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`config_error`	Malformed YAML, outdated action versions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

First LoRA fine-tuning applied to CI/CD log classification
First SHAP attribution on CI/CD failure predictions
First expertise-aware (novice) adaptive explanation system for CI/CD
Multi-source labelling: log text + workflow name + step name signals
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.