SENov 21, 2017

Grouping Environmental Factors Influencing Individual Decision-Making Behavior in Software Projects: A Cluster Analysis

arXiv:1711.07867v19 citations
Originality Synthesis-oriented
AI Analysis

This provides a comprehensive framework for researchers and practitioners to better understand and manage factors affecting decision-making in software projects, though it is incremental as it builds on prior identification of factors.

The authors tackled the lack of an objective taxonomy for environmental factors influencing decision-making in software projects by developing a semantic similarity method using WordNet and applying k-means clustering, resulting in a taxonomy with eleven broad categories and sub-categories.

An individual decision-making behavior is heavily influenced by and adapted to external environmental factors. Given that software development is a human-centered activity, individual decision-making behavior may affect the software project quality. Although environmental factors affecting decision-making behavior in software projects have been identified in prior literature, there is not yet an objective and a full taxonomy of these factors. Thus, it is not trivial to manage these complex and diverse factors. To address this deficiency, we first design a semantic similarity algorithm between words by utilizing the synonymy and hypernymy relationships in WordNet. Further, we propose a method to measure semantic similarity between phrases and apply it into k-means clustering algorithm to group these factors. Subsequently, we obtain a taxonomy of the environmental factors affecting individual decision-making behavior in software projects, which includes eleven broad categories, each containing two to five sub-categories. The taxonomy presented herein is obtained by an objective method, and quite comprehensive, with appropriate references provided. The taxonomy holds significant value for researchers and practitioners; it can help them to better understand the major aspects of environmental factors, also to predict and guide the behavior of individuals during decision making towards a successful completion of software projects.

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