Lauren Payne

The Deloitte Digital Tester: Lifting the Curtain on AI-powered Automation

May 2, 2024 by Lauren Payne

In today’s technology field it’s almost impossible to discuss the future without mentioning the big impact Artificial Intelligence (AI) will have, specifically Generative AI. The concept behind generative AI is not new, but has seen a breakthrough to a wider audience in recent years, with AI-tools such as OpenAI ChatGPT, DALL-E, Microsoft Copilot becoming available. Generative AI is able to – you guessed it – generate outputs such as text, images, audio and all of their derivatives, which up until recently were the exclusive domain of the human brain.

Generative AI is driving a wave of technological innovation that can be felt far and wide. This is no different for our own field of expertise: Digital Test Management. We’re currently still witnessing the early days of a transformation which will impact most aspects of how we test and validate the software applications we use every day. The importance of ensuring whether an application is working as designed and intended has always been paramount: how can we ensure the trust of people and organizations in products and services if we don’t have the evidence to show for it? Generative AI will play a big role in transforming our ways of testing, allowing people to work more efficiently, automating many tasks and greatly improve test coverage. In the end this will make it possible to find and fix more defects earlier, ensuring a much smoother – and in many cases safer – end-user experience.

To this end, we want to introduce the Deloitte Digital tester, an AI-driven test platform which is capable of supplementing human testers as an automated and independent tester across the entirety of the testing journey!

Introducing the Deloitte Digital Tester

The recent advances have made it possible to pioneer a novel concept: a fulltime, AI-driven ‘digital tester’ that is capable of interpreting requirements and user stories to subsequently create test cases, execute them, log defects and finally report on the result. This is not science-fiction, but already reality. The Deloitte Digital Tester solution was built to operate across all phases of testing and fully integrates with existing testing tools and ecosystems. It can autonomously perform the following tasks:

Test Design: generate test cases from requirements and user stories.
Test Planning & Scripting: generate automated test cases or convert existing ones.
Test Data Creation: generate relevant test data to support test execution.
Test Execution: execute test cases and validate the results, all to create defects and support tickets where required.
Reporting: show clearly the test execution progress and outcome analysis by generating consolidated reports.

The Deloitte Digital Tester is not intended to replace human testers, but rather supplement them. This brings some key benefits: human testers will be able to focus on defining and validating business focused test strategies, design and architecture. They will be able to execute more value-adding test cases in parallel to the digital tester, evaluate the overall results and confirm the accuracy. Freeing up human testers also allows for more exploratory-based testing and validation of business interactions in the application. It also keeps testers motivated by automating many repetitive tasks and repeated regression testing. Additionally, the specific skills of the Deloitte Digital Tester allow the solution to do many additional test runs and eliminate blind spots, strongly increasing test coverage while reducing the overall time required to script and run tests.

Benefits of Next-Gen Automation

Our AI-solutions plays a key role in taking Test Automation to the next level. For several years already the concept of Test Automation has firmly rooted itself in the test management sphere: before we had AI, test cases were already being automated to be run as many times as required, and without human input other than creating the automated test cases in the first place and interpreting the results. This allowed for quicker test execution, high test case reusability, improved regression testing and even the creation of large amounts of test data to assist manual testing. With today’s AI-capabilities, the Deloitte Digital Tester is taking the next step: it is able to automate the creation and execution of test cases and evaluate the outcomes. This approach brings many benefits, the main ones we’ll go through here:

Continuous Testing becomes a truly integral part of the software development process, rather than testing being handled during specific phases, often post-development. The Deloitte Digital Tester allows us to automate testing activities and ensure quicker execution and more efficient identification of defects. This can be realized through In-sprint automation, where test cases are created while development is still ongoing. These test cases can already expose defects that can subsequently be addressed as early as possible. The sooner a defect is found, the lower its impact and the cheaper the cost to fix it.
AI/ML-based Test Data Management utilizes AI and machine learning to optimize the generation of representative test data while at the same time masking sensitive information such as personal data or confidential information. Integrating the Deloitte Digital Tester with the wider testing ecosystem makes the benefits even greater by increasing efficiency across the full landscape.
Self-Healing allows automated test cases to automatically update themselves in response to changes in the application’s development. Traditionally automated test scripts required a certain level of human intervention to cover changes in the application being tested. The Deloitte Digital Tester is AI-enabled and scriptless, while at the same time employing machine learning algorithms to dynamically adapt to changes in the application, reducing the need for maintenance. This is particularly relevant in Agile CI/CD environments, where rapid iterations are dependent on efficient regression testing.
Increased Test Coverage is realized by AI-driven automated testing. This allows for a scalable and broad-spectrum approach to validate end-to-end (E2E) processes. Additionally, AI algorithms enable us to identify and prioritize test cases based on their potential impact on critical business processes, thereby optimizing test coverage to focus on the areas with the highest risk.
Product Validation at Scale is a complex sounding term that signifies how the Deloitte Digital Tester enables organizations to industrialize testing by automating repetitive testing tasks and streamlining the testing process. By standardizing testing practices and creating reusable test assets, organizations can achieve consistency and efficiency in product validation efforts. Additionally, AI capabilities facilitate the analysis of test results and identification of trends or patterns across multiple product lines, enabling continuous improvement of testing processes and product quality.

An Impact that Matters

The above brings us to a key question: what impact does the Deloitte Digital Tester make once an organization chooses to implement it? Especially in case of projects with multiple releases the benefits can be very high when compared to the initial investment. What’s important to note is that the business case behind implementing the Digital Tester will depend on the testing maturity level of the organization. The stronger an organization’s testing capabilities are, the faster the Digital Tester will breakeven and start to provide ongoing benefits and efficiencies compared to traditional automation or manual testing. However, even in case of low maturity levels of adoption, the Deloitte Digital Tester allows an organization to break-even 2-4 months earlier compared to manual testing.

Let’s assume that an organization is running on average 500 test cases per month, and they choose to automate up to 80% of their testing lifecycle efforts.^[1] If we are looking at a timeline of 1 year, we can distinguish 2 phases:

Ramp-Up Period (4 months in case of high maturity): The Digital Tester requires an initial period during which the solution is trained, and its automation capabilities are being built. If more traditional testing is already going on during this time, this will require more resourcing to maintain these efforts in parallel.
Benefit Realization Period (8 months & beyond): Once implemented, efficiencies and automation come into play. This drastically reduces any efforts to automate activities and sets the stage for manual testing to be much more focused and exploratory, for example cross-functional end-to-end-testing, business interaction testing and risk-based deep dives.
[1] The complexity distribution of test cases in this example is 30% simple, 30% medium, 20% complex, 20% very complex.

In this particular use case, we observe the following quality outcomes by increasing test coverage and next-gen automation:

Future-Proofing your Testing Capabilities

The advent of Generative AI, exemplified by the Deloitte Digital Tester, marks a big step forward in the evolution of software testing and quality assurance. As our world becomes ever more complex, increased consumer scrutiny, regulations and market trends are challenging organizations in many ways to be more fast and agile. In this context, it’s hard to overestimate the increased importance of testing new software applications in the most efficient and thorough way possible. The Deloitte Digital Tester can play a pivotal role in taking your testing capabilities to the next level. This enables you to ensure the smoothest and safest end-user experience for products and services, crucial to maintaining the trust of all stakeholders. It is often said that building a reputation takes years but losing it can happen in a matter of days or even hours.

Rising to meet these challenges, the Deloitte Digital Tester represents a pivotal shift towards a future where AI-driven automation augments human testing capabilities, upgrading the way we approach software testing. As organizations embrace this technological evolution, they not only future-proof their testing processes but also pave the way for innovation and excellence in the way they deliver their products and services.

Contact Details

Thomas Clijsner, Partner, Deloitte Risk Advisory

Tel: +32 479 65 06 96 Email: tclijsner@deloitte.com

Rohit Neil Pereira, Principal, Deloitte Consulting LLP

Tel: +1 916 803 0079 Email: ropereira@deloitte.com

Ramneet Singh, Director Deloitte Risk Advisor

Tel: +32 471 61 89 67 Email: ramnesingh@deloitte.com

Dirk Evrard, Manager Deloitte Risk Advisor

Tel: +32 472 75 92 03 Email: dievrard@deloitte.com

Deloitte is an Exhbitior at EuroSTAR 2024, join us in Stockholm

Lowering Testing Barriers with Computer Vision-Based AI

April 30, 2024 by Lauren Payne

The constant surge in digital transformation forces organizations to perform tests on an increasing number of platforms but still get results as quickly as possible. Manual testing just can’t keep up with the speed of business, so teams turn to test automation. But traditional software testing tools are only effective to a point. Generally, these tools rely on identifying objects on the screen through their internal representation—e.g., coordinates, class name, type, and many other. This method of identifying objects can be very fragile. Even a small change might result in the tool failing to find the object. The drawbacks of these techniques prevent teams from scaling their test automation efforts up to the levels they require.

To that end, the most common test automation challenges include:

• Relentless test maintenance—Tests that rely on unique object properties can be susceptible to breaks, thereby making testers perform regular updates to ensure their tests still run on each supported environment.

Test execution time is too long—Even if a test set runs without interruption, it can take a significant amount of time to run all the tests to completion.
Insufficient test coverage—Teams must support an ever-expanding range of platforms, devices, and operating systems, requiring testers to customize the tests for each environment.
Test creation fatigue—It takes time to build and design effective tests, with much effort required to uniquely identify on-screen objects that are part of the test.

What our research at OpenText revealed was that automated object detection with computer vision is key to lowering these barriers.

Computer Vision-Based AI for Automated Object Detection

Recognizing objects without knowledge of their internal representation is one key objective to developing an AI engine. This goal can be accomplished by combining AI-based algorithms that accurately and consistently recognize objects regardless of device or environment.

For example, a test step might require clicking the shopping cart icon on a mobile app. The AI engine should be able to locate the shopping cart icon on the current screen without knowing:

If the screen is on a mobile device.
Whether the device is running Android or iOS.
If the screen is a desktop browser.
Whether it’s Chrome, Firefox, Edge, or another browser.

The ability to “Click the shopping cart” step should work under any circumstance with an AI engine using computer vision through an artificial neural network and optical character recognition.

Why Computer Vision?

An AI engine understands a screen’s composition and breaks it down into the unique objects that it contains. Additionally, the AI engine knows nothing about the implementation of the object. It treats the object as an image, regardless of the device or platform it comes from. As such, a powerful computer vision tool is needed and should be supported by an artificial neural network (ANN), a layered structure of algorithms that classify objects. It will train the ANN with many visual objects, resulting in a model that identifies objects it will likely encounter in applications under test (AUT). Thus, when the AI engine is tasked with locating a specific object, it utilizes the model to identify a match in the AUT.

In terms of architecture, a best practice is to implement the AI engine as a separate module. Rather than restricting it to a specific product, any product can theoretically use the engine.

OCR-Based Identification for Text Objects

AI engines also need to leverage OCR to identify text-based objects. These objects may themselves be part of the test, or they could function as a hint to identify the object’s relative location. This capability is useful if an object appears multiple times on a screen. For example, a login screen might have two text boxes, one for the username and one for the password. OCR helps identify which of the edit boxes is which. OCR can also identify a button by its textual caption.

Lowering and Removing Test Automation Barriers

AI-based test automation reduces the time it takes to build and design tests because objects are identified simply by looking at them. AI algorithms lower skill barriers because they identify most objects and are hidden from the user. Teams can also use the same test without modification on different devices and platforms. They simply procure an appropriate device and run the test on it as-is. And because the algorithm doesn’t rely on an object’s underlying implementation and properties, the test keeps running even if there is a change. If the test’s flow stays the same, the test will continue to run.

The final barrier yet to be removed completely is test execution time. Tests will always take a finite time to run; hence there is a lower limit on the amount of time they take. However, AI-based testing helps teams test earlier and provides robust mechanisms that parallelize and optimize test execution, reducing the wait time for results.

Author

Michael O’Rourke, Product Marketing Manager, DevOps Cloud

Michael O’Rourke is a product marketing technologist in cloud, enterprise software, and DevOps. His diverse background derives from 20 years of experience at HPE, IBM, T-Mobile, Micro Focus, and more. He holds a degree in Management Information Systems and is a certified Product Owner, Scrum Master, PMP, and Pragmatic Marketing practitioner. He is also an international speaker, trainer, and blogger. At OpenText, Michael drives the development and execution of go-to-market strategies for OpenText’s DevOps Cloud.

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

The Essentials of Test Data Management in Modern Software Development

April 25, 2024 by Lauren Payne

In today’s fast-paced software development world, Test Data Management (TDM) is more than a technical necessity; it’s a strategic asset. Let’s unpack the essentials of TDM and how it influences the quality, efficiency, and compliance of software testing.

The Core of Test Data Management

At its heart, TDM is about efficiently creating and managing data used for testing software applications. This involves ensuring the data is realistic, comprehensive, and secure, enabling testers to simulate real-world scenarios accurately.

Key Challenges in Test Data Management

Data Complexity: Modern applications demand complex and diverse data sets. TDM solutions must provide ways to generate and manage these data sets efficiently.
Data Privacy and Compliance: With regulations like GDPR, ensuring test data complies with privacy laws is crucial. TDM plays a vital role in anonymizing and protecting sensitive information.
Efficient Test Data Management: Balancing the need for quality data with storage and performance constraints requires efficient management of test data, often across multiple environments.

Approaches to Effective Test Data Management

Data Insight: Understanding the structure and dependencies within your data is vital. Data insight tools aid in creating more effective and relevant test data by providing a deeper understanding of the underlying data.
Data Masking: A critical aspect of TDM, data masking involves obscuring sensitive data within a test dataset. It ensures that the privacy and integrity of personal or confidential data are maintained, while still providing a functional dataset for testing.
Synthetic Data Generation: This involves creating artificial, non-sensitive data that closely mimics real-world data, addressing both complexity and privacy concerns.
Data Subsetting: This approach focuses on creating smaller, more manageable versions of your databases that contain only the data necessary for specific tests. It helps in reducing storage requirements and improving the performance of test environments.
Database Virtualization: Virtualizing databases allows for the creation of multiple, isolated test environments without physically replicating data. It’s essential for managing test data across different scenarios efficiently and reducing storage costs.
Automated Test Data Provisioning: Automation in TDM can significantly reduce the time and effort required to prepare test data, leading to more agile and efficient testing cycles.

The Impact of TDM on Software Development

Implementing robust TDM strategies leads to:

Improved Software Quality: Accurate and comprehensive test data ensures more effective testing, leading to higher-quality software.
Enhanced Compliance: With proper data masking and anonymization, TDM helps in maintaining compliance with data privacy laws.
Increased Efficiency: Automated and streamlined TDM processes contribute to faster testing cycles, reducing time-to-market for software products.

Conclusion

Test Data Management is an indispensable part of modern software development. Its impact on software quality, compliance, and efficiency cannot be overstated. Whether you’re a developer, a QA professional, or a project manager, understanding and implementing effective TDM practices is key to the success of your software projects. Tools like DATPROF play a supportive role in this journey, offering practical solutions to the complex challenges of TDM. Come meet us at EuroSTAR to learn more and see DATPROF in action!

Author

Maarten Urbach

Maarten Urbach has spent over a decade helping customers enhance test data management. His work focuses on modernizing practices in staging and lower level environments, significantly improving software efficiency and quality. Maarten’s expertise has empowered a range of clients, from large insurance firms to government agencies, driving IT innovation with advanced test data management solutions.

DATPROF is an exhibitor at EuroSTAR 2024, join us in Stockholm.

Top 10 Quality Issues to Solve at EuroSTAR 2024

April 23, 2024 by Lauren Payne

As we approach another EuroSTAR in Stockholm, many of us in IT and testing are reflecting on how we can improve our processes and strategies. It will be halfway through 2024, a time of year when doubts and concerns can creep in about our testing goals and improvements.

As you review your software quality strategy, I’d like you to reconsider our impulse towards ever-increasing test automation. Are we falling into the trap of trying to eat faster to lose weight? By only accelerating our efforts, we fail to confront the real root causes of testing inefficiencies and bugs.

You can’t automate quality into software

Just as diet fads promise thinness through gimmicks, we’ve been sold a fantasy. It promises us that more test automation will solve all our quality problems. But, while judicious automation provides value, many teams over-invest in automation at the cost of broader quality blockers.

When you have a hammer, everything looks like a nail, so teams hammer away endlessly to construct vast automated architectures. Meanwhile, quality lingers at the same mediocre levels.

10 Software Quality Issues to Address at EuroSTAR 2024

A common set of fundamental issues plague software projects. Teams often cite problems like:

Confidence and Stability – Frequent defects erode trust in releases
Defects into Production – Poor protection of live environments
Insufficient Test Time – Perpetual last minute “hardenings”
Release Uncertainty – Go/no-go decisions go down to the wire
Failing Requirements – Poorly defined scope leads to endless clarifications
Developer Rework – High levels of unplanned work
Team Misalignment – Lack of transparency across functional groups
Knowledge Silos – Bottlenecks form around key people or tools
Bloated Testing – Massive, unwieldy automation suites requiring heavy maintenance
Technical debt – Volumes of (re)work build over time, with insufficient knowledge to tackle it

Rather than focus on accelerating test execution speed, we need to confront why these problems arise in the first place. Increasing execution automation acts as a bandage; quality gaps stem from deeper process and strategy issues.

From silver bullets to software quality

At EuroSTAR 2024, let’s resolve to understand these root causes and thoughtfully solve them. For example, what drives unstable requirements? Is our analysis happening too late? What drives last minute surprises? Are we integrating and testing incrementally? Do our teams have transparency to coordinate their efforts? Are our tools and environments configured efficiently?

Thoughtful process analysis and improvement is less flashy than automation. Yet, it is far more impactful. Techniques like value stream mapping can uncover waste and barriers. Then, we can apply lean principles like limiting work in progress, optimizing flow, and amplifying feedback loops.

Rather than mindlessly generate more test cases, we should carefully curate automated checks to maximise value. Shifting left helps prevent defects, while good pipelines and test data strategies better isolate changes to fail fast. Teams skilled in exploratory testing and bug advocacy can further spotlight weaknesses early.

A measured (and measurable) approach to software quality

Let’s ring in EuroSTAR 2024 with renewed discipline against reactive thinking. Measure first, understand next, then optimize sustainably. Partner with stakeholders to align priorities. Anchor automation in business needs, not false promises of all-encompassing test suites. Spend smart to conserve budget for high-impact interventions.

Test excellence comes not from hasty automation, but thoughtful rigor, transparency, and accountability. Progress may seem slower, but leads to stable, high-velocity teams. Development, testing, and operations must come together as one delivery team sharing data, tools, and practices.

By taking a measured, evidence-based approach, we can target the disease rather than just treat the symptoms. Just as sustainable diets come from lifestyle changes, let’s commit to curing our quality ills through systems thinking.

This year, at EuroSTAR, let’s fix the fundamentals. Our automation will still be there to serve us, at sustainable velocities and capacities serving downstream needs. Set aside reactionary tactics, and instead bank quality through proactive strategies. Another EuroSTAR brings new perspectives, if we remain open to self-reflection and growth.

Restoring Confidence and Alignment with Curiosity Modeller

I speak to many organizations who experience the recurring quality issues and process misalignments discussed in this blog, each eroding their release confidence.

These challenges all have common roots:

1. Lack of transparency;

2. Incomplete system comprehension;

3. Inadequate feedback loops;

4. Unconnected teams.

Too often software gets built fast then tested slow. Teams lack shared artifacts to capture decisions and expected behaviours, undermining unified understanding.

Curiosity Modeller tackles these systemic issues by making system behaviour explicit early through collaborative models. These living models form the core artifact driving understanding, alignment and test generation.

Curiosity Modeller restores confidence and release quality by:

Visualizing expected functionality clearly across groups – no more hidden assumptions or differing interpretations of requirements.
Auto-generating optimal test cases to validate actual vs intended behaviour – preventing defects via early testing and signalling.

Producing regenerative tests tied directly to the models – no more realigning stale regression suites or maintaining copious test automation artifacts.
Enabling behaviour simulation for rapid prototyping – failing fast to prevent downstream rework.
Integrating with test execution and auto-generating Test Automation – overcoming misalignment, endless maintenance and skills silos.

Supporting API testing to safely exercise business logic – going beyond fragile end user flows.

Generating high-value test data to focus coverage on key scenarios – informed by risk models.

Shift left to deliver quality

Instead of intensifying downstream testing, Curiosity Modeller shines a light starting left in the lifecycle. Visual flows form the central artifact aligning groups on system behaviour, while preventing defects before code gets written. This proactive approach restores trust, accelerates releases, facilitates coordination and uplifts quality engineering. It delivers confidence through deep comprehension.

Find us at EuroSTAR 2024!

The Curiosity team will be in the EuroSTAR Expo hall in Stockholm – drop by to discuss how you can build software confidence early and throughout your delivery pipeline. Before then, why not head to our website to learn more about Curiosity Modeller, try it for yourself, and talk to us about your quality needs?

Author

Rich Jordan

Rich Jordan has spent the past 20 years leading change within the testing industry, primarily within Financial Services. He has led enterprise transformations and quality teams who have won awards in both Testing and DevOps categories. Rich has been an advocate of model-based test automation and test data innovation for over a decade, and joined Curiosity in November 2022.

Curiosity Software is an Exhibitor at EuroSTAR 2024, join us in Stockholm.

How Can Copilot Give Your Test Automation A Super Boost?

April 18, 2024 by Lauren Payne

When OpenAI launched ChatGPT a year ago, it made AI more accessible to the broader population and spurred the adoption of AI across various industries. Today, every enterprise is compelled to work with a generative AI strategy and constantly re-evaluate the impact of AI in their industry. The revolutionary generative AI has heralded a new age of applicability in tech, the most recent being Copilot. Copilots, or generative AI-powered assistants, have been steadily rising with generative AI adoption and advancement and are powerful enough to increase productivity and streamline how we work rapidly. 

“AI has undergone a significant transformation in the digital landscape, evolving from a tool for task automation to a collaborative partner. This evolution is driven by advancements in machine learning, natural language processing, and predictive analytics, enabling AI Copilots to understand and predict human needs, thereby providing adaptive and contextually relevant support.”- Forbes.  

An Overview of Copilot

Copilots are advanced collaborative tools based on large language models and generative AI technology. They can integrate smoothly into the existing software infrastructure and manage various activities, from summarizing everyday information to analyzing large datasets. Effective deployment of Copilot helps teams focus on more significant, demanding strategic initiatives. The role of a Copilot is only to augment, not replace, the workforce by helping complete monotonous, labour-intensive, or routine coding activities, thus enriching the overall work experience. 

Copilots leverage AI and align to accelerate code generation based on inputs from user intent and logic. This helps accelerate problem-solving, writing tests, and exploring new APIs (Application Programming Interfaces) without the tedious process of searching for alternate solutions on the web. It is a step ahead and can boost coding efficiencies for developers and testers. It is an intuitive, advanced AI-driven assistant that advances coding abilities by leveraging Machine learning, natural language processing, and advanced analytics. 

Let’s sum this up for you:  

Copilot and other programs like it, sometimes called code assistants, assimilate required information and put it right next to the written code.  
It tracks the code and comments (or possibly even descriptions written in natural language) in the specific file or files within the same project, assimilates it, and sends this text to the large language model behind Copilot as a prompt. 
The Copilot then predicts the expected programmers’ efforts and accordingly suggests the code.

Transitioning into test automation with Copilot.  

Large language models have gained popularity in various domains, including test automation, and help focus on the quality and reliability of software products. With increasing complexity among applications deployed, the demand for efficient and effective test case creation methods has consequently gained prominence. Generative AI tools, such as OpenAI’s ChatGPT and GitHub’s Copilot, have emerged as powerful and promising resources to accelerate test automation efforts. 

 While enterprise applications can be customized to an organization’s unique needs and processes, setting up Copilot may get tricky with constant release updates, new patches, and configuration changes.  

These custom tweaks still need to function post updates and getting them tested every time can be complicated. Test automation can eliminate the hassles of validation chaos and save time with minimum effort.  

An AI Copilot has its: 

Primary Use Case: Can execute actions.  Core technology strength: Real-time communication with APIs + SQL databases + Vectorized database. 

AI offers remarkable opportunities to streamline and enhance software testing, from script creation to execution. With an increasing rate of technical complexity change and reduced delivery cycle time, AI-powered solutions are stepping up to meet demands for continuous testing. 

QA engineers today have the challenge of updating their test automation suite before each release cycle. This challenge arises from smaller cycles coupled with faster changes- imagine fuelling a car when it is on track. With AI algorithms and automated test script generation, it is possible to keep pace with development and predict potential problem areas before they arise. 

 With AI, QA practices help manage fast-paced development cycles and improve overall quality assurance strategies covering UI (User Interface) Testing and API (Application Programming Interfaces) Testing. With its inherent ability for pattern recognition and predictive analysis powered by machine learning, AI can significantly enhance test coverage, allowing QA teams to focus more on complex tasks and less on mundane, repetitive activities. 

Can Copilot replace developers and automation testers? 

Just visualize a qualified Copilot in an airplane, seated and working in complete tandem with the pilot in charge of the flight. AI Copilots work alongside the user; the developer or the tester takes over basic tasks, handles basic questions, and makes suggestions. 

 Copilots herald a whole new era of testing in day-to-day development, thanks to the advent of AI and ML. Testers and developers can focus on real problems and avoid haggling with the text editor or googling for code completion. Despite its advantages, Copilot could churn out ineffective or invalid code with subtle bugs since it draws from the context it receives as input. AI must still catch all complex nuances a tester or developer can grasp and parse effectively. However, this can rapidly change once such services advance their capabilities with the help of data gathered from skilled users. 

Copilot could have its limitations, but it represents a significant step ahead in harnessing the power of AI in software testing. In an evolving technological environment, Copilot and similar AI tools can become invaluable assets in the testing arsenal, provided the right balance between Automation and human intervention occurs. Copilot is another tool that highlights what can be achieved, but realizing the knowledge and potential lies in the skill of resources.  

 A recent Microsoft report aptly points out that AI cannot fix challenges at work magically; employees still must be adept at critical thinking, creativity, and analytical judgment.  

 So, how can this Copilot super-power be optimized in development and testing areas? 

An all-around automated testing approach is step one. 

Innovative AI solutions can automate testing at scale and help with high-quality software outcomes. AI-powered platforms like Copilot analyze comments and prompts, create test cases, and go through the testing process in a self-healing environment. This does away with flaky testing, enhances test coverage, and improves productivity.  

ACCELQ Copilot 360 is the industry’s first full-cycle, holistic AI-powered test automation solution that goes beyond code generation. 

Features:   

Zero Boilerplate, Perfect Output: AI-Powered Logic Development  

No more wordplay hassles: AI-driven development lets one use regular English for scripting.
Complex passages with inter-dependent information  
Tolerant to user errors with auto-reconciliation with application state  
Real-time validation of developed logic

Code generation with Design-First: AI for Design and Sustainability

Modularity recommendations to achieve well-designed assets.  
AI-assisted parameterization for maximizing re-use.  
Standardized naming convention for asset consistency.

Uninterrupted Execution for Unmatched Reliability: AI for Execution Resilience  

Execution reliability is made possible by a closed-loop feedback engine  
Freedom from application DOM dependence  
Smooth, unsupervised executions in complex environments

    Proactive Change Management for Seamless Automation: AI at Work  

Multi-modal LLM capabilities for change analysis  
Proactive suggestions for change adaptation  
Minimized disruption to test automation.

    Asset Quality Evaluation Against Best Practices: AI-Driven Insights 

Analyze existing test suites for optimization opportunities.  
Identify duplication and redundancy.  
Score the project against a best practice baseline.

If you are eager to experience the power of ACCELQ Copilot 360, contact us and take the first step towards revolutionizing your test automation.

Author

Geosley Andrades, Sr. Director, Product Evangelist at ACCELQ

Geosley is a Test Automation Evangelist and Community builder at ACCELQ. Being passionate about continuous learning, Geosley helps ACCELQ with innovative solutions to transform test automation to be simpler, more reliable, and sustainable for the real world.

ACCELQ is an EXPO Exhibitor at EuroSTAR 2024, join us in Stockholm.

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

April 16, 2024 by Lauren Payne

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:

Speed and efficiency
Consistency in performance
Reusability of test scripts
Improved test coverage
Early detection of defects
Resource savings
Parallel testing
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

April 9, 2024 by Lauren Payne

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

April 2, 2024 by Lauren Payne

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.

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