Archive 10 academic presentations from ~/Downloads/slides/ (2014-2018)

- PhD defense slides (defense.key, Nov 2018) → phd_defense/
- Master's defense on MOOC peer evaluation (Dec 2014)
- ENGI 600 data-driven program repair (Apr 2015)
- COMP 600 data-driven program completion (Fall 2015, Spring 2016)
- COMP 600 Program Splicing presentation + feedback + response (Spring 2018)
- Program Splicing slides in .key and .pdf formats (Spring 2018)

Each file has a .md transcription with academic frontmatter.
Skipped www2015.pdf (duplicate of existing www15.zip) and syncthing conflict copy.

documents/academic/presentations/splicing_comp600_2018.md

@@ -0,0 +1,31 @@
+---
+category: academic
+type: academic
+person: Yanxin Lu
+date: 2018-05
+source: splicing_comp600_2018.key
+---
+
+# Program Splicing — COMP 600 Spring 2018 (Keynote)
+
+Yanxin Lu, Swarat Chaudhuri, Christopher Jermaine, David Melski. Keynote presentation, Spring 2018.
+
+Source Keynote file for the Program Splicing COMP 600 presentation. The PDF export is available as splicing_comp600_2018.pdf.
+
+The presentation covers the same content as splicing_comp600_slides_2018.pdf but is a slightly revised version with subtitle "Data-driven Program Synthesis" on the title slide:
+
+1. Copying and Pasting problem — time consuming and introduces bugs
+2. Program Synthesis — automatically generating programs from specifications
+3. Problem — can we use program synthesis to improve copy-paste?
+4. Related work — Sketching (PLDI 2005), Code Transplantation (ISSTA 2015)
+5. Program Splicing approach — automate copying/pasting using 3.5M program corpus, ensure correctness
+6. Architecture — draft program → Synthesis ↔ PDB → completed program
+7. PDB — 3.5M Java programs, natural language features, similarity metrics, KNN search, fast top-k query
+8. Relevant programs — query PDB with draft program to find similar implementations
+9. Filling holes — enumerative search over candidate expressions from relevant programs
+10. Variable renaming — resolve undefined variables
+11. Testing — filter incorrect candidates via unit tests
+12. Heuristics — type and context-based pruning for search space reduction
+13. Benchmark — 12 programs, synthesis times 3–161 seconds, efficient algorithm
+14. User study — 18 participants (12 grad students + 6 professionals), 4 problems, splicing most helpful for algorithmic tasks and tasks without standard solutions
+15. Conclusion — data-driven synthesis with large corpus, enumerative search, efficient algorithm, fast code reuse