Core Concepts
Comprehensive population analysis is crucial for ensuring the representativeness and generalizability of empirical software engineering research findings.
Abstract
This position paper highlights the importance of population analysis in software engineering (SE) research. The author explores the challenges in analyzing different types of populations, including individual software engineers, organizations, software projects, and software artifacts.
Key insights:
- Sampling techniques are well-established in SE research, but without proper characterization of the target population, the question "Who's actually being studied?" remains unaddressed.
- Distinguishing between generalizability (how findings apply to the target population) and transferability (relevance in comparable settings) is crucial for evolving SE research.
- Analyzing the population of individual software engineers is challenging due to the lack of comprehensive census data and the need to consider diverse expertise levels, from students to experienced professionals.
- Organizational population analysis is complex due to ambiguity in defining "software development organizations" and the need to consider factors like culture, structure, and processes.
- Characterizing the diversity of software projects, in terms of size, complexity, development process, and other key aspects, is essential for meaningful generalization.
- When investigating software artifacts, the study's goals and the overall distribution of relevant metrics (e.g., DORA metrics for DevOps practices) should guide the population analysis.
The author proposes a set of practices to address these challenges, including:
- Establishing clear population definitions and boundaries.
- Identifying and leveraging existing population datasets.
- Expanding and diversifying population datasets.
- Cross-referencing and validating datasets.
- Employing advanced sampling techniques like snowballing and stratification.
- Thoroughly reporting and documenting the population frame.
The paper emphasizes the need for robust population analysis to ensure the empirical rigor and external validity of SE research.
Stats
"The overall population of software developers in 2023 was estimated to be 27.7 million and 26.3 million, respectively, according to two different demographic studies."
Quotes
"If the population is properly described, it is left to the reader to determine the applicability of these findings to their own practice."
"Accurate generalization depends not only on the sample size, but on a comprehensive understanding of the entire range of characteristics and variations present within the target population."
"To ensure that the results have meaningful implications, a precise description of the studied population's characteristics is required."