App Quality Copilot

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App Quality Copilot provides fully AI-automated QA & Testing, offering true AI-powered testing using simple human language, aimed at delivering absolute confidence in app functionality with minimal effort.

How App Quality Copilot can help you:

Enables true AI-powered testing using simple human language.
Ensures absolute confidence that your app is working as intended.
Provides visibility into problems traditional automated testing can't catch, like localization issues and UI/UX problems.
Guarantees consistency and reproducibility by locking tests.

Why choose App Quality Copilot: Key features

Simple, human language interaction for complex QA processes.
Out-of-the-box app quality overview with minimal team effort required.
Generative AI-driven detection of a wide range of testing issues.
Capability to "lock" tests for consistent test execution.

Who should choose App Quality Copilot:

Development teams seeking to minimize the time and resources spent on QA and testing.
Organizations aiming for high confidence in their app's functionality without extensive manual oversight.
Teams requiring visibility into and solutions for problems beyond the reach of traditional automated testing methods.

About App Quality Copilot

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https://www.mobile.dev/app-quality-copilot

Release Date

March 2024

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Talking With Copilot Voice Is Better Than Typing: Try These 4 Functions

Quick Links Copilot Voice Is Easier Than Typing Try These Actions With Copilot Voice Use as a Writing Companion Get Food and Travel Recommendations Learn New Topics and Languages Brainstorm Ideas Microsoft's chatbot, Copilot, is now available as a conversational AI assistant, opening a new dimension of human-AI interaction. Copilot Voice can help you streamline your tasks and activities and spark creativity through the natural cadence of conversation. Copilot Voice is now available to everyone -- no subscription required. You can use Copilot Voice on the Web - copilot.microsoft.com - or on the Copilot app on Android and iOS. Copilot Voice Is Easier Than Typing Copilot Voice is an intuitive and interactive way to engage with the AI-powered chatbot designed to answer questions and provide real-time assistance across various domains. With sophisticated speech recognition and natural language processing (NLP) capabilities, Copilot Voice can understand complex commands, provide nuanced responses, and adapt to different user preferences. With its ability to listen, understand, and respond, Copilot Voice offers a distinct advantage over traditional typing. Typing requires effort and accuracy, often slowing down workflows. Speaking, on the other hand, is a more natural and faster way of communicating. Of course, it's also a game changer for individuals with mobility challenges or those who struggle with typing, making AI experiences inclusive for people with diverse needs. By replacing the need for constant keyboard interaction, Copilot Voice ensures seamless multitasking to enhance your productivity. Whether you're drafting an email, researching a topic, or brainstorming creative ideas, Copilot Voice simplifies the process by letting you speak your thoughts. Its ability to respond intelligently makes interactions fluid, helping users stay focused on their goals rather than the mechanics of typing. And there's a bonus -- reduced screen time! By minimizing the need to stare at a screen while typing, Copilot Voice helps reduce eye strain and fatigue, supporting healthier tech habits. Copilot Voice offers a transcript of the exchange once you end the session, which is how I took the following screenshots. Try These Actions With Copilot Voice Now that we understand the benefits of Copilot Voice let's dive into its practical applications. Here are a few ways I like to use Copilot Voice. Use as a Writing Companion Writing can be a time-consuming and sometimes intimidating task, but Copilot Voice acts as an efficient writing partner. Like an executive assistant, you can simply dictate your thoughts for Copilot Voice to draft emails and documents instead of manually typing them out. You can speak out exact lines as well, and Copilot Voice will transcribe with remarkable accuracy -- of course, it can also suggest edits or rephrasing for clarity and tone. It can also be a great creative brainstorming tool. Dictate your ideas on the go and let Copilot Voice offer coherent drafts. Copilot can also suggest ways to develop the content piece further. For my example, I asked Copilot Voice to help me with an email, and instead of a properly formed prompt with the whole intent, Copilot and I had a little back and forth to arrive at a workable draft of an email. Get Food and Travel Recommendations From dining decisions to planning your next vacation, Copilot Voice simplifies the process. You can ask for nearby restaurants that match your preferences, and Copilot Voice can provide tailored recommendations. Based on your past preferences or dietary restrictions, Copilot Voice can narrow down options to ensure they align with your tastes. If you're exploring a new city or planning a weekend getaway, Copilot Voice can suggest destinations, activities, and help you with food and travel recommendations. I've been to the Museum of Modern Art (MoMA) in San Francisco, so I checked with Copilot Voice to see if it still made sense to visit MoMA in New York City on my upcoming trip. Note that in the screenshot, as soon as I mentioned museums, Copilot Voice suggested the Met (Metropolitan Museum of Art) before I interjected the response to ask about MoMA -- this interactivity is the key incentive of voice over text-based chatbots. Learn New Topics and Languages Learning is also more engaging and effective with Copilot Voice as your guide. Have a question or need a quick overview of a subject? Copilot Voice can deliver concise explanations or dive deep into complex topics based on your needs. It can create a learning schedule or recommend resources like articles, videos, or podcasts tailored to your interests, learning goals, and pace of learning. You can even have Copilot Voice help you practice what you've learned. If you are interested in learning a new language, you can use Copilot Voice to help you. It can engage in conversations, teach pronunciation, and introduce vocabulary in context. It's a great way to pick up quick conversational phrases if you're headed abroad for a trip. Brainstorm Ideas Whether you're solving a problem or generating creative concepts, Copilot Voice excels at brainstorming, too. You can speak your initial thoughts, and Copilot Voice can build on them, suggest alternatives, or provide related insights. You can also ask Copilot Voice to organize your ideas into a structured format, making it easier to visualize and expand upon them. For example, you can ask it to generate a list of possible solutions, help you evaluate the pros and cons of each solution, or come up with a creative way to approach any problem. As technology continues to evolve, tools like Copilot Voice demonstrate how AI can create a more accessible, intuitive, and efficient digital experience. Talking with Copilot Voice is undeniably better than typing for many tasks. Its ability to interpret natural language, provide thoughtful responses, and enhance productivity makes it an invaluable tool for both personal and professional use.

MakeUseOf

Sat, 21 Dec, 6:03 PM UTC

Why You Need to Shift Left With Mobile Testing - DZone

I feel like there's always been a love-hate relationship with the concept of testing. Without a doubt, the benefits of testing whatever you are building help avoid customers reporting those same discoveries. That's the love part of the relationship. The hate part is when project timelines cause testing to become a lower priority...often to the point where it becomes a backlog wishlist item that rarely surfaces in a current sprint. This almost guarantees customers will contact you with unexpected outcomes. As software development lifecycles (SDLCs) have matured, the testing has become easier allowing developers to create unit tests that fully cover the aspect being validated. The use of ChatGPT or GitHub Co-Pilot has matured to the point where such unit tests can be auto-generated. Continuous integration (CI) tooling solutions have improved to enforce high levels of code coverage before any pull request (PR) can be merged. Doing so allows teams to shift left with their development -- forcing problems to be addressed during the development phase and cutting down the cost of features along the way. This approach has worked great for traditional APIs and web frameworks, but testing mobile applications often requires teams to perform manual testing -- executing from a published list of steps across as many different device types as can be managed. I wanted to see if I could identify a way mobile development and testing could employ the shift-left concept. At a high level, the mobile application space must have the ability to test features and functionality in the same way APIs and web frameworks are testing today. This means getting out of the business of performing tests manually using an inventory of physical devices or emulators. This ideal state of mobile testing would be UI-driven to avoid writing cryptic tests focused on user activity. This approach could expand the tooling to internal consumers or product owners who want to validate their vision as a reality. Just like traditional unit or integration tests, the ability to introduce modules can validate small aspects of the mobile application and be used as building blocks for larger flows. In doing this, teams will be able to stay "dry" (don't repeat yourself) and avoid duplicate work. Finally, these tests will need to be able to become part of the CI/CD pipeline, despite being driven by a graphical UI. In this ideal state, mobile software engineers can effectively shift left for their mobile development and testing. "Shift-left testing is an approach to software testing and system testing in which testing is performed earlier in the lifecycle (i.e. moved left on the project timeline). It is the first half of the maxim 'test early and often'. It was coined by Larry Smith in 2001." Simply embracing the shift left allows defects to be identified during the development phase. This is important since the issue can be addressed while the feature is fresh in the mind of the engineer(s) focused on the source. Here are some of the benefits of adopting shift left: Ultimately, the shift left concept will lead to a competitive advantage when solutions reach consumers that are validated and working as expected. Last year, I explored the use of Tricentis Testim in my "Improving Customer-Facing App Quality Using Tricentis Testim" article. I was impressed at how easy it was to validate my Magic 8 Ball solution using a GO-based RESTful API and Vue.js web-based client. I wanted to see if Tricentis had a solution that would allow teams to shift left for mobile testing. Turns out, they have a product called Tosca. The Tosca product provides codeless test generation, allowing the creation of small modules that can be reused and automated. These modules can be thought of as Lego blocks that can be connected where needed due to the employment of a standardized contract between them. Tosca takes things a step closer to more traditional development lifecycles by providing the ability to leverage AI to help generate mobile tests for your features. Tosca also leverages the power of the open-source Appium project without a heavy learning curve via the Tricentis Mobile Agent. This allows all the power provided in my prior article to be included in the shift left journey with mobile development. As a result, Tosca allows testing of native, hybrid, and web mobile apps across real iPhone, Android, mobile phone, and tablet devices without maintaining and possessing these devices. Just like the Testim product, the Tosca solution provides the ability for tests to be executed as part of CI/CD pipelines, allowing for the enforcement of shift left adoption. You can use Tosca to test on an iOS or Android phone directly, as well as through emulators or simulators available through an IDE like Android Studio. Using Tosca, you can scan your application and have it create test cases: Once Tosca has created the test cases, you can also create test cases of your own. One of the advantages of Tosca is that it allows you to write tests without having to write code. This is made possible through its library of modules that can simulate almost every action, including opening a browser or filling out a form. In this example, we've used three modules for our Tosca mobile test case. We test: Notice that all we had to do was provide sample inputs (in the screenshot, we're doing so for step 3 highlighted above). Once the test is complete, you'll receive a diagnostics report on Tosca. By leveraging a product like Tosca, software engineers focused on mobile development can put their teams at a competitive advantage by employing shit left for mobile testing: My readers may recall that I have been focused on the following mission statement, which I feel can apply to any IT professional: "Focus your time on delivering features/functionality that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else." -- J. Vester To reach peak productivity, software engineers focused on mobile development need to adopt the shift left for mobile testing. However, the ideal choice should consider any associated learning curve and supportability when searching for solutions. The Tosca product adheres to my personal mission statement by allowing teams to reach the shift-left state without additional source code to support and maintain. The product also allows non-engineers to participate in the test development, giving teams an advantage in ensuring designs match expectations. I've personally employed the shift left approach for several years and appreciate the experience every time a defect is avoided by simply following the process. It's time for mobile development to employ the concept of shift left.

dzone.com

Fri, 12 Jul, 8:01 PM UTC

AI vs. Human: Who Detects Bugs Better? - DZone

Software testing processes are evolving with the interaction of AI technologies. The advancement in AI tech has helped organizations witness improvements in their testing capabilities. Robust testing enables the release of high-quality and reliable products and services, and bug detection assists. Earlier bug detection and reporting processes required intensive manual labor, which was time-consuming and prone to human errors. Gone are the days when it took hours to review a code only to find that the bugs went unnoticed in the testing process. AI-powered bug detection has helped organizations transform this process by utilizing technologies that make it easy to understand the nature of bugs. It helps speed up identifying and resolving bugs, which is crucial to maintaining the project's integrity. In the early days, the bug detection was done manually. The testing professionals developed tests and did the code reviews. The following points explain the same. Testers would examine the entire software using different use cases and testing scenarios to uncover errors and unexpected behaviors. It was a labor-intensive process. In the team of developers, the code was cross-examined to find potential bugs, logical flaws, and areas prone to errors. The collaboration helped to find mistakes but wasn't exclusive. Earlier, the bug-detection tools offered minimal automation for the testing process. Testers mainly relied on manual data entry and repetitive tasks, making the entire process prone to human errors. The bug detection began only after the code was written, making the process reactive. Sometimes, the bugs remained unidentified even after the product's release. Bug detection has evolved over the past years. Here are some recent advancements. AI models are becoming increasingly tools for bug detection. They play a crucial role in assisting the developers in finding and fixing bugs early. Three approaches are commonly adopted, as listed below. The approach needs massive labeled data, including code snippets with identified bugs. Using this approach, the AI model learns by analyzing the existing examples and extracts of patterns that differentiate faulty code from clean code. Since it relies on the quality of labeled data, the model may need help identifying novel bugs not encountered during training. Unlike supervised learning, the models using this approach analyze the code without knowledge of specific bugs. The model identifies deviations from the norm, making it suitable for uncovering bugs that have yet to be encountered, aka outliners (missed by supervised learning). One of its limitations includes generating several false positives, which requires manual verification of flagged issues. The approach needs massive amounts of data to train complex AI algorithms to uncover complex bug patterns. It uses artificial neural networks to identify intricate relationships within code with high accuracy. One of the drawbacks of implementing this approach is that it can be expensive to train and require significant expertise for model development. Real-time bug detection is the basis of AI-powered software development that identifies the issues as they are written or modified. It works using different methodologies; a few are pointed out below. Static code analysis works like an automated code reviewer. It continuously examines the code for errors and vulnerabilities as you write the code. The code is first parsed, including breaking it into a structured format so the static analysis tool can better comprehend it. The tool then checks for syntax errors and runs semantic checks. This is followed by pattern matching (against the database of known coding patterns) while the real-time feedback remains constant. Machine learning offers an advanced way of generating real-time bug detection algorithms. The technique can identify complex patterns and potential dataset issues that static code analysis can miss. Machine learning models are trained on massive datasets of codes containing bugs. This training helps recognize patterns and identify relationships between code elements that indicate bugs. These models are retrained as the code evolves, which helps detect new bugs over time. A real-time bug detection system uses IDEs to ensure a smooth workflow. This includes using IDE plugins offered mainly by AI bug detection tools like VS Code and Eclipse. These plugins monitor the code in the background as you write while interacting with the bug detection engine. The engine examines the code, sends real-time feedback highlighting potential bugs, and offers suggestions. The integration empowers developers to resolve issues faster without switching between different tools. NLP helps bridge the gap between human intent and code functionality. It analyzes code comments and documentation, ensuring alignment with actual code implementation. NLP can assist in analyzing bug reports written by developers and testers, which can further help classify bugs and prioritize them. AI-powered bug detection is changing how software developers work to identify potential issues in the code. The tools that offer such capabilities are often packed with features like static code analysis, natural language processing, machine learning models, and integration with IDEs. The following sections highlight some of these tools and technologies. Several tools offer machine learning bug detection. A few of the popular ones are listed below. The tool was Founded by Veselin Raychev, advisor Martin Vechev and Boris Paskalev. They all have a lot of experience in machine learning and AI research, which has helped them build a system that quickly comprehends the intent of code. Some of the core features and functionalities offered by DeepCode.AI include focusing on anomaly detection, smooth integration with development environments, and AI-assisted bug detection leveraging language learning models. It utilizes AI to enhance test automation capabilities and provide real-time bug detection services. However, it can not directly assist with bug detection and uses dynamic locators, self-healing tests, and faster root cause analysis to deliver bug detection services. The toolset is dedicated to developers and offers AI-powered bug detection along with other functionalities to solve engineering problems. CodeClimate features static code analysis for identifying syntax errors and coding rule violations. Employs machine learning models trained with known bugs to recognize complex patterns and identify bug issues. It utilizes ML models to improve its static code analysis capabilities for bug detection. Developers fix the identified issues, and the codebase grows, allowing the SonarQube ML models to retrain. Additionally, the tool focuses on building secure code by singling out security vulnerabilities. The following case studies explain how big brands improved their product and services for their customers by using AI-powered bug detection. It used an incredible tool, SapFix, which is nothing less than a game changer for its software products and services. Although its use is not publicly promoted, its presence can be read in different documents explaining the technologies used by Facebook. The figure below describes how it works. The tool represents an AI model that augments human efforts in debugging so the developers can focus on more crucial tasks. It helped Facebook eliminate product vulnerabilities faster and more efficiently. It uses the Aqua tool, which has helped this tech giant reduce 25% of bugs in its software applications. The tool integrates with the Google Cloud Platform to offer real-time bug detection and protection for containerized apps running on Google Kubernetes Engine (GKE). The following points highlight its working. Examining and analyzing container traffic becomes easy as the tool seamlessly integrates with GKE. Aqua scans images for known issues in base images or dependencies while working with threat intelligence feeds and ML algorithms. This allows Aqua to stay updated on the latest security issues. The tool effectively monitors container behavior at runtime to identify issues and potential security vulnerabilities. Aqua enforces policies to ensure container images meet security standards before deployment, which helps eliminate the bugs before entering the prod environment. Using AI-powered bug detection has its challenges. The following points shed light on a few such challenges and the solutions you can choose. Bug detection is a challenging process when done manually; testers must create test scripts that can identify flaws in the code. The automation that comes with AI-powered bug detection tools generates algorithms that can analyze code, test cases, and historic bug data. These tools provide faster and more efficient identification of patterns that indicate potential bugs. The traditional bug detection methods often generate false positives, where non-issues are flagged as bugs. The methods were also responsible for false negatives, where the tests missed potential edge cases or complex bugs. AI-powered bug detection transformed the software development process by analyzing vast amounts of data to identify patterns, reduce false alarms, and identify hidden bugs that traditional methods missed. With the growing complexities, the system and processes often scale and traditional bug detection methods in such situations. Such methods can only handle a small amount of data. The AI algorithms can be distributed around multiple machines to handle large datasets effectively. It offers parallelism by breaking the analyzing tasks into small parts and reduces investment costs (cloud-based infrastructure). The development and QA teams may need to report channels or collaboration tools. This can eventually lead to misunderstanding and project delays. AI-powered bug detection offers several long-term benefits to QA professionals, some of which are listed below. Combining the two bug detection methods is the best as it helps get both benefits. The two methods differ in several ways, and some points shared in the table highlight the same. Traditionally, bug detection was a manual process that required manual code reviews and testing and was prone to errors. AI-powred bug detection transformed the software development process. It accelerates development and testing processes while improving product quality and reducing costs. It empowers developers to write cleaner, more reliable codes with fewer security issues. The technology continuously evolves, and we can expect more sophisticated bug detection capabilities.

dzone.com

Tue, 23 Jul, 2:01 PM UTC

QA Wolf secures $36M to grow its app QA-testing suite | TechCrunch

Quality assurance in the app development world is a necessary, but often resource-draining, undertaking. According to Statista, 23% of companies' annual IT budgets are allocated to in-house or third-party contracted QA and testing. The stakes are high. In a survey by QA software-testing company QualiTest Group, 88% of app users said that they'd abandon apps with bugs and glitches. Jon Perl, Laura Cressman and Scott Wilson -- software developers and marketers by trade -- had the idea to create a platform, QA Wolf, to make QA for mobile and web apps easier by automating aspects of creating and running tests. There's no shortage of solutions to automate QA testing. As Julie Bort wrote in her coverage of code-testing startup Nova AI, it seems that TechCrunch reports on a new one practically every week. So what makes QA Wolf different? Well, for one, QA Wolf pivoted. When the company launched in 2019, it offered QA test orchestration tools only. The tools worked well enough -- at least the way Perl tells it. But customers said that they weren't getting the levels of test coverage that they needed, he said. "We found that feature work was always prioritized over test automation," Perl said. "So we made a decision to transform our business and launch a new category that we call 'outcomes-based test coverage.'" This "outcomes-based" approach encourages engineers to prioritize value-adding features as opposed to internal test suites -- a common pitfall with QA platforms -- Perl said. Some QA platforms, even semi-automated ones, still need large teams, infrastructure and even armies of consultants to maintain. But that's less likely to be the case with QA Wolf. "Our customers ship 2 to 5x more often after partnering with QA Wolf, and because teams 'shift left' -- test earlier in the development cycle -- we also cut down the amount of rework," Perl said. "By investing in QA Wolf, executives can give their developers near real-time feedback on their code as they work and allocate more resources to other parts of their business." Today, QA Wolf customers pay for test coverage rather than individual test runs or compute time. QA Wolf supports largely automated (but with humans in the loop) QA testing for Android and iOS apps as well as web apps and Salesforce apps, and offers additional built-in capabilities for maintenance and bug reporting. "We charge a flat rate which includes test creation, unlimited parallel test runs and 24-hour failure investigation and maintenance," Perl explained. "The pricing model aligns our incentive to deliver coverage as efficiently as possible, with the customer's goals." Companies -- and investors -- seem to like this. QA Wolf has over 130 customers, including Salesloft, Drata and AutoTrader.ca. And QA Wolf this week closed a $36 million Series B funding round led by Scale Venture Partners with participation from Threshold Ventures, Ventureforgood, Inspired Capital and Notation Capital. To date, QA Wolf has raised $57 million. Perl says that the latest tranche will be used to build additional features into the platform, enhance QA Wolf's existing automation technology and grow its 130-employee workforce. "The reason that the various no-code or low-code tools on the market haven't solved the QA test coverage gap is that when push comes to shove, the user is incentivized to ship new features, not new tests, or the user doesn't have the technical expertise to build comprehensive test coverage beyond the simplest workflows," Perl said. "QA Wolf's advantage is that the value we provide is exponentially greater than the alternatives when infrastructure and labor to run and maintain comprehensive end-to-end test coverage is all factored in."

TechCrunch

Tue, 23 Jul, 2:03 PM UTC

Microsoft 365 Copilot Beginners Guide 2024

Ever wondered how much more you could accomplish if you had a personal assistant to handle your routine tasks? What if I told you that Microsoft Copilot for Microsoft 365 could be that assistant? This AI-powered tool integrates seamlessly into your favorite Microsoft applications, helping you manage everything from meeting recaps to document generation. Andy Malone MVP has created a fantastic beginner's guide to using Microsoft's 365 Copilot AI assistant. This new AI tool seamlessly integrates with various Microsoft applications, empowering users to streamline their workflows and achieve more with less effort. At its core, Microsoft Copilot uses the power of large language models and semantic indexing to understand and assist users in their daily tasks. By harnessing these innovative technologies, Copilot can generate content, summarize information, and provide intelligent recommendations, ultimately saving you valuable time and energy. One of the key strengths of Microsoft Copilot is its seamless integration with popular Microsoft 365 applications, including: Here are a selection of other articles from our extensive library of content you may find of interest on the subject of using and improving your skills with Microsoft Copilot AI : In today's data-driven world, security and compliance are paramount concerns for any organization. Microsoft Copilot addresses these issues head-on by incorporating robust security features to safeguard user information. With data protection measures in place, users can trust that their sensitive data remains secure and private. Additionally, Copilot adheres to industry standards and regulations, ensuring compliance with relevant guidelines. To further enhance data management, Copilot offers data labeling capabilities, allowing users to categorize and organize their information efficiently. Retention policies can be set to define rules for data storage and disposal, while auditing tools provide comprehensive monitoring and tracking of data usage. Microsoft Copilot offers a range of pricing plans to accommodate the diverse needs of individuals and businesses. Users can access basic features for free through the Edge browser, providing an opportunity to explore Copilot's capabilities without financial commitment. For organizations seeking advanced functionalities, premium features are available to unlock enhanced productivity. Copilot also provides tailored solutions for specific industries and departments. Sales teams can benefit from dedicated applications designed to streamline their processes, while customer service operations can leverage Copilot's AI-powered tools to improve response times and customer satisfaction. The practical applications of Microsoft Copilot are vast and far-reaching. From generating comprehensive proposals to creating professional presentations, Copilot empowers users to accomplish tasks with unprecedented efficiency. By summarizing lengthy email threads and extracting key points, Copilot helps users stay informed and responsive without getting bogged down in information overload. To maximize the benefits of Copilot, continuous learning is essential. Microsoft offers a wealth of resources through Copilot Lab, providing users with training materials and best practices for prompt optimization. By refining their prompts and understanding how to interact effectively with the AI, users can unlock the full potential of Copilot and achieve even greater productivity gains. Microsoft Copilot represents a significant step forward in AI-driven productivity tools. By harnessing the power of advanced language models and seamlessly integrating with Microsoft 365 applications, Copilot empowers users to work smarter, not harder. With its robust security features, flexible pricing options, and practical applications across various industries, Copilot is poised to transform the way we work. Embrace the future of productivity with Microsoft Copilot and experience the benefits of AI-assisted workflows firsthand.

Geeky Gadgets

Tue, 23 Jul, 2:07 PM UTC

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