In large-scale collaborative software development, building a team of software practitioners can be challenging, mainly due to overloading choices of candidate members to fill in each role. Furthermore, having to understand all members’ diverse backgrounds, and anticipate team compatibility could significantly complicate and attenuate such a team formation process. Current solutions that aim to automatically suggest software practitioners for a task merely target particular roles, such as developers, reviewers, and integrators. While these existing approaches could alleviate issues presented by choice overloading, they fail to address team compatibility while members collaborate. In this paper, we propose RECAST, an intelligent recommendation system that suggests team configurations that satisfy not only the role requirements, but also the necessary technical skills and teamwork compatibility, given task description and a task assignee. Specifically, RECAST uses Max-Logit to intelligently enumerate and rank teams based on the team-fitness scores. Machine learning algorithms are adapted to generate a scoring function that learns from heterogenous features characterizing effective software teams in large-scale collaborative software development. RECAST is evaluated against a state-of-the-art team recommendation algorithm using three well-known open-source software project datasets. The evaluation results are promising, illustrating that our proposed method outperforms the baselines in terms of team recommendation with 646% improvement (MRR) using the exact-match evaluation protocol.

在大规模的协作软件开发中，组建软件从业者团队可能会面临挑战，这主要是因为过多地选择了候选成员来填补每个角色。此外，必须了解所有成员的不同背景，并预期团队的兼容性可能会极大地复杂化并削弱这样的团队组建过程。当前旨在自动建议软件从业人员完成任务的解决方案仅针对特定角色，例如开发人员，审阅者和集成商。尽管这些现有方法可以缓解选​​择超载带来的问题，但是当成员进行协作时，它们无法解决团队的兼容性。在本文中，我们提出了RECAST，这是一个智能的推荐系统，可以为团队配置提供建议，这些配置不仅可以满足角色要求，还可以满足必要的技术技能和团队协作能力，并提供给定的任务描述和任务受让人。具体地说，RECAST使用Max-Logit根据团队适应性得分对团队进行智能枚举和排名。机器学习算法适用于生成评分功能，该评分功能可从表征大规模协作软件开发中有效软件团队的异构特性中学习。回放使用三个著名的开放源代码软件项目数据集，根据最新的团队推荐算法进行评估。评估结果令人鼓舞，这说明我们提出的方法在团队推荐方面使用精确匹配评估方案优于基线，改善了646％。