GASSER: A Multi-Objective Evolutionary Approach for Test Suite Reduction
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
Regression testing is a practice that ensures a System Under Test (SUT) still works as expected after changes have been implemented. The simplest approach for regression testing is Retest-all, which consists of re-executing the entire Test Suite (TS) on the changed version of the SUT. Retest-all could be expensive in case a SUT and its TS grow in size and, if resources are insufficient, its application could be impracticable. A Test Suite Reduction (TSR) approach aims to overcome these issues by reducing the size of TSs, while preserving their fault-detection capability. In this paper, we introduce and validate an approach for TSR based on a multi-objective evolutionary algorithm, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II). This approach seeks to reduce TSs by maximizing both statement coverage and diversity of test cases of the reduced TSs, while minimizing the size of the reduced TSs. We named this approach Genetic Algorithm for teSt SuitE Reduction (GASSER). To assess GASSER, we conducted an experiment on 19 versions of four software systems from a public dataset—i.e. Software-artifact Infrastructure Repository (SIR). We compared GASSER with nine baseline approaches. The comparison was based on the size of the reduced TSs and their fault-detection capability. The most important take-away result is that GASSER, as compared with the baseline approaches, reduces more the size of the TSs with a non-significant effect on their fault-detection capability. The results of our empirical assessment suggest that the application of multi-objective evolutionary algorithms and, in particular, NSGA-II might represent a viable means to deal with TSR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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