8 Best Ways to Reduce Android App Size

With the increase in mobile storage spaces that have gone up to 256 GB, app size is also growing. App size is sure to grow as developers are adding new features, trying to meet customer needs, and also trying to support their apps on various screen sizes. Around 74% of the world uses Android, and 70% of users consider app size before installing them. Moreover, as humans are getting accustomed to instant gratification, they ponder on ways to download apps as they take up storage spaces. Despite the cloud support for photos, videos, and files, android users face issues, such as mobile hanging due to app size. As customer expectations are increasing, android app developers are considering other ways to reduce app size while still incorporating significant additional features and keeping in mind the customer experience.

Below are the 8 best ways to reduce android app size:

1. Use Android App Bundle to Reduce App Size

When generating the release version of your app, you can choose between APK and Android App Bundle. The second option will make Google play to generate the APK with only those features a specific user need.

App Bundle Vs APK

Android App Bundle

It is a publishing format that includes compiled code and resources of your app, and delays APK generation and signing to Google Play.
With Android App Bundles, the compressed download size restriction is 150 MB. The app bundle cannot be used with APK expansion files.

How to build android app bundles?

To build app bundles, follow these steps:

Download Android Studio 3.2 or higher—it’s the easiest way to add feature modules and build app bundles.
Add support for Play Feature Delivery by including a base module, organizing code and resources for configuration APKs, and, optionally, adding feature modules.
Build an Android App Bundle using Android Studio. You can also deploy your app to a connected device from an app bundle by modifying your run/debug configuration and selecting the option to deploy APK from app bundle. Keep in mind, using this option results in longer build times when compared to building and deploying only an APK.
If you’re not using the IDE, you can instead build an app bundle from the command line.
Test your Android App Bundle by using it to generate APKs that you deploy to a device.
Enroll into app Play App Signing. Otherwise, you can’t upload your app bundle to the Play Console.
Publish your app bundle to Google Play.

Please note: Android Package Kit – As per developer console, by the mid of 2021, developers won’t be able to upload apk on play store)

Android operating system uses APK which is the package file format for distribution and installation of mobile apps, games and middleware. APK is similar to other software packages such as APPX in Microsoft Windows or a Debian package in Debian -based operating systems.
Google Play requires that the compressed APK downloaded by the users should not exceed 100 MB.
The expansion files for your app are hosted by Google Play which serves them to the device at no cost to you. The expansion files are saved to the device’s shared storage location (the SD card or USB-mountable partition).

2. Use Proguard

Proguard is probably one of the most useful tools to reduce your APK size. It reduces the source code files to a minimum and can reduce the APK file size upto 90%.

Use it in all variants whenever using “Proguard”
Helps to avoid conflict at the of generate apk or bundle if will use in all the variants.
We cannot let ProGuard rename or remove any fields on these data classes, as they have to match the serialized format. It’s a safe bet to add a @Keep annotation on the whole class or a wildcard rule on all your models.

3. Use Android Size Analyzer Plugin

This Android Studio plugin will provide you recommendations to reduce the size of your application.

With the APK Analyzer, you can accomplish the following:

View the absolute and relative size of files in the APK, such as the DEX and Android resource files.
Understand the composition of DEX files.
Quickly view the final versions of files in the APK, such as the AndroidManifest.xml file.
Perform a side-by-side comparison of two APKs.

There are three ways to access the APK Analyzer when a project is open:

Drag an APK into the Editor window of Android Studio.
Switch to the Project perspective in the Project window and then double-click the APK in the default build/output/apks/ directory.
Select Build > Analyze APK in the menu bar and then select your APK.

More details at: Jetbrains

4. Optimize Your App’s Resources

Whether used or not, every resource takes up memory. It is therefore necessary to have only those resources that you need, and to use those in a memory efficient way. In other words, you should consider the resolution of the image before finalizing on it.

5. Optimize Libraries

As large libraries consume huge amounts of space, it is advisable to remove parts of it in case you do not need them and if it is permitted by the license of the library. Proguard can aid you in this process but it is not always able to remove large internal dependencies.

6. Use Vector Graphics Wherever Possible

They are sharp and do not consume much space as they rely on mathematical calculations and not on pixels that need to be saved. However, they cannot be used for photography.

7. Compress Your Images

By using tools such as pngcrush, you can reduce the file size of PNG images. It is advisable to do this images as they still look the same.

8. Only Support Specific Densities

If only a small portion of users use a specific density, it might be better to let Android scale your other densities for them as it would reduce your APK size.

As mobile storage space is growing, people are installing a large number of apps to meet a wide range of needs. But as app size is increasing, people are continuing to struggle with storage issues. With provisions such as Proguard, one can compress the APK file size and optimize libraries easily. Compressing images and using vector graphs are also useful in reducing app size.

About the author: Anand Singh is Tech Lead at Mantra Labs. He is integral to the company’s Android-based projects and enterprise application development.

Further Reading:

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:

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.

8 Best Ways to Reduce Android App Size