| --- |
| language: en |
| license: mit |
| tags: |
| - ci-cd |
| - github-actions |
| - bert |
| - lora |
| - peft |
| - xai |
| - explainable-ai |
| - text-classification |
| - software-engineering |
| datasets: |
| - facebook/react |
| metrics: |
| - f1 |
| - accuracy |
| model-index: |
| - name: AdaptCI-XAI |
| results: |
| - task: |
| type: text-classification |
| metrics: |
| - type: f1 |
| value: 0.7124 |
| --- |
| |
| # AdaptCI-XAI: CI Pipeline Failure Classifier |
|
|
| **Research project:** IS 8101 — Sabaragamuwa University of Sri Lanka |
| **Student:** Mary Angel Anton Premathas (20APC4548) |
| **Paper:** AdaptCI-XAI: Explainable AI for CI Pipeline Failure Diagnosis using Transformer-Based Models on GitHub Actions |
|
|
| ## What this model does |
| Classifies GitHub Actions CI/CD pipeline failure logs into 4 categories: |
| | Label | Description | |
| |-------|-------------| |
| | `config_error` | Malformed YAML, outdated action versions | |
| | `dependency_failure` | npm/pip install failures, missing packages | |
| | `test_failure` | Unit/integration test failures, type errors | |
| | `infrastructure` | Runner timeout, OOM, network errors | |
|
|
| ## How to use |
| ```python |
| from transformers import pipeline |
| clf = pipeline("text-classification", model="MaryAngel/AdaptCI-XAI") |
| result = clf("npm ERR ENOENT no such file or directory node_modules/react") |
| print(result) |
| # [{'label': 'dependency_failure', 'score': 0.94}] |
| ``` |
|
|
| ## Training details |
| - **Base model:** bert-base-uncased |
| - **Fine-tuning:** LoRA (r=8, alpha=16, target=query+value layers) |
| - **Trainable parameters:** ~0.54% of BERT total |
| - **Training data:** Real failed CI runs from facebook/react (GitHub API) |
| - **Hardware:** Google Colab T4 GPU (free tier) |
| - **Weighted F1:** 0.7124 |
|
|
| ## Novelty |
| 1. First LoRA fine-tuning applied to CI/CD log classification |
| 2. First SHAP attribution on CI/CD failure predictions |
| 3. First expertise-aware (novice) adaptive explanation system for CI/CD |
| 4. Multi-source labelling: log text + workflow name + step name signals |
| 5. Fully reproducible on FREE hardware (Colab T4 + HF) |
|
|
| ## XAI |
| Each prediction includes SHAP token attribution showing which log words drove the classification decision — making the black-box model transparent to developers. |
|
|
