Local-based active classification of test report to assist crowdsourced testing
Bibliographic record
Abstract
In crowdsourced testing, an important task is to identify the test reports that actually reveal fault - true fault, from the large number of test reports submitted by crowd workers. Most existing approaches towards this problem utilized supervised machine learning techniques, which often require users to manually label a large amount of training data. Such process is time-consuming and labor-intensive. Thus, reducing the onerous burden of manual labeling while still being able to achieve good performance is crucial. Active learning is one potential technique to address this challenge, which aims at training a good classifier with as few labeled data as possible. Nevertheless, our observation on real industrial data reveals that existing active learning approaches generate poor and unstable performances on crowdsourced testing data. We analyze the deep reason and find that the dataset has significant local biases. To address the above problems, we propose LOcal-based Active ClassiFication (LOAF) to classify true fault from crowdsourced test reports. LOAF recommends a small portion of instances which are most informative within local neighborhood, and asks user their labels, then learns classifiers based on local neighborhood. Our evaluation on 14,609 test reports of 34 commercial projects from one of the Chinese largest crowdsourced testing platforms shows that our proposed LOAF can generate promising results. In addition, its performance is even better than existing supervised learning approaches which built on large amounts of labelled historical data. Moreover, we also implement our approach and evaluate its usefulness using real-world case studies. The feedbacks from testers demonstrate its practical value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".