Computer Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Computer Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 05071350 | Compiler Desıgn | 4 | Autumn | 7 | 3+0+0 | 3 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Assoc. Prof. Ali ÖZTÜRK |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Assoc. Prof. Ali ÖZTÜRK | A-127 | [email protected] | 12 | Thursday 14.00-15.00 |
Course Content
This course explains the methods used to translate students from formal language to another formal language. The course describes the steps used from the scanner step to the design and development of the parser. In addition, the course provides information on semantic analysis and local and global compiler optimization. During the course, each student is expected to design a simple compiler using lex and yacc software tools
Objectives of the Course
The aim of this course is to provide students with knowledge and skills in compiler design and development.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | X |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Knows the applications of mathematics in engineering | P.1.4 | |
| O2 | Knowledge of algorithm design and analysis techniques. | P.3.1 | 1 |
| O3 | Conducting software tests according to norms. | P.2.20 | 7 |
| O4 | Predictive modeling | P.2.21 | 1,7 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction to Compilers |
| 2 | Scanners (Regular Languages, Lexical Features) |
| 3 | Separators (Cfgs, Decomposition Tree, Top-Down. |
| 4 | Context Sensitive Analysis |
| 5 | Midterm |
| 6 | Interim Representations |
| 7 | Procedure Abstraction |
| 8 | Code Format, Optimizations I (Local Optimizations) |
| 9 | Optimizations II (Global Optimizations) |
| 10 | intermediate representations II |
| 11 | Target Code Generation |
| 12 | Machine Independent Code Optimization |
| 13 | Command Level Parallel Processing |
| 14 | Final |
Textbook or Material
| Resources | Aho, Sethi, Ullman, "Compilers: Principles, Techniques, and Tools 2E", Addison-Wesley, 2006 |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 5 | 70 |
| Midterms | 1 | 3 | 3 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 3 | 3 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 118 | ||
| Total Work Load / 30 | 3,93 | ||
| Course ECTS Credits: | 4 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P2 | P3 |
|---|---|---|---|---|
| O1 | Knows the applications of mathematics in engineering | 4 | - | - |
| O2 | Conducting software tests according to norms. | - | 5 | - |
| O3 | Predictive modeling | - | - | 3 |
| O4 | Knowledge of algorithm design and analysis techniques. | 2 | - | - |