Bundle recommender system is divided into two main stages: bundle editing and bundle recommendation. While substantial research progress has been made in each stage, in practical application scenarios, bundle compositions and the final recommended bundles mutually influence each other: the continuously editing bundle compositions affect the recommendation results, while user feedback on recommended bundles in turn guides the refinement of bundle compositions. This paper presents the first comprehensive reproducibility study of the complete bundle recommendation pipeline. We implement eight bundle-level editing methods, nine item-level editing methods, and seven state-of-the-art bundle recommendation models, and evaluate their performance across six real-world datasets. Our empirical analysis reveals several key findings. First, bundle-level editing faces the challenge of generating high-quality bundles. Second, in the item-level editing, the replacement operation emerges as a universal bottleneck across all methods. Third, in the recommendation stage, recommendation models exhibit varying performance across different interaction density scenarios (e.g., cold-start). Finally, bundle recommendation suffers degraded performance when integrating item-level editing and bundle recommendation within a unified pipeline. Overall, there is the systemic limitation of bundle recommendation: prior work has focused on optimizing individual stages independently, disregarding the interdependencies throughout the entire recommendation system. These findings highlight the urgent need to develop end-to-end solutions that can holistically address the bundle editing and recommendation workflow. Our repository is now available for public access via https://github.com/anyr123/Bundle_Edit_Rec_SIGIR26.