Determining Context Factors for Hybrid Development Methods with Trained Models

TL;DR

This paper investigates how specific project context factors influence the selection of development methods, using statistical analysis on a large dataset to identify key factors and method clusters for hybrid development approaches.

Contribution

It introduces a data-driven approach to identify key context factors and method clusters, aiding the design of hybrid development methods tailored to project contexts.

Findings

Project size and domain significantly influence method selection.

Five method clusters were identified as foundational for hybrid approaches.

Only a few context factors strongly correlate with method choices.

Abstract

Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. Every project is unique and, thus, many context factors have to be considered. Recent research took some initial steps towards statistically constructing hybrid development methods, yet, paid little attention to the peculiarities of context factors influencing method and practice selection. In this paper, we utilize exploratory factor analysis and logistic regression analysis to learn such context factors and to identify methods that are correlated with these factors. Our analysis is based on 829 data points from the HELENA dataset. We provide five base clusters of methods consisting of up to 10 methods that lay the foundation for devising hybrid development methods. The analysis of the five clusters using trained models reveals only a few context factors, e.g., project/product size and target application domain, that seem to significantly influence the selection of methods. An extended descriptive analysis of these practices in the context of the identified method clusters also suggests a consolidation of the relevant practice sets used in specific project contexts.