SHIFT: Sigmoid-Based Heuristic Invertible Fitness-Landscape Transformation for Accelerating SBST
2026-04-10 • Software Engineering
Software Engineering
AI summaryⓘ
The authors address a problem in Search-Based Software Testing where the test-generating process gets stuck in tricky areas called 'local optima' or flat zones with little useful feedback. They introduce a method called SHIFT that changes how the search space is shaped, making it easier for the testing process to move past these difficult spots without losing important information. Their experiments show that SHIFT helps the search find better test inputs faster compared to other common techniques. This suggests that their method can improve the efficiency of automated software testing.
Search-Based Software Testing (SBST)Fitness landscapeLocal optimaPlateausSigmoid functionHill climbingGenetic algorithmsFitness landscape transformationConvergence speedCoverage
Authors
Jeongjin Han, Seunghoon Sim, Jian Lee, Seongyoon Park
Abstract
Search-Based Software Testing (SBST) automates test input generation but is frequently hindered by challenging fitness landscapes characterized by numerous deceptive local optima that impede search progress, as well as extended plateaus where informative fitness signals are scarce. To address this bottleneck, we propose SHIFT (Sigmoid-Based Heuristic Invertible Fitness-Landscape Transformation for Accelerating SBST), a method designed to compress local landscapes and facilitate escape from stagnant regions without altering global semantics. By systematically contracting dense regions where search points cluster, the approach preserves mapping invertibility while enabling optimization algorithms to traverse more effectively toward global coverage with the same step size. When evaluated against established baselines, including pure hill climbing and genetic algorithms, under a normalized experimental protocol, the proposed technique yields consistent improvements in convergence speed and search efficiency. These results demonstrate that sigmoid compression constitutes a lightweight yet effective mechanism for achieving more reliable coverage discovery in complex testing environments.