Loops

Reasoning

Data Abstraction

Representation Invariants (RIs)

Hashing

Exception handling

Generics

HTTP Servers

JSON

Abstract Data Type (ADT)

Software testing

Equality

Module Design & Style

User interface

React

Web Clients

Debugging

Specifications

Abstraction Functions (AFs)

Identity (object-oriented programming)

Assertion (software development)

Subtypes

Event-Driven Programming

TypeScript

Software design pattern

JavaScript

CSE 331 Software Design & Implementation University of Washington Spring 2022 This course aims to improve students' software development skills, focusing on building correct, scalable, and easy-to-understand software. It relies on a solid understanding of Java and the concepts covered in CSE 143. Students will learn about software design principles, modern programming languages, and software tools. It covers software testing, type systems, design patterns, and more. The aim of this course is to help students write programs of higher quality and increased complexity. This requires creating software that is correct, easy to change, easy to understand, and easy to scale. The course covers principled approaches to achieving each of these. The aim of this course is to help students write programs of higher quality and increased complexity. This requires creating software that is (1) correct, (2) easy to change, (3) easy to understand, and (4) easy to scale. We will study principled approaches to achieving these four goals.

After completing this course successfully students should be able to:

- Successfully build medium-scale software projects in principled ways
- Understand the role of specifications and abstractions and how to verify that an implementation is correct, including effective testing and verification strategies and the use of formal reasoning
- Analyze a software development problem and be able to design effective program structures to solve it, including appropriate modularity, separation of abstraction and implementation concerns, use of standard design patterns to solve recurring design problems, and use of standard libraries
- Use modern programming languages effectively, including understanding type systems, objects and classes, modularity, notions of identity and equality, and proper use of exceptions and assertions
- Use and learn new contemporary software tools, including integrated development environments, test frameworks, debuggers, version control, and documentation processing tools

In this class, we will use Java and associated tools like IntelliJ, JUnit, JavaDoc, and git, but the goal is to understand the underlying ideas and concepts that are generally applicable to software construction. The only formal prerequisite is CSE 143. We will very much rely on a thorough understanding of the concepts from CSE 143 as well as Java programming skills. We will go much further and you will be challenged to approach software development much differently than you have in CSE 143 or other courses. ### Texts

The required text for the course is the following:

- Effective Java by Joshua Bloch, 3rd ed., Addison-Wesley, 2018.

Contains a great deal of distilled wisdom about Java best practices, technical details, and style issues.

There will be assigned readings from this book during the quarter, and there will be questions on the exams about those readings.

Besides the required text, the following books may be helpful to you:

- The Pragmatic Programmer by Andrew Hunt and David Thomas, 20th anniversary (2nd) edition, Addison-Wesley, 2020.

Contains a great deal of distilled wisdom about software construction and the craft of programming, including many things that those who read the book later in their careers wish they had learned early on.

- Program Development in Java by Barbara Liskov and John Guttag, 1st ed, Addison Wesley, 2000.

The content of this well-written book closely mirrors much of the content of the course. The only downside is that it was written 17 years ago, so it is out-of-date with regard to some of key parts of the Java language and libraries. Nonetheless, it is a good place to look for another presentation of the material covered in class, if you can ignore those parts.

- Core Java Vol. I - Fundamentals by Cay Horstmann, 10th ed, Prentice-Hall, 2016; 11th ed, 2018.

This is a good general reference for the Java language and basic libraries. While technical documentation for these things is available on the web, the discussions in a book like this one are usually easier to follow and typically include advice not found in the formal documentation. Either recent edition will cover what we need for CSE 331.

CSE 331 Software Design & Implementation

Software Engineering

JoeQ Framework

Partial Redundancy Elimination

Dataflow Analysis

Constant Propagation

Register Allocation

Instruction Scheduling

Software Pipelining

Parallelization

Loop Transformations

Pipelined Parallelism

Pointer Analysis

BDDs in Pointer Analysis

Satisfiability Modulo Theories (SMT)

Garbage Collection

Natural Language Programming

CS 243: Program Analysis and Optimizations Stanford University Winter 2023 Focuses on efficient high-level programming through code optimization and program analysis for quality improvement. Also explores automatic memory management and natural language coding through machine learning. ## What do we teach?

- How do we make high-level programming languages efficient by optimizing the code? (Data-flow analysis)
- Machine learning and scientific computing need a lot of cycles; machines such as GPUs are complex to code. How do we automatically generate efficient code for these machines effectively. (Parallelism and Locality)
- Can we use program analysis to improve the quality of the code such as detecting security bugs in programs? (Pointer analysis, Satisfiability Modulo Theories)
- How do we automatically manage memory efficiently so users do not have to manage it themselves? (Garbage collection)
- The highest programming language is obviously natural language. With the recent introduction of large language models such as GPT3 and CodeX, how do we let consumers code in natural language using machine learning? Note: no prior knowledge in machine learning is needed. (Neural networks)

## How do you learn this?

As compilers are one of the most complex programs we write, by teaching compilers, we are also teaching software engineering. It is a course where programming and mathematics meet. We show how to apply general mathematical concepts (fixpoint computations, graph theory, linear algebra, binary decision diagrams, neural networks) to handle various complex programming problems. You will learn how to formulate solutions to problems, and not just memorizing prior work. ## Course Emphasis

- **Methodology: apply the methodology to other real life problems**
    - Problem statement
        - Which problem to solve?
    - Theory and Algorithm 
        -Theoretical frameworks 
        - Algorithms
    - Experimentation: Hands-on experience (Weekly programming/written homeworks)
- **Compiler knowledge:**
    - Non-goal: how to build a complete optimizing compiler
    - Important algorithms
    - Exposure to new ideas
    - Background to learn existing techniques CS 103 or CS 103B, and CS 107; Java programming language experience 

CS 243: Program Analysis and Optimizations

Compilers

Abstraction (computer science)

For Loop

Environment Diagrams

Dictionaries

Recursion (computer science)

Object-Oriented Programming (OOP)

Mutable Data

Linked list

Tree (data structure)

Generators

Databases

Function (computer programming)

List (abstract data type)

Higher-order function (HOF)

Lambda

Tree Recursion

Inheritance (object-oriented programming)

Efficiency

Iterators

Programming Paradigms

CS 88: Computational Structures in Data Science UC Berkeley Fall 2022 This course provides an introduction to computer science with an emphasis on functional programming, data abstraction, object-oriented programming, and program complexity. The course teaches foundational programming concepts primarily in Python. CS 88 is a "connector" for Data 8 that is designed for students who would like a more complete introduction to Computer Science. We will cover a variety of topics such as functional programming, data abstraction, object-oriented programming, and program complexity. This course will be taught primarily in Python. However, we are interested in teaching you foundational programming ideas, not just how to use one particular programming language. Once you have learned the essence of programming and the concepts that appear in various forms in programming languages, you will be able to pick up other languages and other programming concepts rapidly.

The material for this course has significant overlap with CS 61A. We do not cover the interpretation section of CS61A. If you are certain that you want to major or minor in Computer Science, CS 61A is the right introductory course. You can take CS 61A for credit after having taken CS 88. However, you cannot take CS 88 for credit after having taken CS 61A. Data 8 and CS 88 together satisfy the knowledge prerequisite for CS 61B.  It is recommended, but not required, that you are currently enrolled in or have taken Data 8. There is no formal programming-related prerequisite for CS 88. You do not need to be familiar with any particular programming language. The online textbook for the course is [Composing Programs](http://composingprograms.com/), which was created specifically for CS61A but is also the best basis for this course. Readings for each lecture appear in the course schedule. We will be jumping around a little due to the data-centric orientation of the course. You should complete the readings before attending lecture.

In addition, the course website and Piazza will contain additional links to guides, handouts, and practice materials available for the course.

CS 88: Computational Structures in Data Science

Computer Programming

Data Structures and Algorithms

Regular Expressions from Axioms

Induction

Propositional Encodings

SAT Certificates

Contracts

Dynamic Logic

Convergence

DPLL

Blasting & Quantifiers

Data structure

Compositional Reasoning

Predicate transformer semantics

Sound Transformations

Temporal logic

Ghosts

Sequent Calculus

Semantics

Array (data structure)

Resolution

Equality and Arrays

15-414 Bug Catching: Automated Program Verification Carnegie Mellon University Spring 2022 This course is about software verification, with the goal of writing bug-free code. Students will learn to formalize program correctness, write verified code, and use automated tools for verification. It explores the principles behind verification tools, logical specifications, and deductive reasoning. Previous knowledge in program correctness reasoning is beneficial. High-profile bugs continue to plague the software industry, leading to major problems in the reliability, safety, and security of systems. This course teaches students how to write bug-free code through the process of software verification, which aims to prove the correctness of a program with respect to a mathematical specification. Along the way, students will learn how to:

- Specify correct program behavior
- Prove the correctness of their code
- Use formal semantics to reason about the soundness of proof rules
- Write practical and efficient verified code
- Use decision procedures and model checkers to reduce verification effort

Students will learn the principles and algorithms behind automated verification tools, and understand their practical limitations while gaining experience writing verified, machine-checked code that solves real problems. **Identify and formalize program correctness.** In order to reason about the existence of bugs in code, and ultimately justify their absence, we need to state what it means for the code to be correct. Students will learn how to formalize correctness using logical specifications that give precise meaning to the program's intended behavior.

**Understand how to write correct software from the ground up.** Writing good specifications of program correctness is essential to showing that there aren't any bugs in the code, but it is also useful for writing good code from the start. Students will develop experience reasoning about correctness as they implement functionality, learning how to write code that establishes and preserves properties and invariants needed to meet a target specification.

**Apply rigorous reasoning to program correctness.** Program verification is the process of mathematically proving that all possible behaviors of a given program adhere to its specification. Students will learn how to use deductive reasoning and state space exploration to realize this goal, and gain experience verifying their own code against logical correctness specifications.

**Learn to use automated tools to aid in verification.** Formally verifying real code is challenging, but given the scale of modern software, can also be tedious and labor-intensive. A number of tools exist for automating key parts of the verification process, notably deductive verification tools and model checkers. Students will learn how to use these tools when developing correct software, and gain experience applying them to non-trivial implementations.

**Understand how verification tools work.** Verification tools are not perfect, and will never be able to scale universally to all software development needs. Understanding the principles and techniques that these tools use is necessary for evaluating when it is appropriate to use a particular tool, and for addressing a tool's limitations when they arise in practice.  There is no textbook for this class, and detailed lecture notes will be posted to the website within a few days after each class. Keep in mind that lecture notes may not contain everything we cover in class. Comments and corrections are welcome, please give them to the course staff on Piazza.

15-414 Bug Catching: Automated Program Verification

Static Single-assignment (SSA)

Control-flow Graph (CFG)

Basic Blocks

Instruction Selection

Middle End

Dataflow Theory

Lexing

Parsing

Typechecking

Static Semantics

Calling Conventions

Dynamic Semantics

Mutable Store

struct (C programming language)

Locality

Alias Analysis

15-411 Compiler Design Carnegie Mellon University Fall 2020 Comprehensive study of compiler design and implementation, examining interaction between language design and runtime organization. Topics include program analysis, code generation, optimization, memory management. This course covers the design and implementation of compiler and runtime systems for high-level languages, and examines the interaction between language design, compiler design, and runtime organization. Topics covered include lexical and syntactic analysis, type-checking, program analysis, code generation and optimization, memory management, and runtime organization. - Distinguish the main phases of a state-of-the-art compiler
- Understand static and dynamic semantics of an imperative language
- Develop parsers and lexers using parser generators and combinators
- Perform semantic analysis
- Translate abstract syntax trees to intermediate representations and static single assignment form
- Analyze the dataflow in an imperative language
- Perform standard compiler optimizations
- Generate efficient assembly code for a modern architecture
- Allocate registers using a graph-coloring algorithm
- Understand opportunities and limitations of compiler optimizations
- Appreciate design tradeoffs how representation affects optimizations
- Automatically manage memory using garbage collection
- Develop complex software following high-level specifications  

15-411 Compiler Design

Object (computer science)

Polymorphism (computer science)

Interface (object-oriented programming)

Graphical User Interface (GUI)

Big-O notation

Sorting algorithm

Search algorithm

Stack (abstract data type)

Queue (abstract data type)

Set (abstract data type)

CSCI 0150: Introduction to Object-Oriented Programming and Computer Science Brown University Fall 2022 CS0150 introduces Computer Science via object-oriented design and programming using Java and JavaFX for creating interactive programs with GUIs. Concepts like data structures, algorithms, and computational efficiency are explored. Practical exercises include engaging programming assignments like Doodle Jump and Tetris. The course is designed for all, requiring no prior knowledge. This course introduces students to Computer Science through object-oriented design and programming, using Java and the JavaFX graphics library. You will use these tools for building interactive programs with graphical user interfaces. CS0150 reinforces concepts with practical exercises in weekly lab sessions and with challenging and engaging programming assignments, such as Doodle Jump and Tetris!

There are no prerequisites for CS0150 and the course expects no prior programming experience. This course introduces principles of computer science, emphasizing object-oriented design and programming in Java, an effective modern technique for producing modular, reusable, and internet-aware programs. It also introduces interactive 2D computer graphics, user interface design, some fundamental data structures and algorithms, and computational efficiency. A sequence of successively more complex graphics programs, including Tetris and culminating in a significant final project, helps provide a serious introduction to the field. Each assignment includes an interactive user interface. Therefore, most of the games you program can be played! However, this course is not focused on games nor game design. It uses games as a natural and fun vehicle for learning object-oriented programming and graphical user interfaces.  The course takes an interactive approach to programming assignments and an equally interactive approach to lectures. Andy’s lectures are supplemented by skits performed by the UTAs (Undergraduate Teaching Assistants) to teach course concepts and to make the class fun and enjoyable!  This course is intended for both potential concentrators and those who may take only a single course. No prerequisites and no prior knowledge of programming and mathematics, beyond basic algebra, are required.  

CSCI 0150: Introduction to Object-Oriented Programming and Computer Science