Fall 2026

Testing and Testable Design of Digital Systems

ECE 553 covers fault modeling, test generation, simulation, and design for testability in digital systems.

offering recorded3 credits
Recorded instructors · Fall 2026 Azadeh Davoodi3.9/5

Summary

1 / 6

Workload is manageable, but tests are incredibly long and slightly difficult, with tricky midterm questions.

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

COMP SCI/​E C E 352 and COMP SCI 300, graduate/professional standing, or member of Engineering Guest Students

ECE 553

This is a best-effort interpretation; check the catalog requirements above.

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Professors

Fall 2026
/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 4.42/5 from 12 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 3.7/5 raw quality · 2.7/5 difficulty · 3 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Azadeh Davoodi cares about student learning and is generally nice, though her first-time teaching of this course caused initial hiccups. Reviewers note that while the material is straightforward, her lectures can be heavy, dry, and confusing due to static slides. She is a lenient grader who wants students to succeed, but exams are often lengthy and challenging.

Recent recorded grades — Fall 2022: 3.29 GPA, 45.5% A/AB (n=33 letter grades); Spring 2024: 3.50 GPA, 72.2% A/AB (n=18 letter grades); Spring 2025: 3.53 GPA, 69.6% A/AB (n=46 letter grades).

Historical instructors & teaching patterns

Historical reviews for Kewal Saluja are mixed. One reviewer found tests challenging but doable, while others noted lenient grading and helpfulness regarding digital testing. Confusion arose from Saluja's lecturing style and PowerPoint materials in one account.

AZADEH DAVOODI is recorded teaching in Fall 2022, Spring 2024, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

KEWAL SALUJA is recorded teaching in Fall 2006, Fall 2007, Fall 2008, Fall 2009, Fall 2010, Fall 2011, Fall 2012, Fall 2013, Fall 2014. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Fall 2026

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SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction39 / 600

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Azadeh Davoodi teaches a useful and interesting course with manageable workload, though her first-time instruction led to confusing slides and lengthy exams.

Recent recorded grades — Fall 2022: 3.29 GPA, 45.5% A/AB (n=33 letter grades); Spring 2024: 3.50 GPA, 72.2% A/AB (n=18 letter grades); Spring 2025: 3.53 GPA, 69.6% A/AB (n=46 letter grades).

difficulty & workload

Workload is manageable, but tests are incredibly long and slightly difficult, with tricky midterm questions.

Lectures can be dry and confusing due to repeated slides, but Davoodi is a lenient grader who wants students to learn.

Topics

  • Test generation and simulation for combinational and sequential circuits.
  • Testing and fault location for memory and microprocessor systems.

Skills

  • Analyze faults, generate tests, and apply design for testability techniques to digital systems.

Grades

Latest available · Spring 2025— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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AB
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BC
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Grades over time

Through Spring 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2025 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1289 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
ECE 553 · Fall 2026

Testing and Testable Design of Digital Systems

Recorded 2026-09-07
Raw records
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:320:006433",
    "course_id": "ECE 553",
    "course_uid": "course_41c983685dba88b3038a699b",
    "term_id": "1272",
    "source_course_id": "006433",
    "source_subject_id": "320",
    "title": "Testing and Testable Design of Digital Systems",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-09-07

Download the original dataset
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "b6bb2a7e555e801fd318aa33180e1d28fff784d02575159b67fd199779c19fb7",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}