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International Journal of Human-Computer Studies
Article . 2025 . Peer-reviewed
License: CC BY
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Making trigger-action rules more comprehensible: Investigating which linguistic clues effectively guide non-programmers

Authors: Andrao, Margherita; Treccani, Barbara; Zancanaro, Massimo;

Making trigger-action rules more comprehensible: Investigating which linguistic clues effectively guide non-programmers

Abstract

Trigger-Action Programming is a commonly used paradigm in End-User Development interfaces, allowing users without programming experience to create new automation systems. Even if considered easy to grasp, this approach poses some challenges: non-programmers often confuse events (instantaneous occurrences) and states (prolonged occurrences), leading to critical errors in the definition of triggers. Although past research has already questioned the effectiveness of the typical if-then structure, there is a limited exploration of which specific linguistic cues might help or hinder users from distinguishing between events and states. Our study, involving 85 non-programmers, examines a broader pool of linguistic aspects, investigating (i) preferences for conjunctions and verbs when describing events and states and (ii) which conjunctions help users accurately differentiate these occurrences. Our results indicate that while participants tended to prefer temporally specific language, such as ”when” for events and ”while” for states, some of these conjunctions, like ”when”, may not support users in accurately identifying and differentiating events from states, similar to the generic ”if”. These findings underscore the role of specific language on non-programmers’ comprehension and mental representations of triggers. Designing interfaces with more easily graspable linguistic cues and mapping them at the system level may help guide non-programmer users in correctly structuring trigger-action rules.

Country
Italy
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Keywords

End-user development; Language; Temporal aspects; Trigger-action programming; Conditional reasoning enhancement

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
hybrid