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Supporting the Understanding of Team Dynamics in Agile Software Development Through Computer-Aided Sprint Feedback

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Abstract

The complexity of software projects and inherent customer demands is becoming increasingly challenging for developers and managers. Human factors in the development process are growing in importance. Consequently, understanding team dynamics is a central aspect of steady development planning and execution. Despite the many available management systems and development tools that are being continuously improved to support teams and managers with practical process information, the equally crucial sociological aspects have typically been addressed insufficiently or not at all. In people-focused agile software processes, a first socio-technical understanding can also be promoted by sharing positive and negative development experiences during specific team meetings (e.g., sprint Retrospectives). Nevertheless, there is still a lack of systematically recorded and processed socio-technical information in software projects, making it difficult for subsequent reviews by teams and managers to characterize and understand the sometimes volatile and complex team dynamics during the process. This thesis strives to support teams and managers in understanding and improving awareness of the team dynamics that occur in their agile software projects by introducing computer-aided sprint feedback. The concept builds on four information assets: (1) socio-technical data monitoring, (2) descriptive sprint feedback, (3) predictive sprint feedback, and (4) exploratory sprint planning. These assets unify interdisciplinary fundamentals, practical methods from software engineering, data science, organizational and social psychology. Using a design science research process for information systems, observations in several conducted studies (32 in academic project environments and three in industry) resulted in the foundations and methods for a practical feedback concept on the socio-technical aspects in sprint, prototypically realized for Jira. A practical evaluation involved two industry projects in an action research methodology that helped improve the concept’s usability and utility through practitioner reflections. The collaboration between industry and research resolved practical issues that did not arise during the design science process. Several beneficial outcomes based on the provided sprint feedback are reported and described in this study (e.g., the effect of team structures on development performance). Moreover, the reflections underscored the practical relevance of systematic feedback and the need to better understand human factors in the software development process.
Supporting the Understanding of Team Dynamics
in Agile Software Development
Through Computer-Aided Sprint Feedback
Fabian Kortum
2022, 232 pages
ISBN 978-3-8325-5438-5
Price: 62.50
To purchase, please contact your local bookseller or
order online from www.logos-verlag.com
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Logos Verlag Berlin
ISBN 978-3-8325-5438-5
While modern project management systems support teams during
planning and development activities, primarily through performance-
related process information, the equally relevant human factors are often
insufficiently considered for explaining team dynamics (e.g., the affect of
moods in teams). However, understanding team behavioral patterns are
crucial for the accurate planning and steady execution of development
tasks throughout an ongoing project.
A computer-aided feedback concept is described, unifying interdisci-
plinary foundations and methods from the software engineering, data
science, organizational, and social psychology fields for disclosing team
dynamics in agile software projects. The concept covers the systematic
capture of sociotechnical data combined with descriptive, predictive,
and exploratory model-based methods that support understanding
behavioural changes during the development process. Design science
from information systems research is used in academic and industrial
case studies to conceptualize and operationalize the feedback methods
into a practical Jira plugin.
The concluding evaluation through an action research method in two
industrial software projects results in quantitative and qualitative findings
regarding the feedback utilization and utility during agile development
processes (e.g., team communication changes related to accomplished
performances). The case studies underscore the practical relevance for
systematic feedback and the need to better understand human factors
in software projects.
Fabian Kortum
Supporting the Understanding of Team Dynamics in Agile Software
Development Through Computer-Aided Sprint Feedback
Fabian Kortum
Supporting the Understanding of
Team Dynamics in Agile Software
Development Through
Computer-Aided Sprint Feedback
LOGOS VERLAG BERLIN
Georg-Knorr-Str. 4, G. 10, D-12681 Berlin, Germany +49 (0)30 42 85 10 90
While modern project management systems support teams during plan-
ning and development activities, primarily through performance-related
process information, the equally relevant human factors are often in-
sufficiently considered for explaining team dynamics (e.g., the affect of
moods in teams). However, understanding team behavioral patterns are
crucial for the accurate planning and steady execution of development
tasks throughout an ongoing project.
A computer-aided feedback concept is described, unifying interdis-
ciplinary foundations and methods from the software engineering, data
science, organizational, and social psychology fields for disclosing team
dynamics in agile software projects. The concept covers the systematic
capture of sociotechnical data combined with descriptive, predictive,
and exploratory model-based methods that support understanding be-
havioural changes during the development process. Design science from
information systems research is used in academic and industrial case
studies to conceptualize and operationalize the feedback methods into a
practical Jira plugin.
A concluding evaluation through an action research method in two in-
dustrial software projects results in quantitative and qualitative findings
regarding the feedback utilization and utility during agile development
processes (e.g., team communication changes related to accomplished
performances). The case studies underscore the practical relevance for
systematic feedback and the need to better understand human factors
in software projects.
ix
Contents
Abstract v
List of Figures xi
List of Tables xiii
List of Abbreviations xv
1 Introduction 1
1.1 Human Factors in Software Projects . . . . . . . . . . . . . . . . . . . 1
1.2 Research Motivation and Questions . . . . . . . . . . . . . . . . . . . 3
1.3 Design Science Research . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 ActionResearch............................... 9
1.5 Contributions ................................ 9
1.6 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Fundamentals 11
2.1 Socio-Technical Aspects in Agile Software Projects . . . . . . . . . . . 11
2.2 Data Science Support for Knowledge Foundations . . . . . . . . . . . 24
3 Related Work 41
3.1 Teamwork in Software Projects . . . . . . . . . . . . . . . . . . . . . . 41
3.2 Model-Based Knowledge Support in Software Projects . . . . . . . . 44
3.3 Feedback and Information Visualization . . . . . . . . . . . . . . . . . 46
4 Concept for Capturing Socio- Technical Aspects in Agile Projects 47
4.1 Relevance of Systematic Measurement Methods . . . . . . . . . . . . 48
4.2 Human Factors Associated with Team Behavior . . . . . . . . . . . . 48
4.3 Team Dynamics Feedback: Goal, Questions and Metrics . . . . . . . . 51
4.4 Measurement Methods for Aspects of Team Dynamics . . . . . . . . 57
4.5 Technological Feasibility of the Data Capture Concept . . . . . . . . . 62
5 Concept for Computer-Aided Sprint Feedback 65
5.1 Preprocessing of Socio-Technical Data . . . . . . . . . . . . . . . . . . 67
5.2 Descriptive Sprint Feedback Asset . . . . . . . . . . . . . . . . . . . . 71
5.3 Predictive Sprint Feedback Asset . . . . . . . . . . . . . . . . . . . . . 86
5.4 Exploratory Sprint Feedback Asset . . . . . . . . . . . . . . . . . . . . 98
5.5 Technological Feasibility of the Feedback Concept . . . . . . . . . . . 114
xContents
6 Evaluation of the Computer-Aided Sprint Feedback Concept 117
6.1 Assessing ProDynamics in Industrial Software Projects . . . . . . . . 118
6.2 Action Research Process Applied in Practice . . . . . . . . . . . . . . 121
6.3 StudyRoadmap............................... 122
6.4 Quantitative and Qualitative Study Findings . . . . . . . . . . . . . . 128
6.5 ThreatstoValidity.............................. 146
6.6 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . 147
7 Conclusion 151
7.1 Limitations of the Present Research . . . . . . . . . . . . . . . . . . . . 154
7.2 Prospects for Future Research . . . . . . . . . . . . . . . . . . . . . . . 155
A Metrics and Issue Details in Agile Software Development 157
A.1 Common Metrics in Agile Software Development . . . . . . . . . . . 157
A.2 Supplementary Issue Details Covered in Jira . . . . . . . . . . . . . . 157
B System Dynamics Modeling Documents 161
B.1 Model Conceptualization Documents . . . . . . . . . . . . . . . . . . 161
B.2 Modular Structure of Sprint Dynamics Model . . . . . . . . . . . . . . 161
C GQM-Related Documentation 167
C.1 DenedGoals(G1-G4) ........................... 167
C.2 Defined Abstraction Sheets (AS2-AS4) . . . . . . . . . . . . . . . . . . 167
C.3 Defined Operational Question Set . . . . . . . . . . . . . . . . . . . . . 167
C.4 Defined Quantitative Metrics . . . . . . . . . . . . . . . . . . . . . . . 167
C.5 Overview as GQM-Model . . . . . . . . . . . . . . . . . . . . . . . . . 167
D Sprint Feedback Support in Jira 177
D.1 SurveyManager............................... 177
D.2 Sociological Team Survey . . . . . . . . . . . . . . . . . . . . . . . . . 177
D.3 Customer/Project Manager Satisfaction Survey . . . . . . . . . . . . 177
D.4 AnomalyNotication............................ 177
D.5 ProDynamics Feedback Assets . . . . . . . . . . . . . . . . . . . . . . 177
E Supervised Machine Learning 189
E.1 ModelSettings................................ 189
Bibliography 209
Curriculum Vitae 211
List of Scientific Publications 213
xi
List of Figures
1.1 Adaption of the DIKW Pyramid, based on Ackoff [6] . . . . . . . . . 3
1.2 Applied Design Science Research, based on [92] . . . . . . . . . . . . 6
1.3 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1 Layered Behavioral Model, cf. [53] . . . . . . . . . . . . . . . . . . . . 12
2.2 Scrum Development Cycle with Agile Practices, cf. [59] . . . . . . . . 13
2.3 Development Progress in a Sprint Visualized as Burndown Chart . . 15
2.4 Complexity Growth of Communication Networks [145] . . . . . . . . 18
2.5 Centralized and Decentralized Communication Networks [15] . . . . 18
2.6 Example of Dysfunctional Team Communication Networks . . . . . 19
2.7 Bipolar Mood Model of Positive and Negative Affects, cf. [224, 225] . 21
2.8 Data Science Techniques Applied in this Research, cf. [84] . . . . . . . 24
2.9 Cognitive Pattern Recognition on Code Commits using Heat Maps . 26
2.10 Forecast Based on a Shift Pattern of Highly Productive Weekdays . . 26
2.11 Simplified Knowledge Discovery Support Chain, cf. [71] . . . . . . . 27
2.12 Example of Linear and Polynomial Regression Function, cf. [137] . . 30
2.13 Machine Learning: Model Training, Testing and Prediction, cf. [13] . 32
2.14 Example of K-Fold Cross-Validation with K= 10 ........... 33
2.15 Exploratory Relationship Finding: MIC =.7and Pearson r=.0. . . 35
2.16 Example of a Causal Loop Diagram, based on Brooks’s Law [158] . . 37
2.17 Elements of a Stock and Flow Model [158] . . . . . . . . . . . . . . . . 38
2.18 Example of a Stock and Flow Model, based Brooks’s Law [158] . . . . 38
2.19 Example of Sensitivity Analyses for the Productivity . . . . . . . . . . 40
4.1 Activity-Related Perspective on Team Performance in Scrum . . . . . 51
4.2 GQM-Relevant Information Assets and Associations, based on [17] . 52
4.3 Abstract Goals for Understanding Team Dynamics in ASD . . . . . . 53
4.4 Abstraction Sheet for “G1”, according to [17] . . . . . . . . . . . . . . 54
4.5 Interval-based Data Measurements in Sprints . . . . . . . . . . . . . . 59
4.6 Subjective Measurement of Team Communication in Sprints . . . . . 61
4.7 Data Capturing Concept Applied in Student Software Projects . . . . 63
5.1 Process Layer of the Computer-Aided Sprint Feedback Concept . . . 66
5.2 Preprocessing: Data Merging, Clustering, and Transformation . . . . 70
5.3 Retrospective Sprint Feedback Processing as Flow-Activity , cf. [207] 73
5.4 Course and Anomaly Characterization: Meeting Behavior . . . . . . 74
5.5 Iterative Visualization Stages of the Feedback Modules . . . . . . . . 76
5.6 Retrospective Module: Sprint Board Player . . . . . . . . . . . . . . . 77
5.7 Retrospective Module: Team Communication . . . . . . . . . . . . . . 78
5.8 Retrospective Module: Mood and Satisfaction . . . . . . . . . . . . . 81
xii List of Figures
5.9 Retrospective Module: Productivity Comparison . . . . . . . . . . . . 83
5.10 Retrospective Module: Interaction Revealer . . . . . . . . . . . . . . . 85
5.11 ML Prediction Processing with Automated Feature Selection . . . . . 87
5.12 Schema for the ML Model Training Data . . . . . . . . . . . . . . . . . 91
5.13 Feature Selection Routine Prior Training the Final ML Model, cf. [221] 92
5.14 Weekly RMSE from Different ML-Models of Thirteen Student Projects 94
5.15 Weekly RMSE from Different ML-Models of Sample Student Project . 94
5.16 Visualization Concept of the Predictive Sprint Feedback Module . . . 97
5.17 Automated Dependency Analyses and Visualization Process . . . . . 99
5.18 Visualization Concept of the Sprint Dependencies Module . . . . . . 103
5.19 Sprint Dynamics Modeling Process . . . . . . . . . . . . . . . . . . . . 106
5.20 Preliminary Sprint Relationship Diagram . . . . . . . . . . . . . . . . 107
5.21 Relationship Module for Productivity and Commitment Constancy . 108
5.22 Graph-Based Equation Example for Dynamic Auxiliary . . . . . . . . 109
5.23 Relative Productivity Variance for 100 Sprint Samples, cf [128] . . . . 110
5.24 Dashboard Visualization of the Sprint Dynamics Module . . . . . . . 113
6.1 Action Research Cycle, cf. [19, 202] . . . . . . . . . . . . . . . . . . . . 120
6.2 Report Scheme Applied in the Action Research Cycle, cf. [202] . . . . 121
6.3 Study Roadmap for the Module-focused Action Research Cycle . . . 123
6.4 Socio-Technical Data Coverage in sprints with Participatory Changes 129
6.5 Extended Development Workflow in the Two Industrial Projects . . . 131
6.6 Observed Team Communication Network in Project 1 . . . . . . . . . 135
6.7 Observed Team Communication Network in Project 2 . . . . . . . . . 135
6.8 Observed Balance of Moods for Project 1 and Project 2 . . . . . . . . . 137
6.9 Velocity-Based Performance Variation and Trends in the Projects . . . 140
6.10 Observed Interaction Networks in Project 1 . . . . . . . . . . . . . . . 141
6.11 Observed Interaction Networks in Project 2 . . . . . . . . . . . . . . . 141
6.12 Predictive Sprint Feedback Validation of Project 1 . . . . . . . . . . . 143
6.13 Predictive Sprint Feedback Validation of Project 2 . . . . . . . . . . . 143
6.14 Significant Dependencies Throughout Nine Sprint in Project 1 . . . . 145
xiii
List of Tables
2.1 Team Performance Related Velocity Metrics, based on [63, 85] . . . . 16
2.2 Adapted PANAS-SF for Mood Affects Survey, cf. [207, 225] . . . . . . 22
2.3 Subjective Team Performance Assessment, based on [117, 207] . . . . 23
4.1 Set of Relevant Human Factors from Software Engineering Literature 49
4.2 GQM Definition Step 2: Example Defined Question Set for G1. . . . 54
5.1 Socio-Technical Attribute Types and Statistical Operations, cf. [221] . 68
5.2 Example of the Exploratory Analysis Output Format . . . . . . . . . 100
5.3 Network Graph Components for Visualizing Sprint Dependencies . 101
6.1 Characteristics of the Two Evaluated Software Projects in Industry . 119
6.2 Situation Assessment Summary for the Productivity Module . . . . . 124
6.3 Problem Intervention Summary for the Productivity Module . . . . . 125
6.4 Reflection Summary for the Productivity Module . . . . . . . . . . . . 127
6.5 Observed Usage of the Retrospective Player Module in Practice . . . 132
6.6 Observed Usage of the Communication Module . . . . . . . . . . . . 133
6.7 Reported Utility of the Communication and Meeting Module . . . . 134
6.8 Observed Usage of the Mood and Satisfaction Module . . . . . . . . 136
6.9 Observed Usage of the Productivity Comparison Module . . . . . . . 139
6.10 Observed Usage of the Task Interaction Module . . . . . . . . . . . . 140
6.11 Validation of Dynamic Model Selection versus Static . . . . . . . . . . 143
6.12 Observed Usage of the Predictive Module . . . . . . . . . . . . . . . . 144
6.13 Observed Usage of the Sprint Dependency Module . . . . . . . . . . 144
6.14 Observed Usage of the System Dynamics Module . . . . . . . . . . . 145
193
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