When to automate software testing? A decision‐support approach based on process simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Software test processes are complex and costly. To reduce testing effort without compromising effectiveness and product quality, automation of test activities has been adopted as a popular approach in software industry. However, because test automation usually requires substantial upfront investments, automation is not always more cost‐effective than manual testing. To support decision‐makers in finding the optimal degree of test automation in a given project, we recently proposed a process simulation model using the System Dynamics modeling technique and used the simulation model in the context of a case study with a software company in Calgary, Canada. With the help of the simulation model, we were able to evaluate the performance of test processes with varying degrees of automation of test activities and help testers choose the most optimal cases. The goal of the earlier study was to investigate how the simulation model can help decision‐makers decide whether and to what degree the company should automate their test processes. In this article, we present further details of the System Dynamics model, its usage scenarios and examples of simulation experiments independent from a specific company context. Copyright © 2015 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it