Utilizing Multi-Objective Evolutionary Algorithms to Optimize Open Source Software Release Management

Abstract

Scheduling and resource allocation for Open Source Software (OSS) product development pose crucial and challenging tasks due to program size and resource limitations. The properties of OSS further complicate product assessment and maintenance for developers. This paper proposes a model for the iterative and multi-release OSS development process. Unlike traditional methods that oversimplify the problem by reducing the multi-decision space into a single-objective optimization problem, our approach suggests employing Multi-objective Evolutionary Algorithms (MOEAs) to solve the Optimal Release Time Planning Problem, enabling the simultaneous maximization of reliability and minimization of cost. We consider testing cost and system reliability, two critical dimensions, as the primary objectives, while also incorporating testing resource consumption as the third objective. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is chosen as the primary method for its effectiveness in MOEAs, with a special scenario outlined in the paper where NSGA-II may not guarantee optimal solutions. Demonstrating the practicality of our proposed method, we utilize open source data to assess release time and illustrate its superiority to NSGA-II. Numerical examples further showcase the model's effectiveness.