π Keshav Three-Pass Reading Log
Keshavβs Three-Pass Method (S. Keshav, University of Waterloo, 2007):
- Pass 1 (5β10 min): birdβs-eye view using title, abstract, intro, headings, conclusions
- Pass 2 (β€1h): grasp content, note figures, mark unread references
- Pass 3 (4β5h): re-implement / virtually reconstruct the paper
[!info] Overview
Used in MIT AI Lab Working Paper 316 (βHow to Do Researchβ) as reading guidance for graduate students. Integrated here as the PUMA paper-reading framework.
Reading Queue (Pass 1 pending)
TABLE title AS "Title", first-author AS "Author", year AS "Year", relevance AS "β"
FROM #literature
WHERE status = "to-read"
SORT relevance DESCIn Progress (Pass 2)
TABLE title AS "Title", first-author AS "Author", year AS "Year"
FROM #literature
WHERE status = "reading"Completed β Ready for Permanent Notes (Pass 3 done)
TABLE title AS "Title", first-author AS "Author", year AS "Year", topic AS "Topic"
FROM #literature
WHERE status = "reviewed"
SORT year DESCAll Literature
TABLE title AS "Title", first-author AS "Author", year AS "Year", status AS "Status", relevance AS "β", topic AS "Topic"
FROM #literature
SORT relevance DESC, year DESCManual Log (for papers not yet in Dataview)
| Paper | Author | Year | Pass 1 | Pass 2 | Pass 3 | Permanent Note |
|---|---|---|---|---|---|---|
| Reproducibility of LLM Studies in SE | Angermeir | 2025 | β | β | β | PN-KeyConcepts-Agents-Reproducibility-RedTeam |
| CoGEE: Story Point Estimation | Tawosi | 2024 | β | β | π | PN-IssueTriage-StoryPoints |
| How Big Things Get Done | Flyvbjerg | 2023 | β | β | β | PN-IssueTriage-StoryPoints |
| Chain-of-Thought Prompting | Wei | 2022 | β | β | β | PN-CoT-FewShot-Prompting |
| TAWOS Dataset | Tawosi | 2022 | β | β | β | LN-Datasets-JiraSR-TAWOS |
| Design Science in IS Research | Hevner | 2004 | β | β | β | PN-DSR-SLR-Methods |
| Cognitive Agents for Agile PM | Spichkova | 2025 | β | π | β¬ | β |
| Jira SR Dataset | Ortu | 2015 | β | β | β | LN-Datasets-JiraSR-TAWOS |
| PM-LLM-Benchmark | Berti | 2024 | β | π | β¬ | β |
| AI in Project Management 2019β2024 | Manzoor | 2025 | β | β | π | β |
| Request Formats and Effort Estimation | Calikli | 2025 | β | β | β | PN-CoT-FewShot-Prompting |
| Energy and Policy for Deep Learning | Strubell | 2019 | β | β | β | Carbon-Tracking-Log |
| Experimentation in SE | Wohlin | 2012 | β | β | β | PN-Wilcoxon-FINER-Cornell-PRISMA |
| Local LLMs for Sprint Estimation | Yonathan | 2025 | β | π | β¬ | β |
| Language Models are Few-Shot Learners | Brown | 2020 | β | β | β | PN-CoT-FewShot-Prompting |
Legend: β Done Β· π In Progress Β· β¬ Not started
Keshav Pass-1 Quick Template
For fast capture during Pass 1, use:
**Paper**: [Title]
**Author**: [Last name, Year]
**Category**: measurement | analysis | description | proposal
**Context**: Related to [papers/theories]
**Correctness**: Assumptions [seem valid | questionable because...]
**Contributions**: 1) ... 2) ... 3) ...
**Clarity**: [clear | confusing | jargon-heavy]
**Decision**: Read Pass 2? [YES / NO β reason]
Permanent Notes Generated from Reading
LIST
FROM "30 - Permanent"
WHERE type = "permanent"
SORT file.ctime DESC
LIMIT 20