This is a graduate-level look at how machines can debug and fix buggy code on their own — the core question is how to localize a fault automatically and then synthesize a patch that actually holds up, not just one that happens to pass the existing tests. You'll work through the four main repair families (heuristic, template, constraint-based, and learning-based) and spend most of the semester on a project, with a midterm checkpoint and final presentations driving the timeline. It sits at the intersection of software engineering, program analysis, and increasingly ML, and matters because patch quality and validation are exactly where modern AI coding tools still struggle.
→ STARS müfredatı (resmi syllabus)
https://w3.bilkent.edu.tr/bilkent/generative-artificial-intelligence-genai-guideline/
İlk dosyayı sen atarsan — not, slayt, geçmiş sınav, çözüm, cheat-sheet, ne varsa — defter ekibi öğrenci paylaşımlarından bu dersin notlarını yazar. Drive linki / PDF / ZIP, hepsi olur.
There is no final exam for this course, however, any one of the following will directly result in an F grade: (1) not submitting the term project or a survey paper, (2) being absent in the midterm, (3) being absent in the term project presentation.