TY - GEN
T1 - Human-Machine Teaming and Team Effectiveness in AI tools for Software Engineering
AU - Rauf, Irum
AU - Sharp, Helen
AU - Lopez, Tamara
AU - Wermelinger, Michel
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Background: Artificial Intelligence (AI) is increasingly being used to support software engineering (SE), shifting the role of AI tools for SE (AI4SE) towards team members rather than simply tools. Human-machine teaming (HMT) views humans and machines as members of a team. This approach may be useful in AI4SE but HMT and team effectiveness have received little attention in the software engineering community. Objective: To unpick whether and how HMT and team effectiveness have been applied in AI4SE, and to identify recommendations. Method: A mapping study approach identifies 21 papers on developer-centred AI4SE. Each one is analysed to identify its use of HMT, its focus regarding AI-developer interaction, and any elements of team effectiveness. Results: Different aspects of AI-developer interaction have been considered but only one paper explicitly uses the HMT concept. Some elements of team effectiveness are considered, but others are not addressed at all, and several studies do not consider any. Conclusion: Researchers are encouraged to consider HMT as a framework for AI4SE, to leverage research in all-human teams, adaptive UX and developer motivation to inform tool design, and reduce reliance on human monitoring.
AB - Background: Artificial Intelligence (AI) is increasingly being used to support software engineering (SE), shifting the role of AI tools for SE (AI4SE) towards team members rather than simply tools. Human-machine teaming (HMT) views humans and machines as members of a team. This approach may be useful in AI4SE but HMT and team effectiveness have received little attention in the software engineering community. Objective: To unpick whether and how HMT and team effectiveness have been applied in AI4SE, and to identify recommendations. Method: A mapping study approach identifies 21 papers on developer-centred AI4SE. Each one is analysed to identify its use of HMT, its focus regarding AI-developer interaction, and any elements of team effectiveness. Results: Different aspects of AI-developer interaction have been considered but only one paper explicitly uses the HMT concept. Some elements of team effectiveness are considered, but others are not addressed at all, and several studies do not consider any. Conclusion: Researchers are encouraged to consider HMT as a framework for AI4SE, to leverage research in all-human teams, adaptive UX and developer motivation to inform tool design, and reduce reliance on human monitoring.
UR - https://www.scopus.com/pages/publications/105009054899
U2 - 10.1109/CHASE66643.2025.00017
DO - 10.1109/CHASE66643.2025.00017
M3 - Conference proceeding
AN - SCOPUS:105009054899
T3 - Proceedings - 2025 IEEE/ACM 18th International Conference on Cooperative and Human Aspects of Software Engineering, CHASE 2025
SP - 75
EP - 80
BT - Proceedings - 2025 IEEE/ACM 18th International Conference on Cooperative and Human Aspects of Software Engineering, CHASE 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE/ACM International Conference on Cooperative and Human Aspects of Software Engineering, CHASE 2025
Y2 - 27 April 2025 through 28 April 2025
ER -