Tutorial Debriefing: Applied Statistical Causal Inference in Requirements Engineering

TL;DR

This paper discusses how to reliably infer causal relationships in requirements engineering using statistical methods, especially when controlled experiments are infeasible, to better transfer research knowledge into practice.

Contribution

It introduces applied statistical causal inference techniques tailored for requirements engineering to improve evidence-based decision making from observational data.

Findings

Demonstrates the importance of causal inference in requirements engineering.

Provides guidelines for applying statistical causal methods in practice.

Highlights challenges and solutions in observational data analysis.

Abstract

As any scientific discipline, the software engineering (SE) research community strives to contribute to the betterment of the target population of our research: software producers and consumers. We will only achieve this betterment if we manage to transfer the knowledge acquired during research into practice. This transferal of knowledge may come in the form of tools, processes, and guidelines for software developers. However, the value of these contributions hinges on the assumption that applying them causes an improvement of the development process, user experience, or other performance metrics. Such a promise requires evidence of causal relationships between an exposure or intervention (i.e., the contributed tool, process or guideline) and an outcome (i.e., performance metrics). A straight-forward approach to obtaining this evidence is via controlled experiments in which a sample of a population is randomly divided into a group exposed to the new tool, process, or guideline, and a control group. However, such randomized control trials may not be legally, ethically, or logistically feasible. In these cases, we need a reliable process for statistical causal inference (SCI) from observational data.