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Test Automation

🚀 Revolutionizing Web Test Automation with Nimbal’s Suite of Products 🌐

Dear readers,

At Nimbal, our relentless dedication to refining the landscape of web test automation has driven us to develop a suite of groundbreaking products. Our goal? To streamline and optimize the conventional process of web test automation, making it incredibly efficient.

Our process revolves around three pivotal stages:

  1. Nimbal Chrome Plugin: Seamlessly record user journeys and generate cucumber steps complete with xpaths, all through our intuitive Chrome plugin.
  2. Nimbal Web IDE: Transfer these meticulously crafted steps to our Web IDE, where you can execute them effortlessly.
  3. Nimbal Tree & AI Analysis: After pushing your test code to your Git repository and running it via your CI server, leverage Nimbal Tree’s advanced AI capabilities to perform comprehensive analysis.

We invite you to delve deeper into this innovative process by following the link below. Your feedback and insights are invaluable to us as they guide our continuous efforts to enhance and refine our products, ensuring they meet and exceed your expectations.

Web Test Automation Process using Nimbal Products

Thank you for being a part of Nimbal’s journey toward redefining web test automation. Your support and feedback are instrumental in shaping the future of our products.

Best regards,

Nimbal Team

Test Automation

Exploring possibilities of Generative AI in the Testing World

Over the past six months, we’ve been delving into the realm of Generative AI within Nimbal products. It’s been an exhilarating journey, albeit one filled with challenges as we strive to keep pace with the rapid advancements in AI technology, particularly those emerging from OpenAI.

We’re thrilled to report that our endeavors have borne fruit, with seamless integration of features such as test case generation and test failure summarization. These additions have significantly enhanced the value proposition for our esteemed customers, empowering them with greater efficiency and precision in their testing processes.

Yet, as technology continues to evolve at breakneck speed, so do our ambitions. With the advent of GPT-4o (Omni), we find ourselves at the threshold of a new frontier: voice-generated tests. Imagine a future where interacting with Nimbal Tree involves nothing more than articulating your test objectives aloud, eliminating the need for manual typing altogether.

But that’s not all. We’re also exploring the integration of voice functionality within our Test Cycles pages, enabling users to navigate and interact with the platform using natural language commands. This promises to revolutionize the user experience, making testing more intuitive and accessible than ever before.

Furthermore, we’re considering the incorporation of features that allow users to submit videos or textual descriptions of their screens, with AI algorithms generating tests based on the content provided. This represents a significant step towards automation and streamlining of the testing process, saving valuable time and resources for our users.

We invite you to join us on this exciting journey by signing up on our platform and sharing the news with your network. Your feedback and suggestions are invaluable to us, as we continuously strive to enhance our offerings and tailor them to meet your evolving needs.

To facilitate further engagement, we encourage you to schedule a meeting with us online, where you can share your ideas and insights directly with the Nimbal team. Together, we can shape the future of testing and usher in a new era of innovation and collaboration.

Thank you once again for your continued support and patronage. We look forward to embarking on this next chapter with you, as we work towards building a smarter, more efficient testing ecosystem.

Warm regards,

The Nimbal Team

Test Automation

Java vs Typescript for Test Automation

A couple of weeks back, our team delved into an intriguing investigation concerning the prevalent languages employed by companies for crafting test automation solutions. Among the top contenders in our exploration were Java and TypeScript.

Java stands as a stalwart in the realm of back-end development and corporate environments, owing to its widespread adoption in legacy systems. Technologies like Spring Boot exemplify Java’s stronghold, remaining a preferred choice for constructing back-end REST APIs in enterprise settings. Furthermore, Java boasts a rich ecosystem of open-source testing tools, including stalwarts like Selenium, JMeter, and ZAP. The emergence of newer tools like Playwright has further solidified Java’s position by providing robust support and libraries tailored for the language.

In contrast, TypeScript, an object-oriented variant of JavaScript, has surged in popularity within the full-stack developer community. Leveraging the familiar syntax of JavaScript while adding static typing and other enhancements, TypeScript has garnered significant traction in modern web development.

Without further ado, here are the insights gleaned from our recent poll:

Here’s our distilled conclusion on language selection for test automation:

1. Starting from Scratch without Mobile Test Automation, Performance, and Security Concerns:

If you’re embarking on a new project and prioritize simplicity and versatility over mobile test automation, performance, and security, sticking to TypeScript coupled with Playwright could be your optimal choice.

2. Existing Java-based Frameworks:

For those already entrenched in Java-based frameworks, especially with established infrastructures and workflows, there’s little long-term value in migrating. Stick with what works for you.

3. Transition from Cypress with TypeScript to Playwright:

If you’re currently using Cypress with TypeScript, consider transitioning to Playwright promptly. This move could potentially streamline your test automation efforts without unnecessary delays.

4. Consideration for Playwright with TypeScript and Cucumber Layer:

Planning to utilize Playwright with TypeScript? Incorporating a cucumber layer can enhance test orchestration, especially as support for mobile test automation and other technologies matures.

5. Transition from Other Languages to TypeScript Frameworks:

For teams utilizing languages like C# or Python, contemplating a switch to TypeScript-based frameworks could offer greater flexibility and alignment with modern development practices.

6. Upskilling with TypeScript:

If TypeScript is on your radar, investing in team up skilling through resources like TypeScript playgrounds can accelerate the learning curve and facilitate smoother adoption. You can try it here

7. AI-supported Code Writing for Test Automation:

Explore AI-driven solutions for test code generation to streamline your testing process. Nimbal offers a platform that generates test code in various languages, including Java and TypeScript. Sign up and get in touch to explore how AI can augment your test automation efforts at

By carefully considering these recommendations, you can tailor your language selection to best align with your project’s requirements and team capabilities.

Test Automation

Publishing a docker container product on Azure marketplace to execute a practical Go to market strategy

Dear Readers,

Last week we went live on Azure marketplace with Nimbal Web ide product. This launch was the part of our Go to market strategy as this opens up whole Azure cloud market for us. Below is the link to try the product and please feel free to share it with your Azure cloud team to try it at just 20 cents an hour rate.

We would like to share our steps with you.

Launching a container product on the Azure Marketplace involves several steps. Here’s a general outline of the process:

1. Prepare your Container Image:

– Ensure your application is packaged into a container image (e.g., Docker image).

– The container image should include all necessary dependencies and configurations for your application to run.

2. Create an Azure Container Registry (ACR):

– If you haven’t already, create an Azure Container Registry where you’ll store your container images. You can create one through the Azure portal or using Azure CLI.

3. Publish your Container Image to ACR:

– Push your container image to your Azure Container Registry.

– You can use the Azure CLI, Docker CLI, or Azure portal to push your image to ACR.

4. Create an Azure Resource Manager (ARM) Template:

– Create an ARM template that defines the resources required for deploying your containerized application on Azure. This includes resources like Azure Container Instances (ACI), Azure Kubernetes Service (AKS), or Azure Web App for Containers.

– Make sure to include parameters in the template to allow users to customize their deployment (e.g., container image, environment variables).

5. Test your ARM Template:

– Validate your ARM template to ensure it deploys your application correctly.

– You can use the Azure CLI or Azure portal to deploy and test your ARM template.

6. Publish your Offering on Azure Marketplace:

– Go to the Azure Marketplace Publisher Portal and sign in with your Azure account.

– Create a new offer and fill in the necessary details, such as the offer name, description, pricing, support details, etc.

– Upload your ARM template and provide any additional documentation or resources for users.

– Choose the appropriate categories and regions for your offering.

7. Submit for Publication:

– Review your listing and ensure all details are correct.

– Submit your offering for publication on the Azure Marketplace.

– Azure Marketplace team will review your submission, and once approved, your offering will be published on the Marketplace.

8. Manage and Support your Offering:

– Once your offering is published, you’ll need to manage and support it.

– Monitor usage, provide customer support, and update your offering as needed.

9. Promote your Offering:

– Promote your offering through various channels to increase visibility and attract customers.

Keep in mind that this is a high-level overview, and the specific steps may vary depending on your application and requirements. Make sure to refer to the Azure documentation and guidelines for detailed instructions on each step.

If you would like to try our products without spinning them in your cloud, please sign up at the free SaaS platform here

Test Automation

Ideas for Testing Large Language Models

Dear Readers,

Let us discover some ideas for testing large language models to ensure accurate and reliable results.

Understanding the importance of testing language models

Testing language models is crucial to ensure their accuracy and reliability. Language models are designed to generate human-like text, and it is important to evaluate their performance to determine their effectiveness. By testing language models, we can identify potential issues such as inaccuracies, biases, and limitations, and work towards improving their capabilities.

Language models are used in various applications such as natural language processing, chatbots, and machine translation. These models are trained on large amounts of data, and testing helps in understanding their behavior and identifying any shortcomings. Testing also allows us to assess the model’s ability to understand context, generate coherent responses, and provide accurate information.

Moreover, testing language models helps in validating their performance against different use cases and scenarios. It allows us to measure the model’s accuracy, fluency, and ability to handle diverse inputs. By understanding the importance of testing language models, we can ensure that they meet the desired standards and deliver reliable and trustworthy results.

Choosing diverse and representative test data

When testing large language models, it is important to select a diverse and representative set of test data. This ensures that the model is exposed to a wide range of inputs and can handle different contexts and scenarios. By including diverse data, we can evaluate the model’s performance across various domains, topics, and languages.

Representative test data should reflect the real-world usage of the language model. It should include different types of text, such as formal and informal language, technical and non-technical content, and varying sentence structures. By incorporating a variety of test data, we can assess the model’s ability to understand and generate text in different styles and contexts.

Choosing diverse and representative test data is essential for identifying potential biases and limitations of the language model. It allows us to evaluate its performance across different demographic groups, cultures, and perspectives. By considering a wide range of inputs, we can ensure that the model is fair and unbiased in its responses.

Evaluating performance metrics

To effectively test large language models, it is important to define and evaluate performance metrics. Performance metrics provide a quantitative measure of the model’s performance and help in assessing its capabilities. Common performance metrics for language models include accuracy, fluency, perplexity, and response relevancy.

Accuracy measures how well the model generates correct and coherent responses. It evaluates the model’s ability to understand the input and provide relevant and accurate information. Fluency assesses the grammatical correctness and coherence of the generated text. Perplexity measures the model’s ability to predict the next word or sequence of words based on the context.

Response relevancy evaluates the relevance and appropriateness of the model’s generated responses. It ensures that the model produces meaningful and contextually appropriate output. By evaluating these performance metrics, we can assess the strengths and weaknesses of the language model and identify areas for improvement.

Testing for bias and fairness

Testing language models for bias and fairness is crucial to ensure equitable and unbiased results. Language models can inadvertently reflect biases present in the training data, leading to unfair or discriminatory outputs. It is important to identify and address these biases to ensure the model’s fairness and inclusivity.

To test for bias, it is essential to evaluate the model’s responses across different demographic groups and sensitive topics. This helps in identifying any disparities or inconsistencies in the generated output. Testing for fairness involves assessing the distribution of responses and ensuring that the model provides equitable results regardless of demographic factors.

Various techniques can be employed to test for bias and fairness, such as measuring demographic parity, equalized odds, and conditional independence. By conducting comprehensive tests, we can identify and mitigate biases, ensuring that the language model’s outputs are fair, unbiased, and inclusive.

Iterative testing and continuous improvement

Testing large language models should be an iterative process, allowing for continuous improvement. As language models evolve and new data becomes available, regular testing helps in identifying areas for enhancement and refinement.

By conducting iterative tests, we can track the model’s progress over time and evaluate its performance against previous versions. This allows us to measure the impact of updates and improvements, ensuring that the model consistently delivers accurate and reliable results.

Iterative testing also helps in identifying new challenges and limitations that arise as the model is exposed to different inputs and scenarios. By continuously testing and gathering feedback, we can address these challenges and refine the model’s capabilities.

Continuous improvement is achieved through a feedback loop between testing and model development. Test results provide valuable insights into the model’s strengths and weaknesses, guiding further enhancements and optimizations.

Overall, iterative testing and continuous improvement are essential for ensuring the long-term effectiveness and reliability of large language models.

Please try using our large language model to generate tests and summarise failures at Nimbal Testing Platform and share your comments.

Test Automation

Improving Quality Engineering in Securities Companies

The Importance of Quality Engineering in Securities Companies

Quality engineering plays a crucial role in securities companies, as it ensures that the software and systems used for trading and investment activities are reliable, secure, and meet regulatory requirements. In an industry where accuracy and timeliness are of utmost importance, quality engineering helps to minimize the risk of errors, system failures, and security breaches that could have significant financial consequences.

By implementing robust quality engineering practices, securities companies can build trust with their clients and stakeholders, demonstrating their commitment to delivering high-quality services and products. This is especially critical in an increasingly competitive market where investors have more options to choose from. A strong reputation for quality can set a securities company apart from its competitors and attract new clients.

Understanding the Challenges Faced by Securities Companies

Securities companies face several unique challenges when it comes to quality engineering. One of the main challenges is the complexity of the systems and software used for trading and investment activities. These systems often involve multiple components, integration points, and dependencies, making it challenging to ensure the overall quality of the system.

Moreover, securities companies operate in a highly regulated environment, where compliance with regulatory requirements is essential. Quality engineering processes need to take into account these regulations and ensure that the systems and software comply with all applicable rules and standards.

Another challenge is the need for continuous testing and monitoring. Securities companies deal with large volumes of data and transactions, and any errors or malfunctions can have severe consequences. Therefore, quality engineering practices should include comprehensive testing and monitoring strategies to detect and fix issues before they impact the business.

Implementing Effective Quality Engineering Processes

To improve quality engineering in securities companies, it is essential to implement effective processes that address the specific challenges of the industry. This starts with establishing a clear quality engineering framework that defines the roles, responsibilities, and processes for ensuring quality throughout the development and deployment lifecycle.

Furthermore, securities companies should invest in building a skilled and knowledgeable quality engineering team. This team should have expertise in areas such as software testing, security testing, performance testing, and regulatory compliance. By having a dedicated team focused on quality, securities companies can ensure that the necessary expertise is available to address the unique challenges of the industry.

In addition, implementing a risk-based approach to quality engineering can help prioritize testing efforts and focus resources on the most critical areas. This involves identifying and assessing the potential risks associated with the systems and software used in securities companies and tailoring the testing activities accordingly.

Regular audits and reviews of the quality engineering processes can also help identify areas for improvement and ensure that the practices are aligned with industry best practices and regulatory requirements.

Utilizing Automation and Tools for Improved Quality

Automation and tools play a significant role in improving quality engineering in securities companies. By automating repetitive and time-consuming tasks, such as regression testing and performance testing, securities companies can increase efficiency and reduce the risk of human errors.

Test automation frameworks can be used to streamline the testing process and ensure consistent and reliable results. These frameworks allow for the creation of automated test cases, which can be executed repeatedly to validate the functionality, performance, and security of the systems and software.

Furthermore, the use of specialized tools can help securities companies identify and fix potential vulnerabilities and security issues. These tools can perform security scans, penetration testing, and code analysis, providing valuable insights into the security posture of the systems and software.

By leveraging automation and tools, securities companies can enhance their quality engineering practices, reduce time-to-market, and improve the overall reliability and security of their systems and software.

Continuous Improvement and Monitoring for Long-Term Success

Continuous improvement and monitoring are crucial for long-term success in quality engineering for securities companies. Quality engineering processes should be continuously evaluated and optimized to ensure they remain effective and aligned with the evolving needs of the industry.

Regular monitoring of the systems and software is essential to detect any performance or security issues proactively. This can involve the use of monitoring tools and technologies that provide real-time insights into the health and performance of the systems. By monitoring key metrics and indicators, securities companies can identify potential issues before they impact the business and take timely corrective actions.

Furthermore, feedback loops should be established with clients and stakeholders to gather insights and feedback on the quality of the services and products. This feedback can be used to drive continuous improvement initiatives and address any identified gaps or areas for enhancement.

By embracing a culture of continuous improvement and monitoring, securities companies can ensure that their quality engineering practices remain effective and enable them to deliver high-quality services and products in a dynamic and demanding market.

At Nimbal we have worked with India’s top Securities companies to solve their complex quality engineering problems. If you are working in this space, we would like to hear from you. Please leave a comment and we will be in touch.

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