SHIFT: Sigmoid-Based Heuristic Invertible Fitness-Landscape Transformation for Accelerating SBST

TL;DR

SHIFT introduces a sigmoid-based transformation that compresses fitness landscapes, helping search algorithms escape local optima and plateaus, thereby improving the efficiency of search-based software testing.

Contribution

The paper presents a novel invertible landscape transformation technique that enhances search efficiency in SBST by systematically compressing local regions without losing global semantics.

Findings

Consistent improvements in convergence speed over baseline methods.

Enhanced ability to escape local optima and plateaus.

Effective in complex testing environments.

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.