In this course you will be introduced to the fundamentals of C programming, the Linux operating system environment, development of computational tools for aerospace analysis and design, and technical report writing. The emphasis of the course is on learning best practices that form the basis for modern professional engineering. Therefore, the course will use the tools and processes that are most common in industry today, emphasizing good documentation and version control.

This course is intended to be a fun, interactive introduction to applying computational analysis in the context of real-world systems. Hands-on learning, through the use of industrial techniques in labs/homeworks, will be emphasized. We will learn the computational tools and techniques used at NASA, Boeing, Rockwell Collins, Honeywell, Airbus, Mathworks, and others with an emphasis on professional workflow and engineering best practices.

Course Syllabus and University Integrity Policy

Required; Use this for: git and github bash scripting linux operating system/command line overview debugging with gdb makefiles, libraries, linking, and build management with make prototyping machine numbers and the IEEE 754 floating-point standard

Required; Use this for: all things related to C programming! scientific computing implementation

Optional; Use this for: emacs bash scripting linux operating system/command line overview regular expressions, pattern matching awk and sed

Optional; Use this for: awk and sed

Optional; Use this for: make

Optional; Use this for: supplemental material and exercises related to C programming This is one of many optional supplemental books available. Do not buy this book; it is available free through ISU library!

Optional; Use this for: supplemental material related to computing (versus calculating), memory, and computer organization (and how these affect the execution, performance, and correctness of programs) This is one of many optional supplemental books available. This (short) book is available free HERE!

	Git (and github) http://github.com/ Ubuntu Linux: Run the following command in a terminal: sudo apt-get install git-all Mac OS: Download HERE Windows: Download HERE Additional info: if you want a deeper understanding of git, there are many online courses.
	LaTeX https://www.latex-project.org/ Ubuntu Linux: Run the following command in a terminal: sudo apt install texlive-latex-extra Here is a nice tutorial from NASA! Mac OS: Download HERE Windows: Download MikTeX or proTeXt or TeX Live Kile GUI (for any OS): Download HERE
	gcc, the GNU Compiler Collection https://gcc.gnu.org/ Ubuntu Linux: Run the following command in a terminal: sudo apt install gcc Mac OS: Download HERE Windows: Download HERE
	GNU Emacs https://www.gnu.org/software/emacs/ Ubuntu Linux: Natively installed; for issues read HERE Mac OS: Download HERE Windows: Download HERE
	GNU DataDisplayDebugger https://www.gnu.org/software/ddd/ GNU DDD is a graphical front-end for command-line debuggers such as GDB, the bash debugger bashdb, the GNU Make debugger remake, or the Python debugger pydb. Besides ``usual'' front-end features such as viewing source texts, DDD has become famous through its interactive graphical data display, where data structures are displayed as graphs. See also dynamic analysis tools like Valgrind: a memory analysis/pointer debugger and Callgrind: a call-graph generator.

Lab 2 (Scripting with Bash):
data.tar.gz file needed for the lab is HERE
Demo: The Power of Dot Files
Demo: Debugging Bash Scripts

Lab 8 (The Matrix):
Reading for this lab:
Introduction to Scientific and Technical Computing:
Chapter 15: Libraries for Linear Algebra
C Programming Language, 2nd Edition:
Section 5.7
Lecture: The Matrix, Part 1
Lab Slides: The Matrix (PPTX)
Lecture: A Complete Input Validation Library!

Lab 9 (Systems of Linear Equations):
Introduction to Scientific and Technical Computing:
Chapter 15: Libraries for Linear Algebra
C Programming Language, 2nd Edition:
Section 5.7
Lab Video
Lecture: Sparse Matrices, Part 1: Introduction and COO Algorithm
Lecture: Sparse Matrices, Part 2: Coding COO
Lecture: Sparse Matrices, Part 3: CSR
Slides from Sparse Matrix Lectures COO + CSR

Lab 11 (Circuit Solver):
Reading for this lab: C Programming Language, 2nd Edition: Section 6.6 Table Lookup
Lecture: Introduction to Circuit Solvers: Laying out the Algorithm
Lab Video
Lab Video Transcript
Lecture: Parse My CSV into Current and Voltage Equations

Lab 12 (ODE Solver):
Reading for this lab: C Programming Language, 2nd Edition:
Lecture: Part 1
Lecture: Part 2
Lecture: Part 3
Lecture: Part 4
Lab Video
Lab Video Transcript
Lecture: Algorithm

• LaTeX Symbols Guide: The ultimate symbol list! And the 2021 Edition
• LaTeX Wikibook: Good general purpose reference manual
• LaTeX for Logicians: Symbols of logic and proofs!
• Detexify: Draw your symbol!
• LaTeX Table Generator: Automatically generate LaTeX tables!
• LaTeX Cheat Sheet: All the basics on one (two-sided) page!

• Big-O Complexity Cheat Sheet

Feedback is encouraged! You can help with the continuous improvment goals of this course by providing feedback like suggestions for in-class exercises or corrections to typos in the lab manual. Note that feedback needs to folow the ISU guidelines for providing useful and constructive feedback -- sending feedback in the form of insults about the personal characteristics of the TAs or professor will not be tolerated; vague over-generalizations like "did not make sense to most of the class" are not helpful. Instead, send your personal feedback including constructive comments like "I am struggling the most with debugging, particularly with pointers, so could you please add another in-class exercise where we debug programs with memory errors together to learn more effective debugging strategies." Here is another example: rather than sending the (unhelpful) comment "the lab manual isn't detailed enough" send a comment like "The textbook chapter did not have any examples of X and I found it hard to come up with one from the general definition there, so could you please add an example demonstrating the construction of X in Exercise 2 of the lab manual?"

• Static Code Analysis is a (semi-)formal method for automatically analyzing C programs with verified tools that find bugs and security vulnerabilities. Splint was previously used by students in this class, however there are many Static Source Code Analysis Tools for C, even one for Matlab/Simulink.
• Software Model Checking is a formal method for proving properties about programs. There is a Competition on Software Verification (SV-COMP) that links to downloads and benchmarking data on all of the latest software model checking tools.
• NASA BugView (ARC-16790-1): Bugview is a Web-based graphical user interface (GUI) service that provides software developers and analysts the ability to configure and execute off-the-shelf static code analysis tools and securely manage imported code releases and analysis results. The service: 1) presents a single interface from which multiple static code analysis tools can be configured and executed; 2) offers a means to automate consistent periodic analysis of each code release; 3) affords the capability to track code changes and identified code issues through progressive build releases: and 4) provides tools for identifying and rejecting false-positive results.
• Exploiting Defect Prediction for Automatic Software Repair (Fixie): Software is now at the heart of almost everything we do in the world. This software remains largely handmade, and as such, is prone to defects. Testing detects only a sub-set of software defects with the rest laying dormant, sometimes for years. When these defects emerge in software systems the safety and business consequences can be severe. Software failures and their damaging consequences are regularly reported in the press. Finding and fixing defects has been an intransigent problem over many years. The traditional approach to this problem relies on finding defects during testing then developers manually fixing those defects afterwards. In this project we establish a new technique to automatically fix predicted defects in software code before testing.
• The Cerberus project is developing semantic models for a substantial fragment of C. The Cerberus web interface lets one interactively, randomly, or exhaustively explore the behaviour of small sequential C test programs in the Cerberus semantics. The Cerberus BMC web interface lets one explore the behaviour of small concurrent C test programs with respect to an arbitrary axiomatic concurrency model.
• AddressSanitizer (ASan) is a memory error detector for C/C++ that can integrate with gdb. It finds: (1) use after free (dangling pointer dereference); (2) heap buffer overflow; (3) stack buffer overflow; (4) global buffer overflow; (5) use after return; (6) use after scope; (7) initialization order bugs; (8) memory leaks. AddressSanitizer is the original version, though many variants are openly available.

Check out NASA's Software Catalog HERE .

NASA offers several related activities aimed at a K-12 audience.

• Code a Mars Landing Technology Project | NASA JPL Education: https://www.jpl.nasa.gov/edu/resources/project/code-a-mars-landing/
• NASA's Space Jam: https://chandra.si.edu/sound/code/

Learn Python 3 the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code (a book for total beginners)