Homework 9: Feature Design
CS 538: Programming Languages
Deadline: December 13 23:59
Objective: This project is designed to challenge your ability to condense complex information into a clear
and insightful one-page document. You will explore and compare a speciffc feature of programming language
design against a contrasting approach. Your analysis should provide a mature understanding of the feature
highlight critical differences with the alternative, and offer commentary on the feature’s evolution.
Instructions:
Use the following instructions as a guide to write this report. You may skip, expand or introduce a new
section if needed to convey your ideas. The headers and word counts are suggestions.
If you are writing more than 500 words, you are probably not being concise enough.
• Feature Analysis (100 words): Introduce the language feature. Describe the design axes of your
chosen language feature. Provide insight into its theoretical underpinnings and real-world utility.
• Comparative Analysis (200 words): Compare the language feature with an alternative. Identify and
succinctly discuss the trade-offs involved (e.g. efffciency, reliability, scalability, developer experience).
• Evolutionary Perspective (200 words): Brieffy outline the historical evolution and recent developments
 or future trends related to the language feature. In particular, how have the design axes changed
over time.
• References (in a footer): Cite high quality sources, such as technical papers, books, or expert
commentary. Use a short readable citation format of your choice.
Format:
Single page.
Small headings for each section.
Include citations where relevant.
Export your document as a PDF in a layout that enhances readability.
Assessment Criteria:
Depth of analysis and insight
Relevance and accuracy of comparisons
Quality of sources and literature integration
Clarity of expression and adherence to space constraints
Note: I not only allow, but encourage you to use language model assistants when writing this report. I
would recommend using them as a form of reffnement for your writing process.
Note: If you ffnd yourself writing ”as mentioned above,” you are not being concise. Begin by copy-pasting
the ffrst paragraph of your topic from wikipedia. Continue to write your page, then delete the wiki paragraph.
Note: An example is worth 300 words. Short examples are preferable to trying to vaguely describe a concept.
Note: If your paper is summed up with X is <adj>er, Y is <adj>er, you haven’t written a paper. You’ve
written a boring tweet.
1Feature List
It is recommended, but not required, that you choose a feature from the list below. Memory management is
intentionally omitted from this list because it tends to be lead to low quality submissions.
1. Type Systems:
• Time of Typing (e.g. static, dynamic)
• Strength of Typing (e.g. strong, weak)
• Type Inference
2. Concurrency Models:
• Thread-based Concurrency (e.g., Java threads)
• Event-driven Asynchronous Models (e.g., JavaScript’s event loop)
• Actor Model (e.g., Erlang)
3. Error Handling Mechanisms:
• Exceptions (e.g., Java, Python)
• Return Codes (e.g., C)
• Result Types/Sum Types (e.g., Rust’s Result < T, E >, Haskell)
4. Function Invocation:
• Call by Value vs. Call by Name
• First-class Functions and High-order Functions
• Tail-call Optimization
5. Design Patterns for Code Reusability:
• Inheritance vs. Composition vs. Dependency Injection
• Mixins and Traits (e.g., Scala Traits, Ruby Modules)
• Prototypal Inheritance (e.g., JavaScript)
6. Module Systems and Namespace Management:
• Package Management (e.g., NPM for JavaScript, PIP for Python)
• Modular Programming (e.g., Java Modules)
• Namespaces and Scoping Rules
7. Immutable vs. Mutable Data Structures:
• Beneffts of Immutable Data (e.g., in functional languages like Haskell)
• When and Why to Use Mutable Data (e.g., performance considerations in imperative languages)
8. Compiling Strategies:
• Just-In-Time (JIT) Compilation (e.g., JavaScript V8 Engine)
• Ahead-of-Time (AOT) Compilation (e.g., C/C++, Rust)
• Transpilation (e.g., TypeScript to JavaScript)
2The Actor Model is a framework of concurrent computation that encapsulates state and behavior
within autonomous actors, each processing and communicating asynchronously through message-passing
to avoid shared state challenges. The Actor Model is important in the context of programming language
design due to its efficient handling of concurrency and distributed systems through isolated actors that
communicate via message-passing, simplifying complex, shared-state concurrency issues.
Essential in concurrent and distributed computing, the model revolves around actors as
fundamental units of computation. These independent entities, encapsulating state and behavior, interact
via message-passing, eliminating shared-state concurrency issues like deadlocks. Each actor processes
messages sequentially from its mailbox, maintaining state consistency. Actors can spawn other actors and
dynamically adapt their actions based on messages, allowing flexible responses to computational changes.
Theoretically, the model, established by Carl Hewitt in the 1970s, simplifies parallel computing's
complexity, focusing on system logic over synchronization challenges. Its real-world utility is evident in
scalable, resilient systems, particularly in cloud computing and large-scale internet services. Languages
like Erlang and frameworks like Akka utilize this model, enhancing robustness in high-availability
systems and managing complexities in distributed environments. This abstraction is crucial in modern
computing, enabling developers to construct responsive, fault-tolerant applications adept at handling
distributed system intricacies, such as network failures and variable loads.
The Actor Model and the Event-Driven Asynchronous Model (EDAM), tailored for concurrency,
exhibit distinct approaches and applications. The Actor Model, featuring autonomous actors
communicating via message-passing, excels in distributed systems, offering scalability and fault
tolerance. It efficiently bypasses shared-state concurrency issues, thus enhancing reliability. However, its
inherent complexity can pose a steep learning curve. Conversely, the EDAM relies on event-triggered
callbacks, offering simplicity and an intuitive developer experience. It's particularly effective in
I/O-bound tasks and user interfaces but less so in CPU-intensive scenarios. Challenges arise in managing
state across asynchronous calls and navigating "callback hell," potentially affecting code maintainability.
In terms of scalability, the Actor Model outperforms in distributed contexts, whereas the EDAM is more
apt for single-system setups. The choice hinges on the specific system requirements, balancing the
EDAM’s simplicity against the Actor Model's robustness and scalability, each catering to different aspects
of concurrency in software development.
The model, conceptualized by Carl Hewitt (as mentioned), revolutionized handling concurrency
in computing. Initially a theoretical framework, it gained prominence with the rise of distributed systems
and the need for robust parallel processing. Languages like Erlang, developed in the 1980s for telecom
systems, embodied its principles, demonstrating its practicality in building reliable, scalable applications.
Recent trends see the Actor Model integral to reactive programming, with frameworks like Akka and
Orleans, catering to modern distributed architectures. Looking ahead, its relevance is poised to grow with
the increasing demand for distributed, fault-tolerant systems in cloud computing and IoT applications.
Will the Actor Model, with its intrinsic scalability and robustness in concurrent and distributed
systems, become the cornerstone for future programming languages designed for the ever-expanding
cloud and IoT landscape? Its evolution could well dictate how we tackle the complexities of
next-generation, large-scale, real-time applications.
1. Wade & Gomaa, 2016. "Applied Akka Patterns". O'Reilly Media.
2. Metz, 2016. "Software Architecture Patterns". O'Reilly Media.
3. Vernon, 2015. "Reactive Messaging Patterns with the Actor Model: Applications and Integration
in Scala and Akka". Addison-Wesley Professional.Introduction
Memory management is crucial in programming language design, influencing how
resources are allocated and reclaimed. Automated Garbage Collection (AGC) and Manual
Memory Management (MMM) are two contrasting approaches, each impacting language
behavior and developer experience.
Feature Analysis: Automated Garbage Collection
AGC, used in Java and Python, automates memory management through algorithms like
Tracing and Reference Counting. This automation reduces the programmer's burden
significantly. Martin Heller in InfoWorld states, "using garbage collection can completely
eliminate the major memory allocation and deallocation issues" (1). Additionally, David Reilly
notes in Developer.com, "the automatic garbage collector of the JVM makes life much simpler
for programmers by removing the need to explicitly de-allocate objects" (3). These insights
highlight AGC's role in simplifying memory management and improving software reliability.
Comparative Analysis: Manual Memory Management
MMM in languages like C allows for optimized memory usage but at the risk of
increased errors such as "memory allocation bugs include...failing to release memory...attempting
to read or write through a pointer after the memory has been freed" (1). It poses scalability
challenges in larger applications due to its complexity. AGC enhances reliability and scalability,
but "the downside of garbage collection is that it has a negative impact on performance" (2).
AGC simplifies developer experience by reducing the burden of MMM, allowing for a focus on
application logic. In summary, MMM offers control and potential efficiency but increases
complexity and error risk, while AGC enhances reliability and developer ease at the expense of
performance.
Evolutionary Perspective
The evolution of AGC demonstrates a trajectory from basic memory management to
sophisticated, adaptive systems. Historically, AGC focused on elementary memory reclamation
but has since evolved to incorporate advanced techniques. A pivotal development in this journey
is the application of reinforcement learning to optimize garbage collection policies. As noted in
"Learned Garbage Collection", this approach represents a significant shift: "reinforcement
learning is applied to optimize garbage collection policies" (4) . This statement reflects a trend
towards AGC systems that are not only efficient but also adaptive to varying application
requirements, signaling a future where AGC becomes increasingly central and responsive within
programming language design.
Concluding Insight
As AGC integrates technologies like reinforcement learning, it prompts reflection on its
future trajectory. Could future AGC systems autonomously optimize themselves for specific
applications, revolutionizing memory management in programming languages?

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