From code communication to people communication
This catalog is the output of a case study extending the concept of bad smells — coined by Kent Beck and Martin Fowler for code quality — to the socio-technical domain of communication in multidisciplinary remote agile teams.
Grounding in the literature
Applying the bad smell concept to communication builds on an established body of literature about noise, clarity and communication effectiveness in distributed teams.
Ground the notion of noise in communication and loss of clarity.
Fowler (2018)Coined the concept of a bad smell in code — the metaphor this study extends to communication.
Zahedi & Babar (2014)Sharing technical knowledge through artefacts.
Diel et al. (2016)Communication strategies in distributed DevOps teams.
Weger et al. (2022)Communication effectiveness and reflexivity in teams.
Kostin & Strode (2022)Relate clarity to effectiveness in distributed Scrum teams.
Rauf et al. (2023)Structured communication in requirements elicitation on remote projects.
Neeley (2021)The choice of communication channels as a decisive factor for remote project outcomes.
The case study
The research was conducted in a real remote project developing a track-based training platform. The project ran for five years and involved 50 participants in total, organized into subteams by function and area, across six stages of interaction:
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1
Client → Requirements
The client passes the demands on to the Project Manager and the Requirements Team.
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2
Requirements → UX/UI
Demands are elicited and documented as Epics and User Stories, prioritized, and turned into high-fidelity screen prototypes.
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3
Management → Development
The Project Manager distributes tasks to the Development Team according to scope (coding, testing, DevOps, content production).
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4
Development → QA/Test
The software increment is validated against the definition of done (unit, functional and usability testing).
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5
QA/Test → DevOps
The DevOps Team integrates the code into the infrastructure.
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6
Management → Client
The manager receives the sprint deliverable and presents it to the client.
Methodology
A mixed-methods questionnaire was applied to nine teams, following the concurrent triangulation design of Creswell & Creswell (2017): 17 open and closed questions, organized into four evaluative topics.
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Topic 1
Tools
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Topic 2
Communication Channels
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Topic 3
Challenges (perceptions)
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Topic 4
Possible solutions (perceptions)
The qualitative data were validated with a word cloud and a Sankey diagram (correlating reported challenges with proposed solutions), while the quantitative data measured the frequency (scale of 1 to 5) and quality (scale of 1 to 6) of communication at each contact point in the project.
From questionnaire to insights
The quantitative and qualitative tracks ran in parallel until they converged in the integration of the findings.
Apply Questionnaire
A mixed questionnaire of 17 open and closed questions, applied to nine teams across different areas of the project, covering tools, channels, challenges and perceived solutions.
Quantitative track
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Data Cleaning
Organizing the closed answers before analysis, preparing the frequency (1–5) and quality (1–6) scales for tabulation.
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Statistical Analysis
Computing average frequency and quality of communication for each contact point — teams, leadership, management and coordination.
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Descriptive Statistics
Consolidating the study's headline figures, such as the 100% adoption of WhatsApp and the percentage distribution of responding teams.
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Inferential Test
Comparing contact points to locate where communication is weakest — showing a horizontal structure whose links outside the team (coordination, governance, users) are the most fragile.
Qualitative track
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Coding and Themes
Answers to the two open-ended questions — challenges faced and solutions suggested — are organized into recurring themes cited by participants.
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Thematic Analysis
Coded themes are grouped into consistent patterns — the basis from which the five bad smells of the catalog emerged.
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Word Cloud
Term-frequency visualization over the open-ended answers — project, information, WhatsApp and Discord are among the most cited.
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Content Analysis
Qualitative reading relating each reported symptom to the solution suggested by the participant themselves — the basis of the Sankey diagram below.
Data Triangulation
Integrating the quantitative and qualitative findings, following the concurrent triangulation design of Creswell & Creswell (2017) — the crossing that made it possible to name and categorize the five bad smells.
Insights and Proposals
The outcome of the process: the five communication bad smells identified and their mitigation strategies, documented in this catalog.
Research questions
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RQ1
What are the main communication tools used in the project and how do they impact the effectiveness of communication?
Finding: WhatsApp is used by 100% of respondents, becoming the de facto channel even though it is not a formal project tool — one of the findings behind the recommendation to move important decisions to traceable channels (e.g. Discord, Confluence).
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RQ2
How do the frequency and quality of communication vary between different contact points in the project?
Finding: Communication between teams is the most frequent and the best rated (4.2/5 frequency, 4.5/6 quality); Coordination, Governance and Users fall well below (averages of 2.97 and 3.33) — evidence of a horizontal structure whose links outside the team are the weakest.
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RQ3
What are the main communication challenges faced by the teams and what solutions can be proposed to overcome them?
Finding: The twelve symptoms reported in the open-ended answers (see the correlation diagram below) were grouped into the five bad smells of the catalog, each with specific mitigation strategies drawn from the participants' own suggestions.
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RQ4
How can communication be improved to increase team integration and overall project efficiency?
Finding: The most cited suggestions — centralizing communication, integrating the teams better and establishing consistent processes — recur across almost every symptom, suggesting that gains in integration depend less on a new tool and more on team agreements and present leadership.
What the data showed
Participating teams (% of responses)
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Testing/QA30.4%
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Development21.7%
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Digital Transformation13%
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DevOps8.7%
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Management8.7%
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Requirements4.3%
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UX/UI4.3%
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Coordination4.3%
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Administrative Support4.3%
Tools
100%
of the professionals use WhatsApp to communicate in the project.
Most cited communication points
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Communication between teams78.3%
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Communication between leadership47%
A predominantly horizontal structure, with an emphasis on intra-team interaction.
Communication frequency × quality by channel
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Teams
Frequency 4.2Quality 4.5 -
Leadership
Frequency 3.8Quality 4.3 -
Management
Frequency 3.2Quality 3.7 -
Coordination / Governance / Users (average)
Frequency 3.0Quality 3.3
From reported symptoms to suggested improvements
In the two open-ended questions of the questionnaire, each participant described the challenges they face and the actions they would suggest to solve them. The diagram below reconstructs those associations answer by answer — the same triangulation technique that made it possible to identify the five bad smells and ground the mitigation strategies of the catalog.
The thickness of each link is proportional to the number of answers associating that symptom with that improvement. The questionnaire was administered in Portuguese; these labels are translations of the participants' original answers.
Authors
The team behind the study and its presentation at WASHES 2026 (11th Workshop on Social, Human and Economic Aspects of Software).
Contributions
- Empirical validation of a taxonomy of communication bad smells, extending the established concept of code smells to the human and social dimensions of software development.
- Practical, context-aware mitigation strategies tailored to remote work constraints — rotating meeting times to accommodate time zones, and centralized knowledge bases to break down expertise silos.
- A methodological approach demonstrating the value of combining quantitative communication metrics with qualitative insights to provide a comprehensive view of team dynamics.
Limitations and future work
- The single-case study design suggests a need for broader validation across different organizational contexts and team structures.
- Longitudinal research could help determine whether the proposed mitigation strategies lead to sustained improvements over time.
- Emerging technologies such as AI-powered communication analysis may offer new opportunities for automatically detecting and addressing communication smells before they significantly impact team performance.
How to cite
Bad Smells of Communication in Multidisciplinary Agile Teams of a Remote Software Project. A study aligned with GranDSI-BR 2016–2026 (Boscarioli et al., 2017), on the challenge of collaboration, coordination and information sharing in complex and distributed socio-technical systems.