Java

Incorrect Spring Security configuration easily leads to security breaches including exposing server data, improper authorization, and leaving default settings enabled. Security issues require vigilant code reviews.

Virtual threads introduced in JDK 21 suffer from pinning when using synchronized blocks or certain I/O operations, destroying scalability benefits especially in large codebases with idiomatic Java patterns. Migration from platform threads required architectural changes rather than simple runtime decisions.

Developers misunderstand CompletableFuture behavior, lose exceptions in thread pools, and mishandle error propagation in parallel execution, creating subtle runtime bugs that only appear under production load.

JVM ecosystem depends on external ASM library to support new classfile formats with each JDK release, creating a synchronization race where builds break when frameworks haven't caught up, forcing teams to wait for ASM updates before adopting new JDK versions.

Oracle is slow to provide updates for known Java security bugs and has performed forced downgrades (e.g., removing Java 6 despite assuring enterprise users it wasn't affected) during patch deployments.

Developers must compile class files, replace them in .ear archives, and restart application servers like Tomcat, with the entire cycle taking approximately 5 minutes. This blocks iteration and makes debugging frustrating compared to hot-reload capabilities in other languages.

Development teams face tightened budgets and blanket hiring freezes while being tasked with maintaining increasingly complex applications. Java hiring plans dropped from 60% in 2024 to 51% in 2025, and tool budgets fell from 42% to 34%.

Java has significantly fewer AI-specific libraries compared to Python; TensorFlow and PyTorch are more mature in Python. Java developers face challenges building or training ML models with limited ecosystem depth and fewer experts available.

52% of developers using remote, containerized, or cloud-based environments experience redeploy times of 5+ minutes, with 13% reporting 10+ minutes. This is more than double the 23% experiencing such delays in local environments, creating a significant productivity barrier.

Large Java codebases using pre-generics untyped collections (e.g., plain List, Map) require developers to hunt through code to determine container contents. Modernization to typed generics requires extensive refactoring.

Java startup and warmup time is significantly slower than languages like Go, making it unsuitable for serverless, containerized, and microservice environments where rapid startup is critical.

Organizations continue using older, unnecessarily complex development practices including XML-based Spring Bean configurations, Enterprise Java Beans (EJBs), and Ant build processes, despite modern alternatives like Spring Boot and Gradle being far more developer-friendly.

More than 45% of respondents noted an increase in microservice startup times, with 22% reporting increases of 10% or more. This compounds deployment delays in cloud and microservices architectures.

GraalVM JDK 24 is the last release as part of Java SE; Native Image is no longer in the Java SE offering, and the experimental Graal JIT has been withdrawn. This creates uncertainty for teams relying on GraalVM as their optimization path and forces re-evaluation of architecture decisions.

Java's lack of true value types and inability to mix generics with primitives leads to excessive cache misses and memory overhead, making numeric and high-performance applications less efficient than C or C#.

GPU acceleration support in Java requires extra setup and tuning compared to Python, and forcing GPU allocation per application instance (even when idle) creates scaling and maintenance challenges with higher infrastructure costs and lower resource efficiency.

Java's generics were implemented using type erasure for backward compatibility, limiting available features compared to other languages. A 2016 discovery revealed Java generics to be unsound, causing ClassCastExceptions and runtime errors.

Java's performance on mobile and embedded systems is notably poor. Game designer John Carmack noted Java phones had CPU power equivalent to a 4.77 MHz IBM PC despite modern hardware being far superior.

Java's array indexing is restricted to int values (2^31 elements), creating bottlenecks for collections, memory-mapping files larger than 2 GB, and scientific computing. Fixing this requires JVM-level changes.

Java developers new to AI face lack of clear starting points, feeling overwhelmed by variety of AI models and libraries, missing practical step-by-step workflows, and unclear guidance on securely integrating private models into applications.

Java's verbose syntax requires lengthy declarations for simple tasks, making rapid prototyping and scripting slower compared to terser languages. While modern features like records and var typing help, developers accustomed to more concise languages still find the ceremony cumbersome.

Java's checked exceptions require methods to declare all thrown exceptions in signatures, resulting in unnecessarily verbose boilerplate code. No other major language has adopted this pattern, making it a Java-specific burden.

Java stack traces with long 'caused by' chains are difficult to parse, especially when log files contain tens of thousands of lines. Root causes can be unrelated to the thrown error, making debugging time-consuming and requiring deep analysis.

Java lacks native unsigned integer types, preventing direct data interchange with C programs that generate unsigned data. This is particularly problematic for cryptography and numeric processing fields.

Java's Stream API provides elegant functional data processing but is surprisingly closed to extension, limiting flexibility for developers with functional programming backgrounds who need to customize stream behavior.

Using native libraries from Java is cumbersome and introduces significant complexity, with insufficient SIMD vector support making numeric-heavy operations slower than languages designed for native interop.

26% of respondents cite developer turnover as a barrier to productivity, creating instability in team composition and loss of critical architectural and domain knowledge.

Developers perceive Java as an outdated language with limited fresh online resources compared to languages like JavaScript. YouTube creators discuss Rust, Go, Ruby on Rails, and Zig more frequently, leaving Java feeling like a relic with diminished community enthusiasm.

Java's design philosophy forces developers to think in terms of nouns (classes) with verbs (methods) bound to them. This restricts language expressiveness because functions cannot operate on multiple types—a function is bound to a single class.

Java's design is not idiomatic or optimized for frontend interfaces and lightweight applications, despite being technically possible. Frameworks like React and platforms like Flutter are better-suited for web UI development.

Java's release cadence prioritizes long-term compatibility over rapid innovation, making evolutionary changes feel slow compared to more dynamic languages that frequently introduce expressive features. Developers seeking rapid language evolution may find this limiting.

Developers transitioning from non-OOP or less OOP-heavy languages find Java's class hierarchies, inheritance, and implementation patterns confusing and unnecessarily complex compared to modern, dynamic system design approaches.

Java does not natively support tuples, forcing developers to import and manage numerous third-party implementations. This creates maintenance challenges and code fragmentation.

Java's IO operations are considered cumbersome, with some developers still using outdated patterns like buffered readers in 2025 rather than modern IO approaches, indicating poor discoverability or adoption of better alternatives.

Java does not support user-defined operator overloading, making code less readable for classes representing mathematical objects like complex numbers and matrices.