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Google’s Android N Preview- Developers Perspective

Google released its new operating system Android N preview on 09-03-2016. Google’s unexpected announcement of Android N Developer came that time when several mobile phone manufacturers are struggling to make the Android 6.0 Marshmallow update available to their premium devices.

The launch of Android N developer’s preview saw a good audience and it’s also going to be much, much easier for anybody to try it out. The plan of releasing it in May came little early, as Google wanted to release the preview earlier in order to get more feedback from developers in the process and get the final N release into the hands of device manufacturers this summer. Google’s current plan calls for five preview releases and a final release in Q3 2016.

Google has been working hard on matching Windows and iOS by building a native side-by-side app mode in Android. For Android N, the feature is apparently ready for prime time.

Before you plan of investing in Google’s new OS Android N, here are a few APIs and features we want to highlight which are available as a part of the “Android N Developer Preview”:

Multi-window
A new manifest attribute called android:resizableActivity is available for apps targeting N and beyond. If this attribute is set to true, your activity can be launched in split-screen modes on phones and tablets. You can also specify your activity’s minimum allowable dimensions, preventing users from making the activity window smaller than that size. Lifecycle changes for multi-window are similar to switching from landscape to portrait mode: your activity can handle the configuration change itself, or it can allow the system to stop the activity and recreate it with the new dimensions. In addition, activities can also go into picture-in-picture mode on devices like TVs, and is a great feature for apps that play video; be sure to set android:supportsPictureInPicture to true to take advantage of this.

Screenshot_20160311-121807Screenshot_20160311-121851

 

 

Direct reply notifications
The RemoteInput notification API, which was originally added for Android Wear, now works in N for phones and tablets. Using the RemoteInput API enables users to reply to incoming message notifications quickly and conveniently, without leaving the notification shade.

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Bundled notifications
With N, you can use the Notification.Builder.setGroup() method to group notifications from the same app together – for example individual messages from a messaging app. Grouped notifications can be expanded into individual notifications by using a two-finger gesture or tapping the new expansion button.

Screenshot_20160311-121610 Screenshot_20160311-121558

 

 

Efficiency
You can launch Doze in Marshmallow to save battery when your device is stationary. In N, Doze additionally saves battery whenever the screen turns off. If you’ve already adapted your app for Doze, e.g. by using the GCM high priority message for urgent notifications, then you’re set; if not, here is how to get started. Also, we’re continuing to invest in Project Svelte, an effort to reduce the memory needs of Android so that it can run on a much broader range of devices, in N by making background work more efficient. If you use JobScheduler for background work, you’re already on the right track. If not, N is a good time to make that switch. And to help you out, we’re making JobScheduler even more capable, so now you can use JobScheduler to react to things like changes to content providers.

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Improved Java 8 language support
We’re excited to bring Java 8 language features to Android. With Android’s Jack compiler, you can now use many popular Java 8 language features, including lambdas and more, on Android versions as far back as Gingerbread. The new features help reduce boilerplate code. For example, lambdas can replace anonymous inner classes when providing event listeners. Some Java 8 language features –like default and static methods, streams, and functional interfaces — are also now available on N and above. With Jack, we’re looking forward to tracking the Java language more closely while maintaining backward compatibility.

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Start-Up Time
If you’ve ever updated software on your Android smartphone or tablet, you’ve almost certainly seen that infuriating ‘Optimizing Apps’ popup up card immediately after installing and booting up your device. Depending on how many apps you have, it can take anytime between a couple of minutes and a bazillion years (slight exaggeration) to get past this stage. One of the less obvious new features is that Android’s ‘Optimizing Apps’ screen during startup barely takes any time at all to work through its process with N (Nutella?). Thankfully, with Android N, we won’t have to wait for very long at all.

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Night Mode
Google has bought Night Mode option back, which user can turn to anytime. The night mode produces less strain to user and is addition option in Android N.

Screenshot_20160311-124248 Screenshot_20160311-124254

 

 

Get Started
The N Developer Preview includes an updated SDK with system images for testing on the official Android emulator and on Nexus 6, Nexus 5X, Nexus 6P, Nexus Player, Nexus 9, and Pixel C devices.

This initial preview release is for developers only and not intended for daily use or consumer use. Google plans to update the N Developer Preview system images often during the Developer Preview program. As they are getting closer to a final product, Google will be inviting consumers to try it out as well.

There is more to come as Google continue developing the release. Google is also making it easier for you to try out N on your development devices with the new Android Beta Program. Started yesterday, you can update your Android devices to the developer preview of N and receive ongoing updates via OTA by visiting g.co/androidbeta.

We at Mantra Labs keep continuous watch on latest Technology updates by Google, Apple, Microsoft and others to drive next generation of mobile apps.

 

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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.

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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:

  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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