Modeling Communication Perception in Development Teams Using Monte Carlo Methods

• URL: http://arxiv.org/abs/2504.17610v1
• Date: Thu, 24 Apr 2025 14:35:18 GMT
• Title: Modeling Communication Perception in Development Teams Using Monte Carlo Methods
• Authors: Marc Herrmann, Martin Obaidi, Jil Klünder,
• Abstract summary: Mood surveys enable the early detection of underlying tensions or dissatisfaction within development teams.<n>This paper analyzes the diversity of perceptions within arbitrary development teams.<n>We present a preliminary mathematical model to calculate the minimum agreement among a subset of developers.
• Score: 1.8369669715149237
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software development is a collaborative task involving diverse development teams, where toxic communication can negatively impact team mood and project success. Mood surveys enable the early detection of underlying tensions or dissatisfaction within development teams, allowing communication issues to be addressed before they escalate, fostering a positive and productive work environment. The mood can be surveyed indirectly by analyzing the text-based communication of the team. However, emotional subjectivity leads to varying sentiment interpretations across team members; a statement perceived neutrally by one developer might be seen as problematic by another developer with a different conversational culture. Early identification of perception volatility can help prevent misunderstandings and enhance team morale while safeguarding the project. This paper analyzes the diversity of perceptions within arbitrary development teams and determines how many team members should report their sentiment to accurately reflect the team's mood. Through a Monte Carlo experiment involving 45 developers, we present a preliminary mathematical model to calculate the minimum agreement among a subset of developers based on the whole team's agreement. This model can guide leadership in mood assessment, demonstrating that omitting even a single member in an average-sized 7-member team can misrepresent the overall mood. Therefore, including all developers in mood surveying is recommended to ensure a reliable evaluation of the team's mood.

Related papers

• ML-SPEAK: A Theory-Guided Machine Learning Method for Studying and Predicting Conversational Turn-taking Patterns [25.049072387358244]
  We develop a computational model of conversational turn-taking within self-organized teams. By bridging the gap between individual personality traits and team communication patterns, our model has the potential to inform theories of team processes.
  arXiv  Detail & Related papers  (2024-11-23T01:27:01Z)
• Characterising Developer Sentiment in Software Components: An Exploratory Study of Gentoo [6.253919624802852]
  Collaborative software development happens in teams, that cooperate on shared artefacts, and discuss development on online platforms. Previous research has shown how communication between team members, especially in an open-source environment, can become extremely toxic. Our study shows that, in recent years, negative emotions have generally decreased in the communication between Gentoo developers.
  arXiv  Detail & Related papers  (2024-05-27T09:22:47Z)
• Governing the Commons: Code Ownership and Code-Clones in Large-Scale Software Development [6.249768559720122]
  In software development organizations employing weak or collective ownership, different teams are allowed and expected to autonomously perform changes in various components. Our objective is to understand how and why different teams introduce technical debt in the form of code clones as they change different components.
  arXiv  Detail & Related papers  (2024-05-24T18:23:51Z)
• GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment [72.96949760114575]
  We propose a novel cooperative communication framework, Goal-Oriented Mental Alignment (GOMA)<n>GOMA formulates verbal communication as a planning problem that minimizes the misalignment between parts of agents' mental states that are relevant to the goals.<n>We evaluate our approach against strong baselines in two challenging environments, Overcooked (a multiplayer game) and VirtualHome (a household simulator)
  arXiv  Detail & Related papers  (2024-03-17T03:52:52Z)
• Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
  We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
  arXiv  Detail & Related papers  (2023-09-29T13:33:06Z)
• Locating Community Smells in Software Development Processes Using Higher-Order Network Centralities [38.72139150402261]
  Community smells are negative patterns in software development teams' interactions that impede their ability to create software. Current approaches aim to detect community smells by analysing static network representations of software teams' interaction structures. We show that higher-order network models provide a robust means of revealing such hidden patterns and complex relationships.
  arXiv  Detail & Related papers  (2023-09-14T06:48:15Z)
• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• Building Cooperative Embodied Agents Modularly with Large Language Models [104.57849816689559]
  We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments. We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework. Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
  arXiv  Detail & Related papers  (2023-07-05T17:59:27Z)
• Measuring the Effect of Influential Messages on Varying Personas [67.1149173905004]
  We present a new task, Response Forecasting on Personas for News Media, to estimate the response a persona might have upon seeing a news message. The proposed task not only introduces personalization in the modeling but also predicts the sentiment polarity and intensity of each response. This enables more accurate and comprehensive inference on the mental state of the persona.
  arXiv  Detail & Related papers  (2023-05-25T21:01:00Z)
• Detecting and Optimising Team Interactions in Software Development [58.720142291102135]
  This paper presents a data-driven approach to detect the functional interaction structure for software development teams. Our approach considers differences in the activity levels of team members and uses a block-constrained configuration model. We show how our approach enables teams to compare their functional interaction structure against synthetically created benchmark scenarios.
  arXiv  Detail & Related papers  (2023-02-28T14:53:29Z)
• Few-shot Language Coordination by Modeling Theory of Mind [95.54446989205117]
  We study the task of few-shot $textitlanguage coordination$. We require the lead agent to coordinate with a $textitpopulation$ of agents with different linguistic abilities. This requires the ability to model the partner's beliefs, a vital component of human communication.
  arXiv  Detail & Related papers  (2021-07-12T19:26:11Z)
• Analyzing Team Performance with Embeddings from Multiparty Dialogues [1.8275108630751844]
  This paper examines the problem of predicting team performance from embeddings learned from multiparty dialogues. Unlike syntactic entrainment, both dialogue act and sentiment embeddings are effective for classifying team performance, even during the initial phase.
  arXiv  Detail & Related papers  (2021-01-23T05:18:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.