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Development and Validation of a Descriptive Cognitive Model for Predicting Usability Issues in a Low-Code Development

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A new Descriptive Cognitive Model (DCM) successfully identified usability issues in low-code development platforms (LCDPs). The model distinguished between expert and novice user problems, aiding in predicting interaction challenges.

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Area of Science:

  • Software Engineering
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Low-code development platforms (LCDPs) increase software development abstraction levels.
  • Understanding user programming conceptualization is key for effective LCDP design.
  • Identifying the gap between user conceptualization and platform requirements is crucial.

Purpose of the Study:

  • To develop and evaluate a Descriptive Cognitive Model (DCM) for identifying usability issues in LCDPs.
  • To analyze how end-user skill levels influence programming task conceptualization within LCDPs.

Main Methods:

  • Proposed a DCM named PRECOG, integrating task decomposition, knowledge-based descriptions, and criticality analysis.
  • Validated PRECOG using empirical techniques with 20 programmers (10 experts, 10 novices) interacting with an LCDP.
  • Analyzed user interactions based on the PRECOG DCM to assess performance and identify issues.

Main Results:

  • The DCM accurately identified usability problems experienced by new LCDP users.
  • Observed qualitative differences in issue patterns between expert and novice users.
  • Experts encountered primarily interaction-related issues, while novices faced challenges due to programming skill deficits.

Conclusions:

  • The developed DCM effectively predicted three types of interaction problems for novice LCDP users.
  • The PRECOG model is applicable for identifying potential interaction problems stemming from insufficient user background knowledge.