Kotlin vs Java: Which is a Better Android Programming Language?

Just like Java was named after Java island, Kotlin is also named after an island — Kotlin. Though, sharing the same nomenclature cannot define its usability, right? The 2019 Stack Overflow survey reveals that 72.6% of developers who are using Kotlin for mobile app development want to continue using it, whereas, for Java, it falls to 53.4%.

Kotlin relies heavily on Java libraries. Still, are Java developers migrating to Kotlin or some other programming language? If so, then what’s the reason? This article resolves most of the queries around Kotlin vs Java.

Is Kotlin Better than Java?

In 2017 I/O event, Google announced Kotlin as their officially supported programming language to make Android development faster and more fun. Since then, there is no looking back. Kotlin gained validation for android with the ‘Google’ tag. But, is this enough to evaluate Kotlin vs Java? Certainly not. Let’s look at the 5 aspects where Kotlin is better than Java.

Code syntax: Kotlin has a concise code structure. I.e. you’ll be able to execute a statement in fewer lines than Java. Concise code brings further benefits like code maintainability, readability, and, of course, faster execution. Because of fewer codes, it also saves the developer from making common programming mistakes. In Java, Groovy and Scala are powerful and versatile languages. However, they’re verbose and not optimised for mobiles.
Development Speed: You’ll find default parameter values, object declarations, extension functions, etc. in-built in Kotlin. Now that developers need not write these classes and functions, again and again, it naturally improves project development speed.
Debugging: Kotlin has a fail-fast compiler that searches bugs and prevents them from coming back again. It immediately reports and resolves crash instances, making debugging faster than ever. Also, Kotlin developers don’t have to worry about exception handling.
Features Enhancement: If you want to add some additional features on an existing app, Kotlin allows without changing the architecture. And this is possible on both Java and Kotlin apps. For instance, Uber App’s internal tooling processes (e.g. Annotation processors, Gradle plugins, etc.) are written in Kotlin.
Support: Kotlin also has a fast-growing GitHub community of developers. You can expect rapid support to any run-time issues while developing projects in Kotlin.

Kotlin programing language is the Android’s answer to keeping up with the rapid pace of mobile development. Udemy also reports that Kotlin became the #1 hot tech skills in 2018, with about 95% growth in learners!

Is It Worth Switching from Java to Kotlin?

Kotlin is a statically-typed programming language that runs on the Java Virtual Machine. It is possible to compile it with JavaScript source code or using LLVM compiler infrastructure. Please note, Kotlin’s syntax isn’t compatible with Java. But, it interoperates with Java code and relies heavily on existing Java Class Library.

Kotlin vs Java: Kotlin is an enhancement to Java, rather than a completely new language. Therefore, many of the skills that you’ve acquired through your Java career should still apply to your Kotlin projects. Just in case you come across a code which seems drastically different; Kotlin is intuitive enough to let you understand the purpose of the code. In fact, Pinterest app moved from Java to Kotlin to provide a better user interface.

Well, why should iOS developers have all the fun? If you’re an Android developer and if you’re thinking of rewriting your Java project in Kotlin; don’t stress – Kotlin plugins have you covered. These plugins even have a handy tool that allows you to convert a Java source file to Kotlin.

What About Java 9?

Java 9 introduced a lot of new features for developers and it certainly justifies the requirements of a modern programming language. However, Android-specific developers can use it only partially and are stuck with Java 8 and 7. Its verbose syntax also adds to the agony.

Therefore, you may want to switch to one of the modern programming languages that run on JVM. And Kotlin seems to be the most viable option.

Kotlin’s greatest strengths are the sheer level of interoperability with Java. Everything will still compile flawlessly and users won’t be able to figure out Java/Kotlin components. You won’t have to convert or rewrite anything at all.

It would be unjust if we don’t mention the areas where Java is still better than Kotlin. For instance, there is an extra runtime size with Kotlin. Kotlin Standard Library and runtime will increase the size of your APK but this only equates to around 800KB.

Kotlin Vs Java: Is it a good to switch meme

Kotlin vs Java: What Developers Say?

After reading Josh Bloch’s “Effective Java”, I realized many of the pitfalls and drawbacks of Java. Then after going through the Kotlin docs in Dec 2015/Jan 2016, I was impressed at the clean language design.
Peter Sommerhoff, Developer & Online Instructor

I’m literally all over Kotlin now. It’s awesome! Productivity is up. Code quality is much better. I’m coming up with far smarter and more concise and more elegant algorithms and solutions. Even when it is demanding that you must code in Java, it’s still proving to be useful, because you can use it as a prototyping language.
Mike Milpot, Software Architect, Inventor

I have used Kotlin on the server in areas not related to Android at all. I used it for AWS SDK, Apache Spark, JSON, SQL/JDBC, Apache Presto, Amadeus (travel) Java SDK interop, etc. Kotlin performed in all those tests without any issues, a 1st class JVM citizen.
Yuri Budilov, former SQL Server Database Architect, Consultant, DBA, Developer (contract), Microsoft

Java has been here since like forever while on the other hand, Kotlin is quite new. Being old doesn’t mean the language does not have new features or is not capable of competing with the new one. Java is a very established language and used in plenty of Android apps developed till date.
Pratik Kanada, CEO, 360 Degree Technosoft

Is literally almost the same. It just has a few syntactic sugar stuff that makes coding “shorter” compared to Java. If you already know Java I wouldn’t switch over until everyone switches over.
Ka Tai Ho, Software Engineer, Microsoft

After 10 years with Java development and 2 years with Kotlin, I feel I can’t go back anymore. Why? The simplicity, null safety, the functional programming aspects, the consistent and beautiful APIs and the fact I can use it with any Java library or framework.
Luís Soares, Full-stack Developer, Volkswagen

Source: Internet

Conclusion

Java 7, 8 and 9, with all their workarounds, back-ports and tools to overcome those hurdles, still have room for improvement. The newer, lightweight Kotlin successfully advances existing Java paradigms, solve problems with API design flaws. It is equally suitable for enterprise back-end systems and to make Android mobile development better.

Overall, Kotlin is one of the safest bet as an alternative to Java for custom Android app development.

And did I mention, semicolons are optional ;)

Stay tuned for more updates.

Also read – Kotlin vs Flutter for Mobile App Development

General FAQs

Is Kotlin better than Java?

When it comes to android programming, Kotlin is definitely better than Java. Following are the 5 good reasons-
1. Concise code: You can execute the same function in fewer lines of code in Kotlin. Its concise code structure also makes the code more readable and easy to update/modify.

2. Fast to develop a program: Default parameter values, object declarations, extension functions, etc. are in-built in Kotlin. Thus, making the android app development faster than ever.

3. Instant debugging: Kotlin has a fail-fast compiles that immediately reports and resolves crash instances. Also, developers need not worry about exception handling, unlike Java.

4. Add features easily: Kotlin allows you to add features to existing apps without changing the application architecture.

5. Community support: Kotlin also has a fast-growing GitHub community of developers. You can expect rapid support for any run-time issues.

What is the difference between Kotlin and Java?

Java is an object-oriented programming language and most popular for building standalone applications or back-end development for decades.

Kotlin is a new, open-source programming language based on JVM (Java Virtual Machine). However, Kotlin can be compiled to Javascript, Android, and Native. Kotlin’s syntax isn’t compatible with Java. But, it interoperates with Java code and relies heavily on existing Java Class Library. Kotlin is widely used for android app development because of the flexibility and ease it brings to the programming.

Is Kotlin replacing Java?

In a way, for android app development, Kotlin may replace Java. The 2019 StackOverflow survey reveals that 72.6% of Kotlin developers want to continue with it whereas, 53.4% of Java developers want to continue Java (i.e. nearly half of Java developers are switching to other programming languages). However, since Kotlin relies on Java Libraries, Java is never going out of the scene.

Is Kotlin worth learning?

If you want to specialize in Android application development, then Kotlin is totally worth learning. Moreover, if you’re familiar with Java, then learning Kotlin will be like a cup of tea for you.

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.

Kotlin vs Java: Which is a Better Android Programming Language?

Is Kotlin Better than Java?