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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

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

Software Testing In Regulated Industries

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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.

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

How to overcome common challenges in Exploratory Testing

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Exploratory testing involves testing system behaviour under various scenarios, with a predefined goal but no predefined tests. This focus on discovering the unknown makes exploratory testing both powerful and challenging.

“Exploratory testing is a systematic approach for discovering risks using rigorous analysis techniques coupled with testing heuristics.”

-Elisabeth Hendrickson

Although exploratory testing (ET) is not a new concept, its significance has increased exponentially in the dynamic field of software development. With its simultaneous learning, test design, and execution processes, ET represents a shift from the traditional, script-based testing methodologies. This approach is particularly beneficial in handling the complexities and unpredictabilities of modern software projects. It prepares testers to actively engage with the software, uncovering potential issues that scripted tests might overlook.

In exploratory testing, catching bugs is an adventure – a journey through the unknown aspects of software, where each test can reveal new insights. In the Agile world with rapid development cycles, exploratory testing stands out as a dynamic and responsive testing strategy, essential for ensuring software quality in a fast-paced environment.

Despite its advantages, exploratory testing has challenges that can interfere with its effectiveness. Testers often encounter hurdles in planning and adapting to newly discovered information, managing frequent context switches, maintaining comprehensive documentation, and effectively measuring the success of their testing efforts. Addressing these challenges is crucial for harnessing the full potential of ET. This blog will explore these common challenges and discuss how the Xray Exploratory App provides innovative solutions, enhancing the exploratory testing process and enabling testers to deliver high-quality results efficiently.

How to overcome challenges with Xray Exploratory App

The Xray Exploratory App proves to be a vital resource for successfully navigating these challenges. The tool supports the unique factors of exploratory testing, empowering testers to optimize their testing strategies while maintaining the flexibility and adaptability that exploratory testing demands. 

Planning and Learning

One of the primary challenges in exploratory testing is the balance between planning and learning. While ET is less structured than traditional testing, it still requires a level of planning to be effective. Xray Exploratory App facilitates one of the measures to counter this challenge and optimize your ET adoption –  session-based test management (SBTM). 

Testers must continuously learn from the software they are testing and adapt their approach accordingly. This requires understanding the project’s goals and the ability to quickly assimilate new information and apply it to testing strategies. One of the elements that helps with gaining the skills and experience is the structure of knowledge sharing. For example, if charters are handled as Jira stories, you get a centralized storage (a library of templates, of sorts) that has good examples which help educate any team member about the system and previous ET efforts.

Context Switching

Testers in an exploratory setting often deal with context switches. They must juggle different aspects of the software, switch between various tasks, and respond to new findings in real-time. Managing these switches efficiently is crucial to maintain focus and avoid overlooking critical issues. Beyond common techniques like Pomodoro, you can leverage two key features of Xray Exploratory App – saving sessions locally and editing the detailed Timeline with all your findings.

Proper Documentation

Unlike scripted testing, where documentation is predefined, exploratory testing requires testers to document their findings as they explore. This can be challenging as it requires a balance between detailed documentation and the fluid nature of exploratory testing. Testers need to capture enough information to provide context and enable replication of failure and future test repeatability without getting bogged down in excessive detail.

Xray Exploratory App addresses this challenge with the easily created chronological history of not just text notes but also screenshots, videos, and issues/defects created in Jira during the session (which accelerates the feedback loop).

Reporting and Measuring Success

Another significant challenge in exploratory testing is effectively reporting and measuring success. Traditional testing metrics often do not apply to ET, as its dynamic nature does not lend itself easily to quantitative measurement. Defining meaningful metrics to capture the essence of exploratory testing’s success is crucial for validating its effectiveness and value within the broader testing strategy. In many cases, such definitions would be very company-specific.

The good news – the seamless integration between Xray Exploratory App and Xray/Jira allows you to leverage centralized test management features, such as real-time reporting on several possible metrics (e.g. number of defects, elapsed time). That improves visibility and allows to clearly determine the status of not only exploratory testing, but all testing activities.

For instance, if we want to track defects/issues resulting from exploratory testing, we can see them linked to the test issue in Jira/Xray, which will then allow us to check them in the Traceability report. 

Overall, these challenges, though daunting, are manageable. With the right approach and tools, testers can navigate the complexities of exploratory testing, turning these challenges into opportunities for delivering insightful and thorough software testing.

Future outlook of Exploratory Testing

Exploratory Testing is becoming more acknowledged as an indispensable part of the testing strategy, especially given the limitations of conventional scripted testing. The ability of ET to adapt and respond to the complexities and nuances of modern software development is exceptional. As we look towards the future, several key trends are emerging that are set to shape the landscape of exploratory testing.

Artificial Intelligence (AI)

AI has the potential to significantly transform exploratory testing by automating certain aspects of ideation and, more so, data analysis processes. Leveraging AI in software testing in the correct way can enhance the tester’s capabilities, enabling them to focus on more complex testing scenarios and extract deeper insights from test data. AI can assist in identifying patterns and predicting potential problem areas, making ET more efficient and effective.

Integrations with other tools

The future of exploratory testing will see greater integration with various development, testing, and business analysis tools. This compatibility will streamline the testing process, enabling seamless data flow and communication across platforms. One of the pain points this trend will aim to address is losing time in writing automation scripts as a result of ET. Such integrations will enhance the overall efficiency of the testing process, allowing testers to leverage a wider range of tools and resources during their exploratory sessions more easily.

Enhanced collaboration

As software development becomes more collaborative, exploratory testing also adapts to facilitate better teamwork. Tools like the Xray Exploratory App incorporate features that promote collaboration among testers and between testers and other stakeholders. This collaborative approach ensures a more comprehensive understanding and coverage of the software, leading to better testing outcomes.

Compliance and reporting

Exploratory testing is being used more and more in ensuring compliance, areas like Non-Functional Requirements testing (security and performance), to help find more convoluted flaws and bottlenecks in intricate software systems. The trend is not surprising as the cost of compliance is increasing, both from the customer and the regulatory perspective. 

With the increasing emphasis on compliance and accountability in software development, exploratory testing has to evolve to provide more robust reporting and documentation capabilities. The ability to generate detailed and meaningful reports is essential, and tools like Xray are focusing on enhancing these aspects to meet the growing compliance demands.

The Xray Exploratory App is at the forefront of these changes, continually adapting and evolving to meet the future demands of exploratory testing.

Chart new heights in testing with Xray Exploratory Testing App

Exploratory Testing has become indispensable in our increasingly sophisticated and customer-centric digital landscape. Its importance has expanded across various sectors, including e-commerce, healthcare, and finance, highlighting the universal need for high-quality software experiences. The unique approach of ET, with its focus on discovering the unknown through rigorous analysis and testing heuristics, positions it as a key strategy in addressing the complexities of modern software systems.

The Xray Exploratory App stands out as a vital resource in harnessing the full potential of exploratory testing. The tool enhances the testing process by addressing the everyday challenges of planning, context switching, documentation, and reporting. It enables testers to navigate the intricacies of ET with greater efficiency and effectiveness, ensuring comprehensive coverage and insightful test results.

Explore the capabilities of the Xray Exploratory App and see firsthand how it transforms the exploratory testing experience. Dive into the world of enhanced software testing with Xray and discover the difference it can make in delivering superior software quality.

Author


Ivan Filippov, Solution Architect for Xray.

Ivan is passionate about test design, collaboration, and process improvement.

Xray is an EXPO Platinum partner at EuroSTAR 2024, join us in Stockholm.

Unlocking Success: Top 7 Trends for Attracting Visitors to Your EXPO Booth in 2024 

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As we stride into 2024, the landscape of software testing continues to evolve as the EuroSTAR and AutomationSTAR conferences continue to grow, demanding innovative strategies to stand out in the crowd. Here are the top seven trends for 2024 that will redefine how you attract visitors to your booth at Europe’s largest testing software testing conferences this June: 

Clare’s 7 Top Trends To Follow:

1. Interactive Booth Experiences: Engage your audience with interactive experiences. From live product demos to hands-on testing challenges, creating a dynamic and participatory environment at your booth will draw inquisitive minds. 

2. Virtual and Augmented Reality (VR/AR): Embrace the future with VR/AR experiences. Let visitors immerse themselves in solutions your product or service can achieve through virtual or augmented reality, providing a memorable and futuristic encounter. 

3. Killer Swag: Elevate your EXPO booth experience by offering killer swag that not only grabs attention but also leaves a lasting impression. Unique, high-quality swag items act as powerful magnets, drawing attendees to your booth. From trendy wearables to functional gadgets, thoughtful swag creates buzz, fosters engagement, and serves as a tangible reminder of your brand. In a sea of booths, having killer swag sets you apart, turning curious passersby into enthusiastic visitors and potential long-term connections. 

4. Networking Hubs: Transform your booth into a networking hub. Provide comfortable seating, charging stations, and conducive spaces for impromptu meetings. Networking hubs create an inviting atmosphere that encourages meaningful conversations. 

5. Gamification for Engagement: Infuse an element of fun into your booth with gamification. Create interactive games or challenges related to software testing concepts. Attendees love the opportunity to learn while having a good time. Get involved in the annual EXPO prize-giving and display your AMAZING prize on your booth over the 3 days to attract attention and collect leads via the sign up. 

6. Short Demo Sessions: Elevate your booth’s presence by hosting 3–5-minute demo sessions during the networking breaks. Conduct brief, impactful presentations on emerging trends, best practices, or case studies about your products and services. Position your booth as a knowledge hub within the conference. 

7. Social Media Integration: Leverage the power of social media to amplify your booth’s visibility. Follow and use the event-specific hashtags #esconfs and encourage attendees to share their experiences online, and host live Q&A sessions. Utilise social media platforms to foster engagement before, during, and after the conference. 

Embracing these trends ensures your booth becomes a magnetic destination withing the EuroSTAR EXPO, attracting a diverse audience of software testing professionals. Stay ahead of the curve, make lasting impressions, and turn visitors into valuable connections at EuroSTAR in 2024. 

For more information on how EuroSTAR can help you achieve your business goals, check out the EuroSTAR 2024 EXPO brochure or book a call with me.

Clare Burke

EXPO Team, EuroSTAR Conferences

With years of experience and a passion for all things EuroSTAR, Clare has been a driving force behind the success of our EXPO. She’s the wizard behind the EXPO scenes, connecting with exhibitors, soaking up the latest trends, and forging relationships that make the EuroSTAR EXPO a vibrant hub of knowledge and innovation. 

t: +353 91 416 001 
e: clare@eurostarconferences.com