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_pdf.md

@@ -0,0 +1,64 @@
+---
+category: academic
+type: academic
+person: Yanxin Lu
+date: 2018-05
+source: splicing_comp600_2018.pdf
+---
+
+# Program Splicing — COMP 600 Spring 2018 (PDF Export)
+
+Yanxin Lu, Swarat Chaudhuri, Christopher Jermaine, David Melski. 31 slides.
+
+PDF export of the Keynote presentation splicing_comp600_2018.key. Title: "Program Splicing: Data-driven Program Synthesis".
+
+This is a revised version of the earlier splicing_comp600_slides_2018.pdf. Key differences:
+- Title slide has subtitle "Data-driven Program Synthesis" (vs just "Presented by Yanxin Lu")
+- Adds "Efficient relevant code retrieval" and "KNN search" to PDB slide
+- Adds "Programming time" to user study setup
+- User study result slides titled differently: "Deceptively simple", "No standard solutions", "Good documentations and tests were hard to write"
+- Conclusion adds "Efficient algorithm", "Fast code reuse", "Easy to test", "Future work: synthesis algorithm improvement"
+
+## Slide 2: Title
+Program Splicing: Data-driven Program Synthesis
+
+## Slides 3–7: Motivation and Approach
+- Copying and pasting is time consuming and introduces bugs
+- Program synthesis: automatically generate programs from specifications
+- Problem: can we use program synthesis to improve copying and pasting?
+- Related work: Sketching (PLDI 2005), Code Transplantation (ISSTA 2015)
+- Program Splicing: automate process, large corpus (3.5M programs), ensure correctness
+
+## Slide 8: Demo
+- How does a programmer use program splicing?
+
+## Slides 9–12: Architecture
+- User → draft program → Synthesis ↔ PDB → completed program
+- PDB: efficient relevant code retrieval, 3.5M Java programs, NL features, similarity metrics, KNN search, fast top-k query
+
+## Slides 13–18: Synthesis Algorithm
+- Find relevant programs from PDB
+- Fill holes via enumerative search
+- Variable renaming for undefined variables
+- Testing to filter incorrect programs
+
+## Slides 19–20: Benchmark
+Same benchmark table as the earlier version. Efficient synthesis algorithm highlighted.
+
+## Slide 21: No need to write many tests
+
+## Slides 22–26: User study
+- 18 participants, 4 problems, programming time measured
+- Sieve: deceptively simple
+- Files/CSV: no standard solutions — splicing most helpful
+- HTML: good documentation and tests were hard to write
+
+## Slide 27: Conclusion
+- Program Splicing: large code corpus, enumerative search, efficient algorithm
+- Fast code reuse: no standard solutions, easy to test
+- Future work: synthesis algorithm improvement
+
+## Slides 29–31: Appendix (Heuristics)
+- Type-based pruning: ignore incompatible types
+- Context-based pruning: ignore expressions with no common parents
+- Huge search space reduction