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11 Design Trends in Mobile App Design for 2016

The mobile phone has rapidly become the most widely used technology in the world. It is getting far more common than traditional computers. The statistic forecast that the total number of smartphone users worldwide by 2020 would be 6.1 billion, according to the annual Mobile Report from Ericsson.

The statistics show every year brings to the fore more in the realm of business access via mobile apps. The impact of a smartphone for business is undeniable. From smartphones to tablets, the ways to look up products and research via mobile apps are increasing at an alarming pace and it’s better to stay ahead than be left in the dust.

The rule of the game is simple: you’ve got to get—and hold—the attention of your consumers. And hold it long enough until they’ve paid for your product or services.

With mobile being front and center more than ever, designers are looking to perfect their mobile-design skills for their clients and customers. They have to prioritize the user experience when designing mobile apps, and designing native mobile apps that offer a richer experience than mobile web apps is the way to go.

The top trends that Mobile Design holds for 2016 are:

1- Increasing Wearable Influence
Wearables have taken concept of mobile to a whole new dimension. According to the Gartner’s prediction of 2016 wearables market, there will be 50.4 million units sold in wearable market, which is an 18.4% increase from 2015 sales.

Not only is the screen smaller than a traditional mobile device, but wearables also encourage people to use mobile technology in different scenarios than smartphones and tablets, leading to necessary changes in the user interface.

For instance, the way you’d tap the screen of a smartphone to open a native news app is different to how you’d have to reach over with one hand to touch your Apple Watch to use its features.

As a result, 2016 will see designers and developers race to create intelligent, user-friendly wearable apps that are unique to this type of mobile device.

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2- Multi-app Split Screen
Multi-app Split Screen allows you to do two things at once on your screen. It means, if you are on your Evernote native mobile app, you can email at the same time or tweeting and can look something up on the Internet simultaneously. The convenience is beyond amazing, as it saves the time and trouble of tapping the home button, and then constantly switching between two (or more) open app windows.

It’s clear that impressive features such as split-screen capability have made all the difference to consumers.

With Android fans clamoring for, and wondering when Google is finally going to introduce the mobile split-screen feature on its devices. Google seems to already be making baby steps toward this right now, so it’ll be interesting to see what they come up with later this year.

The time also seems right for designers and developers to focus their efforts on finally producing split-screen multitasking for Android.c6902bcd8538d2bc8d8bfad575352934

3- Surge in Native and Mobile Web Apps
For some time, there’s been a dispute over what type of mobile app works best: native mobile apps that you open by tapping on your smartphone’s home screen or mobile web apps that you access from mobile versions of browsers.

Designers and developers would argue that native apps are superior because they are faster for the users, can be monetized in places like the App Store, and offer users access to mobile operating system features like the camera, contact lists, etc. That’s definitely true!

However, recent research by Google’s product director, Luke Wroblewski, indicates that there’s great demand for both types of apps, in spite of the apparent limitations of mobile web apps. According to his data, native app users spend 18 times longer on native apps than on mobile web apps…yet mobile web apps see almost 9 million monthly visitors compared to native apps’ 3.3 million monthly visitors.

Therefore, mobile audience growth is based on mobile websites, so developers will have to continue serving that market with mobile web apps, too, while continuing to prioritize mobile native apps that have a better UX. Screen-Shot-2016-02-04-at-10.20.38-PM

4- Better UI for Gestures
What’s a gesture? In mobile design, it’s divided into two groups, according to Google:

  • Touch mechanics (what your fingers do on a screen)
  • Touch activities (what they accomplish, as a result)

For example, if I tap on my iPhone’s native mobile mail icon, I’ve produced a touch mechanic, that in turn creates the ensuing touch activity, which is my inbox opening.

With projections of smartphone users at more than 6 billion globally by 2020, it’s high time that designers design UIs to better accommodate a range of mobile gestures.

Touch mechanics include:

  • Touch (tap)
  • Force Touch
  • Double touch
  • Drag, swipe or fling
  • Long press
  • Long-press drag
  • Double-touch drag
  • Pinch open
  • Pinch closed
  • Two-finger touch
  • Two-finger drag, swipe or fling
  • Two-finger long press
  • Two-finger long-press drag
  • Two-finger double touch
  • Rotate

Some native mobile apps, such as Starbucks, use an unappealing UI that makes, for instance, validating a free drink reward more cumbersome than it should be. If I have a free drink, I have to actually shake my phone by using my entire hand to get the barcode to appear on the screen for it to be validated.touchscreen-hand-gestures

5- Material Design Captivates the Mobile Design Community.
Material Design is not completely flat anymore because it uses techniques like gradients, shadows and other subtle, 3D effects. As a result, this slightly different approach to native mobile design has people excited because it’ll drastically improve the UX on mobile devices.

Unsurprisingly, Google will be a big player in influencing native mobile design trends this year. Beyond mobile, Google’s Chrome browser is also set to incorporate material-design touches in its interface, and Google is all set to unveil its new material design-influenced Chrome browser soon.

Though material design was slow to be adopted to various Android apps last year— we’re talking Gmail, YouTube, Google Maps—that’s changing in 2016, as more native apps from companies outside of Google join the material-design bandwagon.Screen-Shot-2016-02-10-at-12.03.46-AM

6- Moving Animation
People are naturally programmed to take notice of movement, so incorporating movement into a mobile design can be the perfect way to highlight a specific product. This makes moving animation a superb tool for e-commerce sites. This also has a secondary, more useful purpose: Letting users move products around on the screen before they buy it makes it easy for them to inspect it from all sides…almost as if they were in a real, tactile store.

With Web Designer Depot proclaiming that “animation is no longer a novelty for web designers…it’s becoming the basis of effective interaction design,” moving animation is set to take off in a large way this year as designers increasingly realize that movement helps tell a story, and that helps the UX.

Bugaboo’s mobile site features moving animation that lets customers examine its popular strollers from all sides, which beats merely looking at still pictures to make a buying decision.

7- Micro-interactions Become More Prevalent
Micro-interactions are focused on the UX of a native mobile app. They’re subtle tasks that work around four elements.

  • The Trigger
  • The Rules
  • Feedback
  • Loops and Modes

For example, Slack is an awesome native mobile app that is replete with great micro-interaction examples.

I tap the “plus” sign next to Direct Messages. This is the trigger because it starts the micro-interaction. Now, I get to communicate directly with my Slack collaborator, which forms the rules or the way the interaction functions. How do I know this? Because, thirdly, the feedback Slack gives me on-screen shows a “New Conversation” box opening up, where I can chat directly with her. Finally, the length of this micro-interaction—or loop—is as long as I want it to be, as I can close the new message when I want to.

As you can see, these micro-interactions let people instantly observe the results of their on-screen actions by providing helpful and understandable feedback. This greatly improves the UX. As more designers see this innate value to users, micro-interactions will get more widespread.

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8- Subdued Color Contrast
You’ve probably been taught that contrast should be high and loud to facilitate an easier reading experience, but 2016’s mobile design trends go against the mainstream conventional wisdom by toning things down a bit.

The usual typography contrast is black font on a white background—there are even various color-contrast calculators on the web (like Contrast Ratio) to help you find good contrast for readability.

In 2016, though, contrast will get less loud and more subtle, which is actually easier on the user’s eyes and facilitates a better reading experience. Some of the biggest websites in the world are already moving to this subdued form of color contrast.Screen-Shot-2016-02-10-at-12.02.15-AM

9- Flat Design 2.0 Begins to Replace Flat Design
One of the few, but big, criticisms of flat design is the lack of signifiers on things like icons and buttons, which fail to adequately communicate functional design elements. This has a negative impact on UX since what users can click and tap isn’t necessarily obvious at first.

This might not seem like a big deal to veteran users, but the point of good native mobile design—mobile and otherwise—is to make the UX easy enough that even novice users can find their way around your native app or mobile website.

Flat design 2.0 is like Material Design in a sense because both use more 3D effects like shadows, gradients and lighting effects. However, whereas Material Design is more of an aesthetic and design philosophy based on paper and ink (read: tactile elements), flat design 2.0 is an actual response to and way of addressing flat design’s shortcomings.

One should look for more mobile sites and apps to retain the flat look, but with noticeable shading to indicate subtle 3D elements. These will make it easier for users to figure out where to tap and slide, such as Android Evernote’s native mobile app above.FLAT_DESIGN_vs_REALISM_02

10- Increased Tracking in Typography
So much of the mobile web today deals with readability. One of the most important aspects of readability is tracking, or the consistent space between all letters in a word. The greater the tracking, the easier the word is to read because users don’t have to squint to read the word.

With the big focus on native mobile usability, designers need to look at increasing the readability of their content.

Typography authority Typewolf released its list of “the most popular fonts of last year”, and the big trend is spacious and generous tracking between letters of the most popular fonts, which will continue into 2016. With Gartner predicting the number of mobile devices increasing in 2016, readability is going to be increasingly vital to UX designs that will attract native mobile app users in even greater numbers.

11- The Grand Scheme of Interactive Designs
his one means we’ve started to see apps focused around users. The challenge to draw in more and more users has given birth to interaction designs. This, of course, entails the integration of interactive elements to lure consumers with an interesting, powerful and creative visual story. This kind of design’s got punch, we’ll give you that.Blog-Post132

Conclusion
Look for these trends to dominate the native mobile landscape as the year progresses. It’s clear that now is the time of mobile, as evidenced by mobile users now completely outweighing desktop users…. Don’t hold your breath waiting for these stats to reverse.

With this focus on mobile, it’s no surprise that the design community is looking for increasingly interesting designs to improve the UX and get people on native mobile with greater comfort than ever.

What do you think of this list of mobile design trends? Are there any we left off? If you think there are, please email us at hello@mantralabsglobal.com

Check out these articles to catch the latest trends in mobile apps:

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  5. AI in Mobile Development
  6. 10 Reasons to Learn Swift Programming Language
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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

Image Source

  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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