Multi-objective optimization of software testing schedules for modular control Software, considering learning and negligence factors of testing Staffs

Abstract

Comprehensive software testing and debugging are essential to prevent customer dissatisfaction resulting from software defects. However, software development managers often encounter significant constraints, such as limited budgets and time-sensitive market opportunities, which hinder extensive debugging efforts. Effective project management in software testing requires a careful balance of cost, quality, and timeline considerations to facilitate optimal decision-making. Traditional Software Reliability Growth Models (SRGMs) typically focus on single-system testing; however, modern control software for machine or robot development increasingly adopts a modular systems engineering approach. This transition allows for parallel testing across multiple modules, enabling managers to assign different testing teams to work concurrently on separate components. This study addresses the need for a structured approach by proposing multi-objective programming models to optimize testing schedules for complex, modular software systems. These models aim to minimize overall testing time while ensuring that reliability standards are met within resource constraints. Furthermore, this study introduces a novel SRGM that incorporates the learning curves of testing personnel and potential negligence factors. The fitting results for prediction accuracy can reach 95% in the majority of the cases analyzed in this study. Additionally, we propose the development of a computerized decision support system to facilitate practical implementation and decision-making in real-world scenarios.