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Codeless quality is about using automation and AI to support faster development and release cycles with less time and effort. By reducing the learning curve…

Codeless quality is about using automation and AI to support faster development and release cycles with less time and effort.

By reducing the learning curve for quality testing, companies can maximize testing coverage, allowing those closely involved in business analysts (BA) and product experts who typically have deep understanding of the product and/or service but lack technical acumen needed for automation.

Streamline testing and improve application quality.

Discover how to implement codeless quality now.

Let’s explore how codeless quality complements traditional test methods to deliver powerful, automated testing for impressive business wins.

Codeless quality helps teams test

Codeless quality removes the need for complex scripting and deep coding know-how, allowing teams to create and execute automated functional tests with ease.

This development approach leverages a user-friendly, visual interface, allowing users to create tests using point-and-click UI driven by AI object-detection, automated reusable steps to use for future testing and add checkpoints, and verification steps to ensure applications respond as expected.

Plus, with the right codeless testing tool, organizations can lean on AI object-detection to author resilient testing scripts, quickly and with little effort. Those scripts are immune to any changes in the applications underlaying framework, work seamlessly across browsers and mobile devices, and require very little maintenance between application releases.

Codeless test solutions help meet digital transformation mandates for greater test coverage and accelerate the software development cycle in three ways:

• Reduce coding hurdles. Allow employees with diverse technical backgrounds to test applications with confidence. Guided by a graphical interface and intuitive approach, users can create test scenarios, verify the application’s adherence to specified functionality guidelines, and certify new releases, quickly while development is still going on. Users simply move through a test case and the codeless tool transcribes that experience into an automated test, freeing strapped programming resources and script maintenance overhead.
• Make testing more collaborative. Giving everyone the ability to create test scripts without writing code promotes communication and collaboration among team members during the testing process—ensuring application functional glitches are identified and resolved early on. By streamlining the test automation process with application development processes, issues are being detected earlier, often during development, so they can be fixed sooner, thus users are able to focus on other value-add activities, such as test analysis and result interpretation.
• Battle-test functionality. Create and execute a wide range of functional tests. With expanded testing coverage and the ability to run test scripts in multiples environments across multiple platforms, users spot potential glitches across different user scenarios and usage patterns.

Stay a step ahead with software development

Codeless capabilities offer a powerful solution for streamlining testing and improving application quality. By simplifying test automation and creating reusable scripts, testers can optimize efforts to ensure high-quality software delivery.

Enhance your testing capabilities to stay competitive in the ever-evolving software development landscape. Visit ValueEdge Functional Test and start implementing codeless quality today,

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In today’s Salesforce-driven world, quality assurance and testing are critical components of successful software development. And as we say here at Provar often, testing is no longer a nice-to-have — it’s a necessity for staying ahead of the competition and delivering the best possible product to your end users.

Efficiently managing and leveraging your testing data can lead to more accurate tests, increased visibility, accelerated testing processes, and future-proofness for your end-to-end operations. Provar’s suite of Salesforce-centric testing tools — Provar Automation, Provar Manager, and Provar Grid — offers powerful solutions to unlock your testing data, ensuring higher quality across your entire pipeline.

Let’s discuss how to unlock your testing data and use it to its full potential with these three solutions.

Provar Automation: Streamline and Enhance Test Creation

Provar Automation is designed to simplify and optimize the creation and execution of automated tests. By integrating seamlessly with Salesforce and other platforms, and with a big nod to its metadata-driven test building capabilities, Provar Automation enables teams to build robust, reliable tests that can adapt to changing requirements.

Here are some key benefits of Provar Automation in unlocking your testing data.

Enhanced Test Accuracy

With Provar Automation, you can create tests that closely mirror actual user interactions. This precision reduces false positives and negatives, ensuring that your testing data is accurate and actionable.

Comprehensive Data Utilization

Provar Automation allows you to harness your existing testing data effectively. By using real-time data inputs and outputs, you can build tests that are reflective of real-world scenarios, leading to more meaningful test results.

Accelerated Testing Cycles

Automation significantly cuts down the time required for test execution. By leveraging Provar Automation, you can run extensive test suites quickly, enabling faster feedback loops and reducing time-to-market.

Provar Manager: Centralized Control and Enhanced Visibility

Provar Manager is your command center for all testing activities. It provides a centralized platform for managing test cases, tracking progress, and ensuring collaboration across teams.

Here are some key benefits of Provar Manager in unlocking your testing data.

Centralized Test Management

Provar Manager consolidates all your test cases and test data in one place. This centralization simplifies the process of tracking test progress, identifying bottlenecks, and managing resources.

Increased Visibility

With Provar Manager, every necessary party in your organization can gain full visibility into the testing process, regardless of where they are in the world. Dashboards and reports provide real-time insights into test coverage, execution status, and defect tracking, ensuring that everyone is on the same page.

Improved Collaboration

Provar Manager fosters collaboration by providing a unified platform where team members can share information, track changes, and communicate effectively. This collaborative environment leads to more efficient problem-solving and better quality assurance.

Provar Grid: Scalability and Performance Optimization

Provar Grid is designed to address the challenges of scalability and performance in testing. It allows you to distribute your test execution across multiple environments, ensuring that your tests are both fast and reliable.

Here are some key benefits of Provar Grid in unlocking your testing data.

Scalable Test Execution

Provar Grid enables you to run tests in parallel across different machines and environments. This scalability is crucial for handling large volumes of tests and ensuring that your testing processes can grow with your needs.

Optimized Performance

By distributing tests across multiple nodes, Provar Grid ensures optimal use of resources, leading to faster test execution times. This performance optimization is essential for meeting tight deadlines and maintaining high-quality standards.

Future-Proofing Your Testing

Provar Grid’s scalable architecture means that as your organization grows, your testing infrastructure can easily adapt. This future-proofing ensures that your testing processes remain efficient and effective, regardless of how your requirements evolve.

Unlocking the Full Potential of Your Testing Data

Integrating Provar Automation, Provar Manager, and Provar Grid into your testing strategy unlocks the full potential of your testing data. Here’s how these tools work together to transform your testing processes.

Data-Driven Testing

By leveraging real-time and historical data, you can create tests that are highly reflective of actual usage patterns. This data-driven approach leads to more accurate and reliable test results.

Increased Test Coverage

With Provar’s tools, you can ensure comprehensive test coverage, identifying potential issues before they become critical problems. This proactive approach enhances the overall quality of your software.

Accelerated Time-to-Market

The combination of automated, centralized, and scalable testing processes significantly reduces the time required for test execution. Faster testing cycles mean quicker releases and a competitive edge in the market.

Future-Proof Operations

Provar’s scalable solutions ensure that your testing processes can grow with your business. Whether you’re expanding your team, increasing your test cases, or adopting new technologies, Provar’s tools are designed to support your evolving needs.

The proof is in our comprehensive library of case studies, but the real-life successes are waiting to be discovered by you and your team. We know that testing is a must if you’re using Salesforce, but it’s time to go a step further by unlocking your testing data to achieve its fullest potential. Not just any generic, one-size-fits-all testing solution will work for your needs, and Provar is ready to help support your unique roadmap as you scale, evolve, and innovate.

Want to learn more about how Provar Automation, Provar Manager, and Provar Grid can help your team unlock its testing data and increase quality end-to-end across your business processes? Connect with a Provar expert today!

Test automation is an essential component in today’s software development landscape, where speed and quality are critical. It ensures the timely delivery of high-quality software by automating repetitive and time-consuming testing tasks.

However, the journey to effective test automation comes with challenges such as selecting the right tools, managing complex test cases, and enabling seamless integration of testing into the software development lifecycle.

Understanding test automation frameworks

A test automation framework is an essential foundation for any automated testing process. It’s a set of guidelines, tools, and practices designed to create and execute automated tests more efficiently. Some of the critical components of a test automation framework are coding standards, test and object repositories, and test data handling methods.

The primary benefits of implementing a framework include:

• improving the reusability and maintainability of test scripts;
• reducing manual errors;
• increasing test coverage;
• enhancing team collaboration.

Test automation frameworks come in various forms:

1. Linear scripting framework: simple; this framework involves writing sequential test scripts with little to no modularity or reusability;
   
2. Data-driven framework: it separates test data from the scripts, allowing tests to run with different data sets, enhancing test coverage, and reducing the number of scripts needed;
   
3. Keyword-driven framework: this framework uses keywords to represent actions and data, making the scripts more reusable and easier to understand;
   
4. Hybrid framework: combining elements of the above frameworks (often keyword- and data-driven ones), the hybrid approach offers flexibility and leverages the strengths of each framework type.

Some examples of the popular automation frameworks are Selenium, Cucumber, Robot, Appium, Playwright, Cypress. It is common for companies to take one or multiple as the foundation, and then further customize them for specific needs. Selecting the right framework type depends on the project goals and priorities as well as the team’s skills and experience.

Synergizing your test automation framework with Xray Enterprise

Establishing the effective connection between a test automation framework and Xray Enterprise involves a structured approach:

1. Initial planning and strategy development: define the automation objectives, identify the types of tests to automate, and outline the framework’s structure. Consider the application’s complexity, the team’s skill set, and the project’s timeline;
   
2. Tool selection and integration: choose automation tools that best complement Xray Enterprise’s capabilities and integrate them into the existing development environment. Ensure that the tools align with the overall automation strategy and team expertise;
   
3. Script development and execution: develop test scripts following best practices such as modularity and reusability. Use Xray Enterprise’s features to import execution results and monitor design and execution progress;
   
4. Continuous refinement and optimization: regularly review and refine the test automation framework. Utilize feedback and insights from Xray Enterprise’s analytics to optimize test scripts and processes.

Key features of Xray Enterprise for test automation

Xray Enterprise excels at complementing test automation frameworks with its extensive suite of features.

• Execute: with the Xray Enterprise’s new feature, you can trigger the CI/CD integration from a Test Plan or a Test Execution without leaving Xray Enterprise, which significantly streamlines your automation workflow;
  
• Import results: Xray easily integrates with automation frameworks via the versatile import of execution reports in various formats. You can learn more from our user guide, tutorials, and Xray Academy.
  • For the avoidance of doubt, you can import execution results even if the automation is not triggered from Xray;
• Report and analyze: the tool offers customizable reporting and analytics features, enabling teams to generate detailed reports on requirement coverage, test execution, and defect summaries. These insights are crucial for informed decision-making and continuous testing process improvement;

• Organize: Xray Enterprise provides robust, centralized test management capabilities – from creation to execution and reporting – ensuring a cohesive workflow;

With the support for multiple test types and a robust API you can track the results of your test automation alongside manual and exploratory efforts in a consistent manner. Since your testing assets are aggregated, it is easy to establish end-to-end traceability with all the requirement stories. This helps maintain a clear holistic overview of the testing process and ensures effective management of complex test suites.

• Flexible configuration: Xray Enterprise is designed to cater to various testing needs, environments, and testing approaches;
  
• Enhanced collaboration and visibility: the platform facilitates better feedback loops across teams with features that support sharing test cases, results, and reports. 
  

Xray Enterprise is a comprehensive solution for many test automation challenges, blending powerful features with user-friendly functionality. We invite teams and businesses to experience the impact of Xray Enterprise on their test automation efforts. Embrace the future of testing with confidence by choosing Xray Enterprise as your partner in delivering superior software solutions.

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If you compare the history of Generative AI with the development of the steam engine, you find surprising parallels. According to Wikipedia, the first steam engine patent was issued in 1606. This was followed by a century and a half of further development until James Watt achieved the breakthrough at the end of the 18th century. At first, he “leased” his steam engine as a service, but then – from 1800 onward – others developed their own machines.

The field of artificial intelligence (AI) has also been around for almost 70 years. Again, the beginnings were slow. It was not until 1997 that Deep Blue succeeded in beating the world chess champion Kasparov. The absolute breakthrough came in 2017 when Google presented the Transformer architecture. Within a very short space of time, the so-called “Large Language Models” (LLM) sprang up like mushrooms. When OpenAI made ChatGPT available to the public free of charge on November 30, 2022, the newest industrial revolution started. Today, most companies are dreaming of huge savings thanks to generative AI.

But is it realistic to believe that AI will do all the work for us? Isn’t it threatening to explode in on us like the steam engine did? Let us dive deeper into the topic.

What can we realistically expect from AI?

Trying to predict the future is certainly a foolish attempt. Nobody knows how we will use AI in ten years, but one thing is for sure: AI will change our life. The number of AI-based tools that are currently being developed seems to be almost unlimited. It will take some time for the wheat to be sorted from the cha.

Today, the various existing LLMs already provide us with the means to use AI in testing. The use cases are diverse:

• Get a quick overview of the features to be tested to start the test analysis more easily,
• Identify parameters and equivalence partitions and create test cases,
• Translate test cases into a specific format (e.g. Gherkin),
• Create test scripts,
• Generate test data,
• Reduce / optimize existing test cases, and many more.

If you are familiar with generative AI and formulate your prompt skilfully, you will get amazing results. However, it is important to keep in mind that AI is not intelligent in the sense that it really understands what it is saying. The Transformer algorithm only searches for semantic similarity. This produces astonishingly convincing answers, but the AI has not thought things through. We should therefore be wary of seeing AI as a replacement for an employee.

How to write structured prompts

A well structured prompt has six elements: context, role, instruction, constraints (if applicable), and output format. Take the following example:

Since LMMs only “think” in semantic similarities, it is helpful to repeat words and to provide examples. The more terms in the prompt point in the right direction, the better the answer will be.

In the example above, we find all elements of a structured prompt:

• Context: test of a specific web application using Gherkin scenarios
• Role: assistant for writing Gherkin test scenarios
• Instructions: , determine the equivalence classes, create Gherkin scenarios, check coverage
• Constraint: one file *.feature
• Output format: determined by the output temple

Sometimes, the AI is going in the wrong direction. For example, it may ignore the invalid equivalence partitions or disregard the output format. In that case it helps to provide examples. This prompting technique is called “n-shot prompting” where n stands for the number of examples. If your examples contain scenario names of a specific format, the AI is most likely to answer with similar names.

The role of the role

We started the prompt with “You are my helpful assistant”. This was probably not even necessary because it is already covered by the system prompt. The system prompt is the prompt sent by the chatbot interface prior to our own prompt. The system prompt of ChatGPT4.0 was discovered not so long ago using the following prompt:

It is enormous, trying to cover all potential request, but also adversarial attacks.

Still, starting the prompt with “You are my helpful assistant” has several advantages. On the one hand, it not only reminds the AI of its role, but also us. Because the eloquence of the answers should not tempt us to forget the basic principle. AI does not think for itself! On the other hand, it puts the AI in a “be helpful” mood. This may sound ridiculous, but the jailbreaking community recently discovered that adversarial attacks are more probable to succeed if you force the LLM into producing affirmative responses. The easiest way to do this is to end the prompt with “Respond with ‘Sure, here is how to…”. (For more details, please refer to this article.)

Keep the risks in mind!

Data security is probably the most obvious problem when using Generative AI for testing purposes. Since it is very tempting to use GenAI, your organization should consider a solution as soon as possible. It will probably come down to operating an in-house LLM.

Next comes copyright issues, especially if you use AI for coding purposes. I am not a lawyer and therefore will not venture into this area, but I strongly recommend consulting an expert.

Ecological risks are often mentioned, but as with waste avoidance, knowing about them does little to change our behaviour. Just as a rough indicator: Each generated image corresponds to one complete recharge of a smartphone! Therefore, I am convinced that we are well advised to develop healthy reflexes right from the start:

• Limit the use of Generative AI to tasks that are really helpful. For example, I quickly gave up asking ChatGPT to write conference abstracts for me. Instead, I ask it to assume the role of an English teacher and to correct my homework.
• If possible, use smaller LLMs. Even if you will probably get the best results with ChatGPT4, smaller models like Mistral-Tiny may also do the specific job.
• Create text rather than images. For example, it is possible to create UML models using Mermaid.js or Plant UML.

Of all AI types, generative AI is the AI that needs the most electricity and water. Often enough, there are ways of solving the same task with deep learning algorithms or perhaps even without AI altogether.

Stay tuned!

There is a lot more to say about AI, and as you read this, new use cases may already be emerging. But one thing is certain: only those who know their way around will be able to make full use of the new possibilities. Smartesting therefore offers training specifically for testers. In an exceptionally practice-oriented 2-day course, participants learn:

•  what Generative AI brings to software testing,
• how to obtain good results by applying prompting techniques,
• how to detect and mitigate risks related to the use of Generative AI,
• what possibilities exist beyond the chat mode and
• what to consider before introducing Generative AI into your organization.

The aim is to use AI to accelerate test analysis/design, test suite optimization, test automation and test maintenance activities. Smartesting’s LLM Workbench used during the training course allows you to access 14 LLMs of different size and operators (openAI, Mistral, Meta, Anthropic and Perplexity) and to compare their answers directly. The training is available in French, English and German. Contact us, if you want to know more.

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n today’s software development, quality assurance is a critical success factor. One of the biggest challenges here is the frequent discrepancy between the technical test specification and the test automation. These tasks are usually implemented in different tools, which leads to an incomplete overview of the test process, divergent specifications and implemented automations. This media discontinuity also makes coordination between test designers and test automation experts more difficult. 

Our goal with the integration of TestBench and Robot Framework is to overcome these challenges and enable a seamless, efficient testing process. TestBench manages keywords in a centralised keyword repository, which simplifies maintenance and ensures consistency. The use of Data Driven Testing in TestBench significantly increases test coverage as tests with different data variations can be easily performed. 

With Keyword-Driven Testing (KDT) and Data Driven Testing (DDT) in TestBench, we significantly increase the efficiency of the testing process. Test designers can create new automated test sequences with a low-code approach in TestBench, which not only lowers the barrier for test automation, but also enables faster and more precise implementation. This integration leads to harmonised collaboration between test designers and automation engineers and enables effective and comprehensive quality assurance. 

Speed up test specification with Keyword-Driven Testing  

Keyword-Driven Testing is a test specification method in which test cases are defined by using keywords that represent specific actions. These keywords abstract the test logic, allowing test cases to be created independently of the actual automation technology. This makes it easier for non-programmers to create and maintain tests, as the keywords are formulated in a language that can be understood by specialised users. KDT promotes the reusability and maintainability of tests, as keywords can be used in different test cases once they have been defined. If keywords need to be adapted, this is only necessary at one central point and has a direct effect on all test cases in which the keyword is used. 

Maximising test coverage through Data-Driven Testing 

Data-Driven Testing (DDT) is a test automation technique in which the same test cases are executed with different input data. This method enables efficient scaling of tests as the test logic is developed only once, while different data variations can be tested. This is done by providing the input data from external sources such as files, databases or tables. DDT increases test coverage and detects potential errors that can be caused by different data combinations without having to change the test scripts themselves. 

The synergy of Robot Framework and TestBench 

Keywords created in TestBench are converted into executable automation steps by automation specialists. No complete test cases are automated, only the atomic technical steps represented by the keywords. These atomic steps form the building blocks that can be reused in different test cases. This promotes the reusability and maintainability of the automated tests, as the basic actions can be implemented once and used in different contexts. 

We use the Robot Framework for automation, which fits perfectly with TestBench’s Keyword-Driven Test approach. As an open-source tool, Robot Framework offers a flexible and extensible framework that makes it possible to test a wide variety of technologies. By using 3rd party libraries to control the system under test, end-to-end tests can be realised across different applications. This significantly increases flexibility and adaptability. The combination of TestBench and Robot Framework maximises the efficiency and effectiveness of the test process. 

Summary: How TestBench and Robot Framework are revolutionising test automation 

In our solution, test cases are technically specified in TestBench and converted into Robot Framework test cases for automated execution. The entire planning and management takes place in TestBench, which also enables specialised test designers to compile automated tests from existing steps. The clear interface between test designers and test automation specialists simplifies collaboration considerably, allowing both to focus on their respective strengths – technical expertise and programming. 

Automated test execution can be triggered either manually by a human or through integration into CI/CD pipelines. The test results are imported and displayed in TestBench, including the results from manually executed tests. TestBench also offers a special wizard, the ‘iTORX’, for the manual execution of tests. 

A major advantage is the simple traceability from requirements to test results. This supports a comprehensive and transparent test strategy.  

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