GASSER: A Multi-Objective Evolutionary Approach for Test Suite Reduction
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".