Are you an Android developer eagerly awaiting the next big update? Well, your wait is over with the arrival of Android 14! This latest version of the Android operating system, known as Upside Down Cake, brings a plethora of exciting features, promising to revolutionize app design and development. In this article, we will explore Android 14 from a developer’s perspective, diving deep into its innovative features, the impact on app design, and the best practices for leveraging its powerful capabilities.

Minimum Android Studio Version required: Android Studio Flamingo | 2022.2.1 or higher.

What’s New in Android 14?

Android 14 brings a host of new features and updates for developers to take advantage of. Let’s dive into some of the most significant changes.

Performance and Efficiency

Freezing cached applications

Android 14 introduced several restrictions on the use of cached applications. After a brief interval, we freeze cached apps on Android 14, giving them no CPU time. Cache-based programmes use up to 50% fewer CPU cycles in Android 14 Beta populations when compared to Android 13 public devices. Therefore, outside of standard Android app lifecycle APIs like foreground services, JobScheduler, or WorkManager, background work is prohibited.

Optimized broadcasts

We changed how apps get context-registered broadcasts once they enter a cached state; they may be queued, and recurring ones, like BATTERY_CHANGED, may be combined into a single broadcast, in order to keep frozen applications frozen longer (i.e., not receive CPU time).

Faster app launches

With broadcast optimizations and cached apps, we were able to raise the platform’s long-standing caps on the maximum number of cached apps in Android 14, which decreased the number of cold app starts, which is based on the RAM capacity of the device. The beta group experienced 20% fewer cold app starts on 8GB devices and nearly 30% fewer on 12GB devices. In comparison to warm companies, cold startups are slower and require more electricity. This approach effectively reduces total programme starting times as well as battery consumption.

Reduced memory footprint

The Android user experience can be greatly enhanced by improving the Android Runtime (ART). One of the most important metrics we consider is code size; smaller generated files are better for memory (RAM and storage). With no degradation in speed, Android 14’s ART optimizations lower code size by an average of 9.3%.

Customization

Since customization is fundamental to the Android experience, Android 14 maintains our promise to empower users to tailor their experience to meet their unique requirements. This includes improved accessibility and internationalization tools.

Bigger fonts with non-linear scaling: With Android 14, users will be able to magnify text by 200%. The maximum text size scale for Pixel devices used to be 130%. A non-linear font scaling curve is automatically applied to text that is large enough to keep it from increasing at the same rate as smaller text. Learn more here.

Per-app language preferences: You can dynamically update your app’s localeConfig with LocaleManager.setOverrideLocaleConfig to customize the set of languages displayed in the per-app language list in Android Settings.

IMEs can now use LocaleManager.getApplicationLocales to know the UI language of the current app to update the keyboard language. Starting with Android Studio Giraffe and AGP 8.1, you can configure your app to support Android 13’s per-app language preferences automatically.

Regional preferences: Users can customize temperature units, the first day of the week, and numerical systems based on their regional preferences.

Grammatical Inflection: You may add support for users who speak languages with grammatical gender more quickly thanks to the Grammatical Inflection API. All you have to do to display customized translations is incorporate the API and add translations that are inflected for every grammatical gender in the languages that are affected.

New media capabilities

Ultra HDR for images: With support for the Ultra HDR image format, Android 14 adds support for 10-bit high dynamic range (HDR) photographs. Because of the format’s complete backward compatibility with JPEG, programmes can work with HDR photographs with ease.

Zoom, Focus, Postview, and more in Camera Extensions: With Android 14, Camera Extensions are enhanced and expanded, enabling apps to manage longer processing times and, on compatible devices, better photographs through the use of compute-intensive algorithms like low-light photography.

Lossless USB audio: Lossless audio formats are supported on Android 14 devices, enabling audiophile-quality experiences when using USB-wired headsets.

New graphics capabilities

Custom meshes with vertex and fragment shaders: Custom meshes, which are defined as triangles or triangle strips and may optionally be indexed, are now supported by Android 14. Custom properties, vertex strides, variables, and AGSL-written vertex/fragment shaders are used to specify these meshes.

Hardware buffer renderer for Canvas: In Android 14, HardwareBufferRenderer is introduced to help with drawing with hardware acceleration into a HardwareBuffer using Android’s Canvas API. This is especially useful if your use case involves low-latency drawing through SurfaceControl communication with the system compositor.

User experience

Predictive Back: In addition to the back-to-home animation seen in Android 13, Android 14 brings two additional Predictive Back system animations: cross-activity and cross-task. To give more time for refinement and to let more apps choose to use Predictive Back, the system animations are still hidden behind a developer option. However, users can now access Material and Jetpack Predictive Back animations.

Privacy and security

Data sharing updates: When an app shares location data with third parties, users will see a new section in the location runtime permission dialogue where they can manage the app’s data access and obtain further information.

Partial access to photos and videos: Android 14 users can now allow your app access to only specific images and videos when it asks any of the visual media permissions (READ_MEDIA_IMAGES / READ_MEDIA_VIDEO) introduced in SDK 33. We advise using our most recent best practices to modify your app in light of this update.

App compatibility

Android prioritizes app compatibility to make updates faster and more seamless with each platform release. To offer you more time to make any necessary app modifications, we’ve made the majority of changes to Android 14 opt-in until your app targets SDK version 34. We’ve also improved our tools and processes to help you get ready sooner.

Easier testing and debugging of changes: This year, Android will make many opt-in modifications toggleable once more to make it easier for you to test the changes before they impact your app. You can use the toggles in Developer settings or adb to individually force-enable or deactivate the changes. See the information on this link.

How to Get Started with Android 14?

For the best development experience with Android 14, we recommend that you use the latest release of Android Studio Hedgehog. Once you’re set-up, here are some of the things you should do:

Try the new features and APIs. Report issues in our tracker on the feedback page.
Test your current app for compatibility – learn whether your app is affected by default behavior changes in Android 14. Install your app onto a device or emulator running Android 14 and extensively test it.
Test your app with opt-in changes – Android 14 has opt-in behavior changes that only affect your app when it’s targeting the new platform. It’s important to understand and assess these changes early. To make it easier to test, you can toggle the changes on and off individually.
Update your app with the Android SDK Upgrade Assistant – Android Studio Hedgehog now filters and identifies the specific Android 14 API changes that are relevant to your app, and walks you through the steps to upgrade your targetSdkVersion with the Android SDK Upgrade Assistant.

Conclusion

Android 14 is offering an array of new features and improvements that can elevate app design and development to new heights and ensure your app is compatible and provides a great user experience. By embracing the customization options, developers can adapt their apps to meet the ever-changing preferences and needs of users creating more engaging and user-friendly applications.

Reference taken from: https://android-developers.googleblog.com/2023/10/android-14-is-live-in-aosp.html

https://developer.android.com/about/versions/14/summary

https://developer.android.com/about/versions/14/behavior-changes-14

About the Author:

Anand Singh is currently working with Mantra Labs as a Tech Manager. He has a strong knowledge of mobile development.

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.

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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:

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.

Industry	Potential Annual Value
Banking	$200 billion – $340 billion
Retail and CPG	$400 billion – $660 billion

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.

Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

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.
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.
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.
Limited Contextual Understanding:
- AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
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:

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.
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.
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.
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:

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

Enablers:

Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.

Future Trends in Code Automation:

AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
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.

Android 14: Unlocking New Possibilities for Developers

What’s New in Android 14?