| --- |
| license: unknown |
| task_categories: |
| - tabular-regression |
| tags: |
| - software-engineering |
| - technical-debt |
| - sonarqube |
| - code-quality |
| - mining-software-repositories |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # Technical Debt Regression Dataset |
|
|
| One row per `(repository_id, artifact_id, t0)` -- a single Java file, at a |
| weekly snapshot, in one of the source repositories. Built by scanning each |
| repo's history week-by-week with SonarQube and pairing each snapshot's |
| features with the technical debt that file has 3 weeks / 1 month / 3 months |
| later. |
|
|
| ## Repositories covered |
|
|
| | repository_id | Source | |
| |---|---| |
| | OFBiz | apache/ofbiz-framework | |
| | SystemML | apache/systemds | |
| | Groovy | apache/groovy | |
| | NiFi | apache/nifi | |
| | Guava | google/guava | |
| | OkHttp | square/okhttp | |
| |
| Each repo was scanned weekly (see `t0` per row) starting `2015-01-01`, for |
| 60 weeks, on its default branch at scan time (`trunk` for OFBiz, `main` for |
| SystemML/NiFi/OkHttp, `master` for Groovy/Guava). |
| |
| ## Quick start |
| |
| ```python |
| import pandas as pd |
| df = pd.read_csv("hf://datasets/<your-username>/td-regression-mart/combined_td_mart.csv") |
| ``` |
| |
| ## ⚠️ Before you train anything, read this |
| |
| - **`TD_3w` / `TD_1m` / `TD_3m` / `TD_*_status` / `path_at_*` are LABELS, not |
| features.** Never include them in your feature matrix `X` -- they encode |
| the future outcome you're trying to predict. |
| - **Filter each horizon independently.** A row can have `TD_3w` observed |
| while `TD_3m` is still `UNKNOWN_OBSERVATION` (that horizon's scan window |
| hasn't been reached yet). Don't drop a row just because one horizon isn't |
| ready -- filter `TD_{h}_status == 'OBSERVED'` per horizon, per model. |
| - **Use the provided `partition` column for splitting**, don't re-shuffle |
| randomly -- it's already a chronological, purged train/validation/test |
| split designed to prevent a training row's future label window from |
| overlapping a test row's T0. Drop `partition == 'PURGED'` rows entirely. |
| |
| ## Labels |
| |
| | Column | Meaning | |
| |---|---| |
| | `TD_3w` | Technical debt (SonarQube remediation effort, minutes) on this file, 3 weeks after `t0` | |
| | `TD_1m` | Same, 4 weeks after `t0` | |
| | `TD_3m` | Same, 13 weeks after `t0` | |
| | `TD_{h}_status` | `OBSERVED` / `UNKNOWN_OBSERVATION` / `UNKNOWN_LINEAGE` / `EXCLUDED` -- check before using the target | |
| | `TD_{h}_exclusion_reason` | Why, if not `OBSERVED` | |
| | `path_at_{h}` | The file's path at that horizon (may differ from `t0` if renamed) | |
| |
| "Technical debt" = sum of remediation-effort minutes across every SonarQube |
| issue (bug + vulnerability + code smell) active (OPEN/CONFIRMED/REOPENED) on |
| that file at that point in time. |
| |
| ## Row eligibility / split columns |
| |
| | Column | Meaning | |
| |---|---| |
| | `eligible_t0` | Whether this row is usable as a T0 at all (production `.java` file + full trailing history available) | |
| | `exclusion_reason_t0` | Why not, if `eligible_t0` is false | |
| | `lineage_confidence` | `HIGH`/`MEDIUM`/`AMBIGUOUS`/`UNSUPPORTED` -- confidence the file's identity was tracked correctly across renames/copies | |
| | `split_family` | `CHRONOLOGICAL_POOLED`, or `REPOSITORY_HELD_OUT` for any repo passed as a full holdout | |
| | `partition` | `TRAIN` / `VALIDATION` / `TEST` / `PURGED` -- **drop `PURGED` rows** | |
| | `max_git_feature_ts` / `max_sonar_feature_ts` / `max_feature_ts` | Leakage-audit timestamps: the latest info-date any feature in this row could see. Should never exceed `t0`. | |
| |
| ## Identity columns |
| |
| `origin_id`, `repository_id`, `artifact_id`, `lineage_id`, `path_at_t0`, |
| `t0` (the file's real historical commit date), `commit_hash_t0`, |
| `sonar_analysis_key_t0`, `language`. |
| |
| ## Features |
| |
| **Git / process (trailing exact-day windows ending at `t0`):** |
| `ncloc_t0`, `file_age_days`, `commits_{30d,90d,180d}`, |
| `lines_added/deleted_{30d,90d,180d}`, `churn_{30d,90d,180d}`, |
| `authors_{30d,90d,180d}`, `days_since_last_change`, `commit_acceleration`, |
| `churn_acceleration`, `commit_burstiness_180d`, `churn_burstiness_180d`. |
| |
| **Legacy 3-week-bucket git features (independent second measurement of similar signals):** |
| `total_lines_g3`, `commits_g3`, `churn_g3`, `churn_rate_g3`, |
| `commit_acceleration_g3`, `churn_acceleration_g3`, `commit_burstiness_g3`, |
| `churn_burstiness_g3`. |
| |
| **SonarQube snapshot at `t0`:** |
| `finding_count_t0`, `finding_density_t0`, `bug/vulnerability/code_smell_remediation_effort_t0`, |
| `technical_debt_minutes_t0`, `technical_debt_density_t0`, |
| `{blocker,critical,major,minor,info}_finding_count_t0`, |
| `{code_smell,bug,vulnerability}_count_t0`, `complexity_t0`, |
| `cognitive_complexity_t0`, `duplicated_lines_density_t0`, `comment_lines_density_t0`. |
| |
| **Trend + lifecycle (rolling `3w`/`1m`/`3m`/`6m` windows, blank until full trailing history exists):** |
| `finding_density_slope/volatility_*`, `technical_debt_density_slope/volatility_*`, |
| `complexity_slope_*`, `cognitive_complexity_slope_*`, `duplication_slope_*`, |
| `finding_arrivals_*`, `finding_closures_*`. |
| |
| ## Known limitations |
| |
| - `identity_ambiguous_count` / `identity_excluded_count` are always 0 (not yet implemented). |
| - `file_age_days` undercounts true age for files older than (earliest scanned week − 180 days). |
| - Lineage (rename/copy) tracking is heuristic, not a certified production system -- treat `lineage_confidence != HIGH` with caution for horizon-dependent targets. |
| - Not every feature computed during pipeline construction made it into this table (e.g. `ownership_entropy_180d`, an alternate issue-survival label). Ask if you need one of these -- they exist in intermediate pipeline outputs and can be added. |
| |
| ## Citation / provenance |
| |
| Built with a custom SonarQube-based mining pipeline: weekly-snapshot scanning |
| + git-window feature extraction + Sonar trend/lifecycle features, assembled |
| into a chronologically-split regression mart. Questions about how a specific |
| column was computed -> ask the person who generated this dataset. |
| |
