Using LLMs to Bridge the Gaps in QA Test Plans at Firefox
Bibliographic record
Abstract
As software systems grow in scale and complexity, ensuring their reliability becomes increasingly challenging due to factors like diverse platforms, rapid release cycles, and evolving user needs. This places greater demands on software quality assurance (QA), where comprehensive test planning is crucial. However, in addition to this process being manual and time-consuming, skilled QA engineers may potentially overlook critical scenarios.To bridge this gap, we used an LLM, GPT-4 Turbo, to generate test plans for eight Firefox features, evaluating them for novelty, validity, and relevance. Our results showed that 27% of the test cases in LLM-generated plans surfaced previously missed test scenarios the QA team deemed valuable, while 50.5% replicated existing ones. Although 22.5% were invalid and out of scope, our approach shows potential for improving test coverage. In this paper, we share our experience with this methodology and offer insights for SE/QA practitioners integrating LLMs into their workflows.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".