Guided Mutation Testing for JavaScript Web Applications
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
Mutation testing is an effective test adequacy assessment technique. However, there is a high computational cost in executing the test suite against a potentially large pool of generated mutants. Moreover, there is much effort involved in filtering out equivalent mutants. Prior work has mainly focused on detecting equivalent mutants after the mutation generation phase, which is computationally expensive and has limited efficiency. We propose an algorithm to select variables and branches for mutation as well as a metric, called FunctionRank, to rank functions according to their relative importance from the application's behaviour point of view. We present a technique that leverages static and dynamic analysis to guide the mutation generation process towards parts of the code that are more likely to influence the program's output. Further, we focus on the JavaScript language, and propose a set of mutation operators that are specific to Web applications. We implement our approach in a tool called MUTANDIS. The results of our empirical evaluation show that (1) more than 93 percent of generated mutants are non-equivalent, and (2) more than 75 percent of the surviving non-equivalent mutants are in the top 30 percent of the ranked functions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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