How Do Practitioners Interpret Conditionals in Requirements?

TL;DR

This study investigates how practitioners interpret conditional statements in software requirements, revealing ambiguity and disagreement that can impact downstream activities like testing and analysis.

Contribution

It provides empirical insights into the varied interpretations of conditionals in requirements among practitioners, highlighting the need for clearer guidelines.

Findings

Practitioners often disagree on whether antecedents are sufficient or necessary.

Ambiguity persists even when system behavior is known.

Cue phrases influence interpretation of conditionals.

Abstract

Context: Conditional statements like "If A and B then C" are core elements for describing software requirements. However, there are many ways to express such conditionals in natural language and also many ways how they can be interpreted. We hypothesize that conditional statements in requirements are a source of ambiguity, potentially affecting downstream activities such as test case generation negatively. Objective: Our goal is to understand how specific conditionals are interpreted by readers who work with requirements. Method: We conduct a descriptive survey with 104 RE practitioners and ask how they interpret 12 different conditional clauses. We map their interpretations to logical formulas written in Propositional (Temporal) Logic and discuss the implications. Results: The conditionals in our tested requirements were interpreted ambiguously. We found that practitioners disagree on whether an antecedent is only sufficient or also necessary for the consequent. Interestingly, the disagreement persists even when the system behavior is known to the practitioners. We also found that certain cue phrases are associated with specific interpretations. Conclusion: Conditionals in requirements are a source of ambiguity and there is not just one way to interpret them formally. This affects any analysis that builds upon formalized requirements (e.g., inconsistency checking, test-case generation). Our results may also influence guidelines for writing requirements.