in the wafer lab. Anna brings a wealth of technical knowledge from her bachelor’s degree in physics from Sonoma State University. She is a published technical writer who is passionate about educating others on the remarkable innovations in software technology, always striving to bring light to the advances in her field.

Keysight is an EXPO Gold Sponsor at EuroSTAR 2024, join us in Stockholm

Power Up Your Test Automation Practices With AI: Unlock Key Use Cases

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With the rapid pace of development cycles and the complexity of modern software systems, manual testing alone often can’t meet the demands of quality assurance. This is where test automation comes into play, offering efficiency, accuracy, and scalability. 

However, even with automation, challenges can still arise, such as maintaining test scripts, handling dynamic user interfaces, and detecting subtle defects. Enter AI, a game-changer poised to revolutionize test automation.

By infusing AI and ML into test automation, testers can build better automations faster through supercharged productivity, as well as improve accuracy and time-to-value through combining Generative AI and Specialized AI. Plus, testers can unlock new use cases by building AI-powered automations. 

So, what are some of the top uses for AI and ML in testing that can supercharge your application testing practices?

Deploy an agent that performs testing fully autonomously

An AI-powered agent can seamlessly tackle the challenge of finding critical problems in your applications, as it can interact with an application constantly. Then, the agent can build a model of your application, discover relevant functionality, and find bugs related to performance, stability, and usability. An agent can also aid in creating a resilient object repository while navigating through a target application, gathering reusable controls for future test case development. The potential of AI doesn’t stop there—the agent can then continuously verify and refresh controls within an object repository, enabling self-healing and maintaining automated tests. 

Generate automated low-code and coded tests from step-by-step manual tests

Have manual tests that you want to convert to automated tests? With the power of AI, you can accelerate automation by generating automated low-code and coded tests from manual tests, as well as leverage a flexible automation framework to ensure the resilience of your automated tests. And remember the object repository that your AI-fuelled agent assisted with creating? Equipped with this object repository, you can use AI to consider and smartly reuse any kind of object, such as buttons, tables, and fields.

Create purposeful and complex test data

With AI-infused large language models, you can supercharge your data through enhanced synthetic test data generation for manual and automated test cases. Using AI also enables you to create meaningful test data faster, allowing you to handle intricate data dependencies across multiple test data dimensions.

Streamline automated localization testing by leveraging semantic translation

By integrating AI into your test automation practices, you can leverage semantic automation and translation to remove the need for creating separate test cases for each language. The result? Maximized efficiency through seamless automated localization testing. Plus, you can run your automated test cases in different languages, allowing you to expand and scale your testing capabilities globally.

Overall, there’s unlimited potential for AI to supercharge continuous testing across the entire lifecycle—from defining stories, to designing tests, to automating and executing tests, to analyzing results.

UiPath Test Suite for AI-powered test automation

UiPath Test Suite, the resilient testing solution powered by the UiPath Business Automation Platform, offers production-grade, AI-fueled, low-code, no-code, and coding tools so you can automate testing for any technology while still managing testing your way. Later this year, you’ll be able to unlock AI-infused use cases for test automation, such as test generation, coded automations, and test insights, with Autopilot for Test Suite.

Author


Sophie Gustafson, Product Marketing Manager, UiPath Test Suite

Sophie Gustafson has worked at UiPath for two years and is currently a product marketing manager for Test Suite. Sophie has previous experience working in the consulting and tech industries, specializing in content strategy, writing, and marketing.

UiPath is an EXPO Platinum Partner at EuroSTAR 2024, join us in Stockholm.

Empowering Enterprises with Seamless Test Execution on a Unified Test Execution Environment

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The digital landscape is evolving every day and ensuring software quality is extremely important To ensure the applications meet the standards of functionality, reliability, and performance, businesses rely on extensive testing practices. Nevertheless, there are many hurdles to overcome to conduct tests successfully and efficiently due to the sheer complexity and size of current software systems.

Overseeing test execution gets harder as businesses mature and their software ecosystems get more and more complex. Traditional approaches often result in inefficiencies, delays, and increased expenses because they use diverse tools, fragmented processes, and fragmented teams.

These challenges are easily resolved with a unified test execution infrastructure, providing an integrated structure for managing and carrying out tests over the entire software development lifecycle. Enterprises can broaden test execution with ease and maximize efficiency and quality via a unified infrastructure, which integrates testing tools, standardizes processes, and fosters cooperation.

Unified Test Execution – The Need of the Hour

Businesses frequently use an assortment of testing frameworks and tools to meet distinct technological and testing requirements. However supporting this fragmented ecosystem can be challenging and can cause problems with compatibility, integration, and overhead.

As teams or projects function independently in siloed test environments, it may result in duplication, inaccurate testing procedures, and a lack of visibility across the operation. It can hinder interactions, limit teamwork, and reduce the effectiveness of the testing process as a whole.

Establishing consistency, repeatability, and scalability in test execution requires standardizing testing procedures and centralizing testing infrastructure. Enterprises can gain greater oversight and insight over their testing attempts, enhance resource utilization, and accelerate workflows by implementing a unified approach in testing.

LambdaTest: Empowering Enterprises with AI-driven Test Execution

The unified test execution environment offered by LambdaTest revolutionized the way businesses plan, organize, and execute their testing activities. LambdaTest’s range of AI-powered capabilities enables enterprises to increase test efficiency, enhance test infrastructure management, and deliver software designed to be of better quality at scale.

Through an assortment of innovative capabilities, LambdaTest uses artificial intelligence (AI) to improve testing processes. Its Auto Heal feature efficiently recognizes and fixes issues with the test environment in real time, minimizing interruptions and ensuring testing operations progress. The capacity to identify test failures promptly with fail-fast capabilities allows teams to address vulnerabilities early in the development cycle and accelerate resolution, thus enhancing overall efficiency. Also, test cases get intelligently prioritized by the Test Case Prioritization functionality using AI algorithms based on their impact and likelihood of failure. Teams can reduce time-to-market and improve software quality by employing this strategic approach to focus on high-risk areas, increase testing coverage within restricted schedules, and swiftly address important issues. 

Moreover, GPT-powered RCA (Root Cause Analysis) offers deeper insights into the underlying causes of test failures by analyzing test results and historical data. By identifying patterns, trends, and potential correlations, the AI engine enables teams to address root causes effectively and prevent the recurrence of issues. Furthermore, the Test Intelligence module provides actionable insights derived from comprehensive test data and analytics. 

By aggregating metrics, performance indicators, and user feedback, LambdaTest empowers teams to make informed, data-driven decisions, optimize testing strategies, and continuously enhance software quality.

Conclusion

LambdaTest’s unified test execution environment, enriched with AI features such as Auto heal, Fail fast, Test case prioritization, GPT-powered RCA, and Test intelligence with test insights represents a significant advancement in enterprise test automation. By harnessing the power of AI, LambdaTest empowers organizations to streamline test execution, mitigate risks, and deliver superior software products that meet the demands of today’s dynamic market landscape.

Author


Mudit Singh

 A product and growth expert with 12+ years of experience building great software products. A part of LambdaTest’s founding team, Mudit Singh has been deep-diving into software testing processes working to bring all testing ecosystems to the cloud.  Mudit currently is Head of Marketing and Growth for LambdaTest.

Lambdatest is an EXPO Gold Sponsor at EuroSTAR 2024, join us in Stockholm.

No-code Test Automation: What it Actually Means

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No-code test automation solutions are supposed to ease build and maintenance. But does no-code actually equate to an easier and lower maintenance test automation? Well, the short answer is – it’s complicated. We’ll go into more detail below. 

In this short article, we’re going to explain:

1.    What no-code test automation actually means

2.    How to assess no-code test automation vendors

3.    The test automation fallacy

4.    True no-code test automation

What no-code test automation actually means

To be no-code, a solution or test automation vendor doesn’t require a user to use a programming language to build an automated test. This makes test automation accessible to the people responsible for QA.  While the underlying solution is built on top of a programming language, the user will never have to interact with code. At least, that’s how it’s supposed to be. What is sold as an easy, no-code, scalable solution is often just a thin layer of UI based on top of a complex machine.

“No-code” and “low-code” are often used interchangeably as well. While in fact, they’re very different once you take a closer look. Low-code solutions do require developers, making them difficult to scale and maintain. 

And so the meaning of no-code has transformed and morphed into something that is no longer no-code So how can you assess whether a test automation vendor is actually no-code?

How to assess no-code test automation solutions

When you’re on the hunt for a test automation vendor, this is your time to put their solution to the test. 

Beyond the technology, process, and organizational fit, have the vendor show you how the solution performs on test cases that are notoriously complex for your business. 

Do they require coded workarounds to get the test case to work? Or can a business user or QA team member handle the build and maintenance of the test cases, without requiring developers? And when something breaks, how easy is it to find the root cause?

This is where you can understand whether no-code actually means no-code. 

We detail all the steps that you need to consider when you’re on the hunt for a test automation vendor in this checklist – you’ll be equipped to assess a vendor on their process, technology, and organizational fit, their ease of use and maintenance, training, and support. 

The test automation fallacy 

Automation tools are complex and many of them require coding skills. If you’re searching for no-code test automation, you’ll undoubtedly know that. Because 8 out of 10 testers are business users who can’t code​. 

And because of this previous experience, many have internalized three things:

1.    Test automation always has a steep learning curve – regardless of whether or not they’re no-code. 

2.    Test automation maintenance is always impossibly high

3.    Scaling test automation is not possible

But what if we told you that’s not the case. 

What if there actually was a solution that:

1.    Is easy to use, and can bring value to an organization in just 30 days. 

2.    That maintenance can be manageable, without having to waste valuable resources

3.    And that test automation can be scaled

Introducing Leapwork: a visual test automation platform

Leapwork is a visual test automation solution that uses a visual language, rather than code. This approach makes the upskilling, build, and maintenance of test automation much simpler, and democratizes test automation. This means testers, QA and business users can use test automation, without requiring developers. 

Users can design their test cases through building blocks, rather than having to use code. This approach works even for your most complex end-to-end test cases. 

Read the full article on Leapwork.

Author


Maria Homann 

Having worked for 4+ years at the forefront of the QA field to understand the pains of implementing testing solutions for enterprises, her writing focuses on guiding QA teams through the process of improving testing practices and building out strategies that will help them gain efficiencies in the short and long term.

Leapwork. is an EXPO Gold Sponsor at EuroSTAR 2024, join us in Stockholm.

How to choose between manual or automated testing for your software

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Testing software is the process of measuring a program against its design to find out if it behaves as intended. It’s performed in order to ensure that the developed app or system meets requirements and to enable further development of the product.

In the realm of software development, automated testing has become indispensable. Whilst it may require an initial investment, over time, it can more than repay the upfront cost. Manual testing offers advantages and disadvantages, such as being more prone to error yet providing insight into your visuals. Ultimately, it all comes down to what your project requires and the resources you have.

What is manual testing?

Manual testing is a type of application testing where QA or software engineers are tasked to execute test cases manually without using any automation tools. In this process, the testers utilize their own experience, knowledge, and technical skills to perform testing on the application or software in development. It’s done to find bugs and any issues in the software or application and ensure that it works properly once it goes live.

In contrast to automated testing, which can be left to run on its own, manual testing necessitates close involvement from QA engineers in all phases, from test case preparation through actual test execution.

Manual software testing with Test Center

Test Center, one of the tools in the Qt Quality Assurance Tools portfolio, provides a streamlined system for managing manual testing results, providing an overview of these alongside the automated test results. Additionally, there’s a test management section where the manual testing procedures and documentation can be set up and managed.

It has a split screen design where the left is for creating and managing the test hierarchy and includes making test suites, test cases, features, and scenarios. Meanwhile, the right pane is where changes to the test case or scenario’s description and prerequisites are made. It is also utilized to design and administer each part of a test.

What is automation testing?

Automation testing is the use of software tools and scripts to automate testing efforts. A tester will have to write test scripts that instruct the computer to perform a series of actions, such as checking for bugs or performing tasks on the target platform (e.g., mobile app or website). It helps to improve test coverage by enabling the running of more test cases than manual testing allows, and in less time.

Users with experience in scripting are needed. Tools like Selenium, QTP, UFT, and Squish are used for automation. Squish supports a number of non-proprietary programming languages, including Python, JavaScript, Ruby, Perl, and Tcl, thus, knowledge of them is advantageous.

Automated software testing with Squish

With Squish, you can automate your GUI testing across cross-platform desktop, mobile, embedded, and online apps and is usable on different development platforms. It simplifies what is typically a laborious and error-prone process – testing the user interface of today’s new and evolving apps.

Squish supports functional regression testing and automated GUI functional testing. It also helps you to automatically test your application in different environments, simulating users’ actions in a controlled and repeatable manner.

It includes: 

  • Full support for all leading GUI interfaces
  • Complete compatibility for various platforms (PCs, smartphones, web, and embedded platforms)
  • Test script recording
  • Robust and trustworthy object identification and verification techniques
  • Independent of visual appearance or screenshots
  • Efficient integrated development environment (IDE)
  • A large selection of widely used scripting languages for test scripting
  • Full support for behavior-driven development (BDD)
  • Full control with command line tools
  • Support for integrating test management with CI-Systems

Choosing manual or automated testing – Pros & Cons

There are a number of factors to consider when choosing between the two. For one, the biggest challenge facing software developers is the deadline. If the completion date is missed, then the company could lose customers. There is also an issue with budgets, as automated testing will require setup and maintenance.

Both solutions offer advantages and disadvantages, so you will need to examine them based on your needs. Here’s a closer look:

Manual testing

Pros:

  • Costs less than automated testing to initiate
  • Gives room for human perception, which helps provide insights into user experiences
  • Can provide valuable human feedback on your visuals (such as the colors, fonts, sizes, contrast, and button sizes used)
  • More efficient when test cases only need to be run once or twice
  • Small modifications can be applied quickly without having to be coded
  • Best for exploratory, usability, and ad-hoc testing

Cons:

  • Can be time-consuming and labor-intensive for QA engineers or testers
  • There is a possibility of human error
  • Cannot be reused – repetitiveness can lead to the work being quite tiring and dull for QA engineers or testers
  • Scales poorly as more manual testers would be needed for larger and more sophisticated applications

Automated testing 

Pros:

  • Works faster since it doesn’t rest or sleep
  • Has the ability to find more defects
  • Good for repetitive test cases
  • Can run multiple tests simultaneously
  • Increases the breadth of coverage compared to manual
  • Can be recorded and reused for similar test cases
  • Best for regression, performance, load, and highly repetitive functional test cases
  • Larger projects may require more manpower, but still less than manual testing as only new test scripts need to be written

Cons:

  • Exploratory testing is not possible
  • Needs to be coded
  • Unable to take human factors into account so it is unable to provide user experience feedback
  • Small modifications will have to be coded which can take time
  • Initial test setup and the required maintenance can be expensive

In most instances, automated testing provides advantages, but all technology has limits. When creating anything to enhance the consumer experience, human judgement and intuition provided by manual testing can make a difference.

Deciding on whether automated or manual testing is better for your organisation will largely depend on the number of test cases you need to run, the frequency of repeated tests, and the budget of your team. 

Ideally, your organisation should incorporate both as they each have their own merits. There are many instances where manual testing is still necessary and where automated testing could be more efficient. Either way, these two software testing methods are both important assets.

Read more about quality assurance from our comprehensive guide here: The complete guide to quality assurance in software development

Author


Sebastian Polzin, Product Marketing Manager,
Qt Quality Assurance

The Qt Company is an EXPO Gold Sponsor at EuroSTAR 2024, join us in Stockholm.

Data Testing VS Application Testing

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Introduction

This blog will explore the critical distinctions between application testing vs data testing, common mistakes with data testing, and reveal the consequences of neglecting it.  

Testing is a critical step for any software development project. Web applications, or mobile apps are tested to ensure proper functionality of the UI. But what about data-centric projects such as data warehouses, ETL, data migration, and big data lakes? Such systems involve massive amounts of data, have long running processes, and unlike applications, they lack screens. In such projects how does testing work? 

Data Testing vs Application Testing 

At a high-level data testing and application testing both share a common goal of ensuring functionality of a system, however on a closer look, it reveals that they have very distinct focuses and methodologies. Here is a quick list of differences for your reference. 

Project Types:  

  • Application testing spans a wide spectrum of web apps and mobile apps.  
  • On the other hand, data testing zeroes in on projects like data migration, Data pipelines, data warehouses. 

Testing Objective and Focus: 

  • Application Testing addresses everything from user interface intricacies to scripting, APIs, functions, and code integrity.  
  • For data testing, the emphasis is on ETL/data processes, process orchestration, and unique attention to data integrity sets it apart as a specialized discipline.  

Data Volume: 

  • Application Testing spans various dimensions, one of them being data. But in the scope of application testing data involvement is extremely limited to a few records created by a transaction. 
  • Data testing however, puts a spotlight on the critical nuances of data. The contrast is stark: compared to application testing, data testing involves millions and billions of records. 

Certification: 

  • In application testing the certification focus is on code integrity. 
  • Data testing is essentially designed to certify data integrity. 

Expected vs. Actual: 

  • Application testing compares the actual behavior of user interfaces and scripts vs expected. 
  • Data testing navigates the complex terrain of data integrity, migration accuracy, and the nuances of big data. 

Performance Testing: 

  • In application testing the focus is on the speed at which the UI or the underlying functions respond to a request. It is in the realms of microseconds. On the other hand, performance testing for Data is in minutes and hours. 
  • For data testing the performance is usually calculated by rows processed per second. It is usually computed in the time required to read data, transport data, process data and load data in a target database. The loading time is further calculated in terms of update, insert, and delete speed. 

Employee Skillsets: 

  • Both processes demand a skill set that combines technical acumen and a deep understanding of the tools at play. Application Testing requires proficiency in user interface testing, scripting, and tools like Selenium/JMeter. Application testing requires understanding screen behavior, and utilizing tools tailored to the unique challenges presented by data. 
  • In contrast, data testing necessitates expertise in handling data sources and target data, SQL, Data Models, and Reference data. Proficiency in scripting and code-level understanding is essential for application testing, while data testing demands a command over SQL for effective data manipulation and validation. 

Testing Tools: 

  • Application testing often employs tools like Selenium and JMeter. 
  • Data testing leverages specialized tools like iceDQ for comprehensive data quality assurance. 

Top Data Testing Mistakes

At the heart of the issue lies a fundamental misunderstanding – the perception that application testing and data testing can be treated interchangeably. 

  1. Ignore Data Testing: Organizations often neglect data testing. A QA professional with an application background does not understand data testing, while the data engineers are not classically trained in testing.  
  1. Lack of Dedicated Data Testing Team: The lack of a dedicated team will result in knowledge gaps. Dedicated teams is essential to properly train and acquire proficiency.  
  1. Application Testers for Data Testing: Just because someone is skilled in application testing does not mean that the person will have the know-how of data testing.
  1.  Manual Data Testing: Automation has become the mantra for efficiency in software testing, but this mantra is often focused more on application testing. Automated UI tests and functional checks take centre stage, leaving data testing to be more of a manual process. The absence of automation in data testing not only hampers efficiency but also introduces the risk of human error. 
  1. Data Sampling: In the absence of automation, organizations resort to manual data testing, a daunting task when faced with millions of records. Manual testing becomes a mammoth task to undertake, prone to errors, inconsistencies, and a significant drain on resources. The sheer volume of data makes it humanly impossible to ensure comprehensive testing, forcing the testing team to resort to testing sample data rather than the entire dataset. 
  1. Misuse of application testing tools for data testing: While tools like Selenium and JMeter excel in UI and functionality checks, testing data pipelines demands specialized tools. The mismatch not only results in inefficiencies but also fails to address the unique challenges posed by data-centric projects. 
  1. Low /No Budget for Data Testing: Organizations, in pursuit of flawless user experiences, often channel a significant portion of resources towards application testing tools and frameworks.  Meanwhile, data testing, which operates in the complex terrains of data migration testing, ETL testing, data warehouse testing, database migration testing and BI report testing is left with a fraction of the QA budget. 
  1. In-house Scripts or Frameworks: Some organizations realize the distinct nature of data testing and attempt to build in-house frameworks. However, this approach often has more disadvantages than advantages. In-house frameworks, while tailored to specific needs, may lack the scalability required for projects dealing with millions of records and complex data structures. The inefficiencies in this approach become apparent with the growth in data volumes and complexity.  

Consequences of Ignoring Data Testing 

  1. Cost and Time overruns 
  1. Complete failure of projects 
  1. Data Quality issues in Production 
  1. Compliance and regulatory risks 
  1. Reputation Risks 

Conclusion

To summarize the difference, while Application Testing and data testing share the overarching goal of ensuring the robustness of a system, they operate in distinct realms. Application Testing spans the broader landscape of application functionality, whereas data testing homes in on the intricate dance of data within the system. Understanding and appreciating these differences is crucial for organizations aiming to fortify their digital transformation.  

Recognizing the critical distinctions between application testing and data testing is the first step towards comprehensive Quality Assurance. Organizations must recalibrate their approach, acknowledging the unique requirements of data testing and allocating resources, budgets, and automation efforts accordingly.  

Embracing specialized tools like iceDQ which is a low code-no code solution for testing your data-centric projects is key to building software that stands the test of both user experiences and data integrity. 

For more details please visit our blog: https://bit.ly/3SWpgYs 

Author

Sandesh Gawande is the CTO at iCEDQ (Torana Inc.) and a serial entrepreneur.

Since 1996, Sandesh has been designing products and doing data engineering. He has developed and trademarked a framework for data integration – ETL Interface Architecture®. He consulted various Insurance, Banking, and Healthcare. He realized, while companies were investing millions of dollars in their data projects, they were not testing their data pipelines. This caused project delays, huge labor costs, and expensive production fixes. Herein lies the genesis of the iCEDQ platform.

iCEDQ is an EXPO Gold Sponsor at EuroSTAR 2024, join us in Stockholm

 

Myth vs. Reality: 10 AI Use Cases in Test Automation Today

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For decades, the sci-fi dream of simply speaking to your device and having it perform tasks for you seemed far-fetched. In the realm of test automation and quality assurance, this dream is inching closer to reality. With the evolution of generative AI, we’re prompted to explore what’s truly feasible. Embedding AI into your quality engineering processes becomes imperative as IT infrastructures become increasingly complex and integrated, spanning multiple applications across business processes. AI can help alleviate the daunting tasks of knowing what to test, how to test it, creating relevant tests, and deciding what type of testing to conduct, boosting productivity and business efficiency.

But what’s fact and what’s fiction? The rapid evolution of AI makes it hard to predict its capabilities accurately. Nevertheless, we’ve investigated the top ten key AI use cases in test automation, distinguishing between today’s realities and tomorrow’s aspirations.

1. Automatic Test Case Generation

Reality: AI can generate test cases by analyzing user stories along with requirements, code, and design documents, including application data and user interactions. For instance, large language models (LLMs) can interpret and analyze textual requirements to extract key information and identify potential test scenarios. This can be used with static and dynamic code analysis to identify areas in the code that present potential vulnerabilities requiring thorough testing. Integrating both requirement and code analysis can help generate potential manual test cases that cover a broad set of functionalities in the application.

Myth: But here’s the caveat: many tools on the market that enable automated test case generation create manual tests. They are not automated. To create fully automated, executable test cases is a use case that remains a myth and still requires further proof. Additionally, incomplete, ambiguous, or inconsistent requirements may not always generate the right set of tests, and this requires further development. Test cases may not always cover edge cases or highly complex scenarios, nor are they able to cover completely new applications. Analysing application and user interaction data may not always be possible. As a result, human testers will always be required to check the completeness and accuracy of the test suites to consider all possible scenarios.

2. Autonomous Testing

Reality: Autonomous testing automates the automation. Say what? Imagine inputting a prompt into an AI model like “test that a person below the age of 18 is not eligible for insurance.” The AI would then navigate the entire application, locate all relevant elements, enter the correct data, and test the scenario for you. This represents a completely hands-off approach, akin to Forrester’s level 5 autonomous state.

Myth: But are we there yet? Not quite, though remarkable technologies are bridging the gap. The limitation of Large Language Models (LLMs) is their focus on text comprehension, often struggling with application interaction. For those following the latest in AI, Rabbit has released a new AI mobile phone named r1 that uses Large Action Models (LAMs). LAMs are designed to close this interaction gap. In the realm of test automation, we’re not fully there. Is it all just hype? It’s hard to say definitively, but the potential of these hybrid LAM approaches, which execute actions more in tune with human intent, certainly hints at a promising future.

3. Automated Test Case Design

Reality: AI is revolutionising test case design by introducing sophisticated methods to optimise testing processes. AI algorithms can identify and prioritise test cases that cover the most significant risks. By analyzing application data and user interactions, the AI can determine which areas are more prone to defects or have higher business impact. AI can also identify key business scenarios by analysing usage patterns and business logic to auto-generate test cases that are more aligned with real-world user behaviors and cover critical business functionalities. Additionally, AI tools can assign weights to different test scenarios based on their frequency of use and importance. This helps in creating a balanced test suite that ensures the most crucial aspects of the application are thoroughly tested.

Myth: However, AI cannot yet fully automate the decision-making process in test suite optimisation without human oversight. The complexity of certain test scenarios still requires human judgment. Moreover, AI algorithms are unable to auto-generate test case designs for new applications, especially those with highly integrated end-to-end flows that span across multiple applications. This capability remains underdeveloped and, for now, is unrealised.

4. Testing AI Itself

Reality: As we increasingly embed AI capabilities into products, the question evolves from “how to test AI?” to “how to test AI, gen AI, and applications infused with both?” AI introduces a myriad of challenges, including trust issues stemming from potential problems like hallucinations, factuality issues, and explainability concerns. Gen AI, being a non-deterministic system, produces different and unpredictable outputs. Untested AI capabilities and AI-infused applications can lead to multiple issues, such as biased systems with discriminatory outputs, failure to identify high-risk elements, erroneous test data and design, misguided analytics, and more.

The extent of these challenges is evident. In 2022, there were 110 AI-related legal cases in the US, according to the AI Index Report 2023. The number of AI incidents and controversies has increased 26-fold since 2021. Moreover, only 20% of companies have risk policies in place for Gen AI use, as per McKinsey research in 2023.

Myth: Testing scaled AI systems, particularly Gen AI systems, is unexplored territory. Are we there yet? While various approaches and methodologies exist for testing more traditional neural network systems, we still lack comprehensive tools for testing Gen AI systems effectively.

AI Realities in Test Automation Today

The use cases that follow are already fully achievable with current test automation technologies.

5. Risk AI

It’s a significant challenge for testers today to manage hundreds or thousands of test cases without clear priorities in an Agile environment. When applications change, it raises critical questions: Where does the risk lie? What should we test or prioritize based on these changes? Fortunately, risk AI, also known as smart impact analysis, offers a solution. It inspects changes in the application or its landscape, including custom code, integration, and security. This process identifies the most at-risk elements where testing should be focused. Employing risk AI leads to substantial efficiency gains in testing. It narrows the testing scope, saving considerable time and costs, all while significantly reducing the risk associated with software releases.

6. Self-Healing

By identifying changes in elements at both the code and UI layer, AI-powered tools can auto-heal broken tests after each execution. This allows teams to stabilize test automation while reducing time and costs on maintenance. Want to learn more about how Tricentis Tosca supports self-healing for Oracle Fusion and Salesforce Lightning and Classic? Watch this webinar.

7. Mobile AI

Through convolutional neural networks, mobile AI technology can help testers understand and analyze mobile interfaces to detect issues in audio, video, image quality, and object steering. This capability helps provide AI-powered analytics on performance and user experience with trend analysis across different devices and locations, helping to detect mobile errors rapidly in real time. Tricentis Device Cloud offers a mobile AI engine that can help you speed up mobile delivery. Learn more here.

8. Visual Testing

Visual testing helps to find cosmetic bugs in your applications that could negatively impact the user experience. The AI works to validate the size, position, and color scheme of visual elements by comparing a baseline screenshot of an application against a future execution. If a visual error is detected, testers can reject or accept the change. This helps improve the user experience of an app by detecting visual bugs that otherwise cannot be discovered by functional testing tools that query the DOM.

9. Test Data Generation

Test data generation using AI involves creating synthetic data that can be used for software testing. By using machine learning and natural language processing, you can produce dynamic, secure, and adaptable data that closely mimics real-world scenarios. AI achieves this by learning patterns and characteristics from actual data and then generating new, non-sensitive data that maintains the statistical properties and structure of the original dataset, ensuring that it’s realistic and useful for testing purposes.

10. Test Suite Optimisation

AI algorithms can analyze historical test data to identify flaky tests, unused tests, redundant or ineffective tests, tests not linked to requirements, or untested requirements. Based on this analysis, you can easily identify weak spots or areas for optimization in your test case portfolio. This helps streamline your test suite for efficiency and coverage, while ensuring that the most relevant and high-impact tests are executed, reducing testing time and resources.

What about AI’s role in performance testing, accessibility testing, end-to-end testing, service virtualization, API testing, unit testing, and compatibility testing, among others? We’ve only just scraped the surface and begun to explore the extensive range of use cases and capabilities that AI potentially offers today. Looking ahead, AI’s role is set to expand even further, significantly boosting QA productivity in the future.

As AI continues to evolve, offering tremendous benefits in efficiency, coverage, and accuracy, it’s important to stay cognizant of its current limitations. AI does not yet replace the need for skilled human testers, particularly in complex or nuanced scenarios. AI still lacks the human understanding needed to ensure full software quality. Developing true enterprise end-to-end testing spanning multiple applications across web, desktop, mobile, SAP, Salesforce, and more requires a great deal of human thinking and human ingenuity, including the capability to detect errors. The future of test automation lies in a balanced collaboration between AI-driven technologies and human expertise.

Want to discover more about Tricentis AI solutions and how they can cater to your unique use cases? Explore our innovative offerings.

Tricentis offers next-generation AI test automation tools to help accelerate your app modernisation, enhance productivity, and drive your business forward with greater efficiency and superior quality.

Author

Simona Domazetoska – Senior Product Marketing Manager, Tricentis

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Software Testing In Regulated Industries

by

In today’s landscape of digital adoption and the rapid growth of software technologies, many domains leveraging technology are within regulated industries. However, with the introduction of more technology comes the need for more software—and more software testing. This article will touch on the unique attributes, challenges, and considerations of software testing within these regulated domains.

Defining “regulated” industries

While many industries have specific guidelines and domain nuances, we will refer to “regulated” industries as those that are governed by overarching regulatory compliance standards or laws. 

These governance standards in most cases impact the depth, agility, and overall Software Development Lifecycle (SDLC) on how these standards are developed into requirements and then validated.

Below is a sampling of some of these domains:

  • Healthcare
  • Manufacturing
  • Banking/Finance
  • Energy
  • Telecommunications
  • Transportation
  • Agriculture
  • Life sciences 

Unique requirements

Common characteristics that teams will likely encounter when analyzing the software quality/testing requirements in these environments include:

  • Implementation of data privacy restriction laws (like HIPAA)
  • Detailed audit history/logging of detailed system actions
  • Disaster recovery and overall data retention (like HITRUST)
  • High standards for traceability and auditing “readiness”
  • Government compliance and/or oversight (like the Food and Drug Administration / FDA)

These common regulatory requirements are critical for planning and executing testing and establishing a quality of recording artifacts essential to supporting auditing and traceability.

Testing considerations & planning

Many testers and their teams are now being proactive in using paradigms such as shift-left to get early engagement during the SDLC. As part of early requirements planning through development and testing, specialized considerations should be taken within these regulated industries.

Requirements & traceability

  • The use of a centralized test repository for both manual and automation test results is critical
  • Tests and requirements should be tightly coupled and documented
  • Product owners and stakeholders should be engaged in user acceptance testing and demos to ensure compliance
  • Test management platforms should be fully integrated with a requirement tracking  platform, such as Jira

Image: The TestRail Jira integration is compatible with compliance regulations and flexible enough to integrate with any workflow, achieving a balance between functionality and integration.

Once teams have solidified a process for defining and managing requirements and traceability, it becomes imperative to ensure that the quality of test records is not only accessible but also restricted to those who require it. 

This controlled access is crucial, particularly in auditing situations, where the accuracy and reliability of test records may play a critical role. This approach for access controls is commonly referred to as the “least privilege” principle.

Image: With TestRail Enterprise role-based access controls, you can delegate access and administration privileges on a project-by-project basis

Test record access controls

  • Limit test management record access to the minimum required for team members
  • Ensure only current active team members have test record access
  • Implement a culture of peer reviews and approval to promote quality and accurate tests

Image: TestRail Enterprise teams can implement a test case approval process that ensures test cases meet organizational standards.

As test cases and test runs are created manually or using test automation integrations like the TestRail CLI, it is important to maintain persistent audit logging of these activities. Within regulated industries, audit requirements and “sampling” may require investigation of the history and completeness of a given test that was created and executed against a requirement.

Image: TestRail Enterprise’s audit logging system helps administrators track changes across the various entities within their TestRail instance. With audit logging enabled administrators can track every entity in their installation.

Audit history

It’s important to maintain a log that allows viewing of historical data on test case creation and execution. This supports audit readiness for requirements validation traceability.

Lastly, as teams focus on the development, testing, and delivery of software, we have to be mindful of disaster recovery and data retention of the artifacts we create. 

In the same thought process as disaster recovery of a given system under test, the quality of records for testing and release must persist to support compliance requirements and audits. Although centralized test management platforms with integrated restore capabilities are preferred, various tools and processes can be used to achieve this.

Image: TestRail Enterprise’s configurable backup and restore administration features enable administrators to specify a preferred backup time window, see when the last backup was completed, and restore the last backup taken.

Self-assessments & internal auditing

For all teams that are iterating on engineering, testing, and overall SDLC improvements, it’s important to dedicate time to perform self-assessments. 

Self-assessments in the context of software testing and quality in regulated environments can be a highly effective tool for identifying process gaps and shortcomings. 

Self-assessment/audit evaluation criteria

Examples of critical areas to include in your self-assessments or audit readiness exercises include:

  • Having full traceability via linkage of all tests to the corresponding requirements​ artifact (such as a Jira issue or defect)
  • Tests that have been planned and executed are linked to a given release event/designation
  • Failed tests for a given release or sprint are linked to a defect artifact (such as a Jira defect)

Once a self-assessment or internal audit is performed, ensure that the team collects actionable information such as improvements to requirements traceability or more detailed disaster recovery documentation that can be used to improve the overall SDLC with a focus on core compliance best practices and standards.

Bottom line

Additional considerations and requirements must be made across the SDLC when operating teams within regulated industries. The early inclusion of these additional requirements with all team members is critical to ensuring compliance and overall success in audits and other regulatory assessments. 

Key Takeaways

  • Focus on traceability, ensure linkage of tests to requirements
  • More focus on security and access controls testing
  • Centralize all test artifacts in a repository with backups/data retention
  • Plan and execute disaster recovery validation

Watch the Testing In Regulated Industries webinar on the TestRail Youtube channel for more information on the unique challenges and characteristics of software testing in regulated industries!

Author


Chris Faraglia, Solution Architect and testing advocate for TestRail.

Chris has 15+ years of enterprise software development, integration and testing experience spanning domains of nuclear power generation and healthcare IT. His specific areas of interests include but are not limited to test management/quality assurance within regulated industries, test data management and automation integrations.

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