Proceedings of the 2nd Workshop on Managing Technical Debt
Bibliographic record
Abstract
Welcome to the Second International Workshop on Managing Technical Debt, MTD 2011, co-located with the 33rd International Conference on Software Engineering at Waikiki, Honolulu, Hawaii! The first workshop on technical debt was held at the Software Engineering Institute in Pittsburgh on June 2 to 4, 2010 with the goal of understanding open research questions related to managing technical debt in software. The goal of this second workshop is to come up with a more in-depth understanding of technical debt, its definition(s), characteristics, its different forms. For this second workshop we accepted 3 research and 7 position papers. The papers were selected after a peer review by at least three members of the program committee. The accepted submissions cover a range of topics such as: monitoring and visualizing code quality, relationship of technical debt and maintainability, an economic model for technical debt and interest, software architecture related technical debt, and definitional foundations of technical debt. Managing technical debt is a broad concern of software engineering that blends research and practice. This can be seen from the programme and those involved in the workshop programme selection process. To encourage interactive discussion, foster brainstorming, community building the workshop will consist of only short presentations from the accepted papers. These short presentations will provide a basis for the participants to investigate further open research questions and challenges in practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".