b85169f4e7
- 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.
65 lines
2.6 KiB
Markdown
65 lines
2.6 KiB
Markdown
---
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category: academic
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type: academic
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person: Yanxin Lu
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date: 2018-05
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source: splicing_comp600_2018.pdf
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---
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# Program Splicing — COMP 600 Spring 2018 (PDF Export)
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Yanxin Lu, Swarat Chaudhuri, Christopher Jermaine, David Melski. 31 slides.
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PDF export of the Keynote presentation splicing_comp600_2018.key. Title: "Program Splicing: Data-driven Program Synthesis".
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This is a revised version of the earlier splicing_comp600_slides_2018.pdf. Key differences:
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- Title slide has subtitle "Data-driven Program Synthesis" (vs just "Presented by Yanxin Lu")
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- Adds "Efficient relevant code retrieval" and "KNN search" to PDB slide
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- Adds "Programming time" to user study setup
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- User study result slides titled differently: "Deceptively simple", "No standard solutions", "Good documentations and tests were hard to write"
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- Conclusion adds "Efficient algorithm", "Fast code reuse", "Easy to test", "Future work: synthesis algorithm improvement"
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## Slide 2: Title
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Program Splicing: Data-driven Program Synthesis
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## Slides 3–7: Motivation and Approach
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- Copying and pasting is time consuming and introduces bugs
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- Program synthesis: automatically generate programs from specifications
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- Problem: can we use program synthesis to improve copying and pasting?
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- Related work: Sketching (PLDI 2005), Code Transplantation (ISSTA 2015)
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- Program Splicing: automate process, large corpus (3.5M programs), ensure correctness
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## Slide 8: Demo
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- How does a programmer use program splicing?
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## Slides 9–12: Architecture
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- User → draft program → Synthesis ↔ PDB → completed program
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- PDB: efficient relevant code retrieval, 3.5M Java programs, NL features, similarity metrics, KNN search, fast top-k query
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## Slides 13–18: Synthesis Algorithm
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- Find relevant programs from PDB
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- Fill holes via enumerative search
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- Variable renaming for undefined variables
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- Testing to filter incorrect programs
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## Slides 19–20: Benchmark
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Same benchmark table as the earlier version. Efficient synthesis algorithm highlighted.
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## Slide 21: No need to write many tests
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## Slides 22–26: User study
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- 18 participants, 4 problems, programming time measured
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- Sieve: deceptively simple
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- Files/CSV: no standard solutions — splicing most helpful
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- HTML: good documentation and tests were hard to write
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## Slide 27: Conclusion
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- Program Splicing: large code corpus, enumerative search, efficient algorithm
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- Fast code reuse: no standard solutions, easy to test
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- Future work: synthesis algorithm improvement
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## Slides 29–31: Appendix (Heuristics)
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- Type-based pruning: ignore incompatible types
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- Context-based pruning: ignore expressions with no common parents
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- Huge search space reduction
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