AI-Powered Salesforce Testing, Shocking Agile Failure Rates, and More

What automation tool just announced a new AI-driven solution for Salesforce Test Automation?

Have you seen the study that shows a 268% Higher Failure Rate for Agile Software Projects?

And how do you scale with AI?

Find out in this edition of the Test Guild New Shows Newsletter for the week of June 9th. So, grab your favorite cup of coffee or tea, and let's do this.

Salesforce Test Automation by Testsigma is here!

Testsigma has launched its latest product, Salesforce Test Automation, built on its GenAI-powered, low-code platform. This new tool aims to simplify and expedite Salesforce testing, enabling users to automate tests up to ten times faster than traditional methods.

The platform utilizes a Natural Language Programming (NLP) engine, allowing tests to be written in plain English, which makes it accessible to users with varying technical backgrounds. This simplicity ensures that even those with limited technical knowledge can feel comfortable and confident in using the tool. Additionally, Testsigma leverages Salesforce metadata and APIs to create robust and adaptive test scripts, reducing maintenance and improving reliability.

The new Salesforce Test Automation tool offers significant benefits for testers, according to TestSigma, like

• Increased Efficiency: Automate tests up to 10 times faster, speeding up the testing process.
• Ease of Use: The NLP engine enables writing test cases in plain English, making it easier for non-technical team members to participate in test automation.
• Robust and Adaptive: Utilizing Salesforce metadata and APIs ensures tests are resilient to changes, reducing the need for frequent updates and maintenance. This reliability instills trust and confidence in the tool, making it a valuable asset for your testing needs.
• Scalability: Easily scale test automation efforts to match enterprise needs without additional complexity.

?I highly recommend you check it out now using the link https://testguild.me/testsigma.

Higher Failure Rates for Agile Software Projects?

I saw this recent study on Scott Aziz LinkedIn feed that reveals that software projects using Agile methodologies are 268% more likely to fail compared to those that do not. Conducted by Junade Ali and J.L. Partners, the research analyzed responses from 600 software engineers in the U.K. and the USA. It found that 65% of Agile projects failed to deliver on time and within budget, whereas only 10% of projects using a new Impact Engineering methodology failed.

The study highlights the importance of robust requirements engineering and psychological safety for successful project outcomes. Agile practices, such as working without a clear specification and making significant late-stage changes, were linked to higher failure rates. In contrast, clear project requirements and the ability to address problems promptly improved success rates significantly.

?If you're a tester, you might want to have your teams check out the benefits of the Impact Engineering methodology. This methodology emphasizes clear requirements and early problem-solving. According to this article, this approach reduces the risk of defects and late-stage changes, ensuring more stable and predictable project outcomes.

?But take a look yourself and let me know your thoughts in the comments below.

AI Shift-Left, Test Right

In the latest insights from EPAM, the growing trend of integrating AI into applications and processes is highlighted. A key focus is on the adoption of a multimodal test strategy to ensure the quality and reliability of AI systems. Multimodal AI, as exemplified by models like GPT -4 Turbo, involves the use of various forms of data such as images, video, text, speech, audio, or numerical data to make predictions and draw insights. This approach is proving to be a standard for many organizations.

?"Shift-left testing" emphasizes addressing quality early in the software development lifecycle to minimize late defects. For AI systems, it is crucial to test pre-release and conduct post-release production testing. This dual approach ensures ongoing quality and performance in dynamic environments.

?I think testers will benefit from the multimodal test strategy by having a more comprehensive framework to address different types of testing. This approach allows for early defect detection and continuous quality assurance post-release. Testers can leverage various data forms to enhance testing accuracy and effectiveness, leading to more robust and reliable AI systems.

Firefox to Deprecate CDP Support, Transition to WebDriver BiDi

I found this next news item on a LinkedIn Post from David Burns

Mozilla announced that starting with Firefox version 129, support for the Chrome DevTools Protocol (CDP) will be deprecated. Users are encouraged to migrate to the W3C WebDriver BiDi protocol, which offers a more comprehensive set of features. The deprecation period allows users to continue using CDP using Firefox ESR 128 or adjusting specific preferences in Firefox settings. CDP support will be fully removed by the end of 2024.

?The transition to WebDriver BiDi is part of Mozilla's efforts to enhance cross-browser testing and automation. WebDriver BiDi provides superior features compared to Firefox's experimental CDP implementation, including improved DOM updates, network interception, and better logging capabilities. This change aligns with the industry's preference for standardized, cross-browser automation protocols.

?If you haven't already you should check out the benefit from the enhanced capabilities of WebDriver BiDi, which offers more robust automation features. The transition promises improved interoperability and a more seamless testing experience across different browsers. Testers using tools like Puppeteer will experience minimal disruption, as Puppeteer already supports WebDriver BiDi. The move to WebDriver BiDi ensures testers can access modern, efficient tools for comprehensive web application testing and automation.

GitLab's Test Platform Team Validates AI Features

GitLab's Test Platform team has shared insights into their approach to validating AI features. The team uses a blend of automated and manual testing techniques to ensure the reliability and performance of new AI functionalities. This approach includes rigorous testing protocols, continuous integration practices, and synthetic data to simulate real-world scenarios.

?Through the strategic integration of AI into their testing processes, the team is able to swiftly identify and resolve potential issues. This not only enhances the overall quality and efficiency of their software development lifecycle but also underscores the significance of thorough validation processes in deploying AI-driven features.

Mistral AI Launches Codestral for Code Generation

Mistral AI has introduced Codestral, a new AI-driven tool to assist developers and testers in code generation tasks. Codestral supports over 80 programming languages, including popular ones like Python, Java, and C++, and more niche ones like Swift and Fortran. The model aims to streamline coding by completing functions, writing tests, and filling in partial code, reducing errors and saving time.

Devs and Testers can access Codestral through various platforms.

?It is currently available for download on HuggingFace under the Mistral AI Non-Production License for research and testing. Additionally, it integrates with popular development environments like VSCode and JetBrains via plugins from Continue.dev and Tabnine, enabling seamless code generation and interaction.

New Open-Source Driver to Enhances Appium and Flutter

New Open-Source Driver to Enhances Appium and Flutter Automation Sai Krishna announced the release of a new open-source tool called Appium Flutter Integration Driver that was designed to simplify the automation of applications built with Appium and Flutter. This tool integrates Appium's powerful automation features with Flutter's modern app-building capabilities, leveraging the Flutter integration test framework.

If you use Appium, this is great news because testers can now more easily automate testing for Flutter applications, reducing manual testing time. So, a must-check-out driver all mobile testers should be familiar with.

Snowflake Expands Capabilities for Developers

Snowflake announced new tools and innovations to enhance developers' capabilities working with enterprise-grade data pipelines, models, and AI-powered applications at its annual user conference. These updates include Snowflake Notebooks, Snowflake Trail, and various DevOps tools, all integrated within Snowflake's unified platform. The aim is to simplify development processes and accelerate the deployment of AI applications.

?Jeff Hollan, Head of Applications and Developer Platform at Snowflake, underscored the transformative potential of these innovations, which are poised to redefine the boundaries of what developers can accomplish with the AI Data Cloud. The new tools enable faster prototyping, development, and deployment of data products. Snowflake Notebooks, currently in public preview, offers an integrated development environment for Python, SQL, and Markdown, fostering enhanced productivity and collaboration.

AppDynamics Integrates Log Observer Connect with Splunk

I found this on Rebecca Clinard feed that AppDynamics has introduced Log Observer Connect, a new integration with Splunk that enhances log-based troubleshooting within Cisco's Full-Stack Observability solution. This integration allows users to centralize log collection in Splunk, streamlining the process of identifying and resolving application performance issues. The unified platform aims to reduce operational costs and improve the efficiency of root cause analysis.

?The integration comes with a user-friendly interface and deep linking capabilities, enabling seamless navigation between AppDynamics and Splunk. This feature, combined with single sign-on, simplifies troubleshooting by allowing users to access relevant logs directly from AppDynamics dashboards. The collaboration leverages Splunk’s log analytics to provide detailed insights into application performance issues, improving the mean-time-to-resolution (MTTR).

Scaling Businesses with AI and Hybrid Cloud

IBM's CEO, Arvind Krishna announced significant advancements in AI and hybrid cloud solutions at the IBM Think 2024 event. The company is open-sourcing its powerful AI models, the Granite family, on Hugging Face and GitHub under Apache 2.0 licenses. These models are designed to push the boundaries of AI capabilities. Additionally, IBM introduced a suite of new Watsonx assistants powered by Granite to enhance productivity and streamline operations. These include tools for coding, deploying custom AI assistants, and improving interaction with mainframe systems.

IBM also focuses on AI-powered automation, which is crucial for managing the estimated 1 billion apps to be developed in the next four years.

Their tools include Apptio for cost management, Instana for observability, Turbonomic for resource management, and the upcoming HashiCorp for cloud infrastructure automation. IBM Concert, currently in preview, will offer visibility across business applications.

IBM is actively expanding its AI ecosystem through strategic partnerships with major tech companies. These collaborations, with the likes of Adobe, AWS, Microsoft, Meta, Salesforce, and SAP, are set to foster a culture of innovation and drive the industry forward. A particularly noteworthy partnership is with the Saudi Data and Artificial Intelligence Authority, with the aim of launching the 'ALLaM' Arabic large language model on Watsonx.

That's a Wrap

So that's it for this Test Guild News Show Newsletter edition.

Make sure to subscribe to never miss another episode.

I'm Joe Colantonio, and my mission is to help you succeed in creating end-to-end full-stack DevSecOps automation awesomeness.

As always, test everything and keep the good. Cheers!

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