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Do Programmers Prefer Predictable Expressions in Code?

Casey Casalnuovo1, Kevin Lee1, Hulin Wang1

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Summary
This summary is machine-generated.

Programmers prefer more predictable code expressions, aligning with predictions from language models. This study explored surprisal in programming languages, finding that developers favor code that is easier for models to anticipate.

Keywords:
Dual channel constraintsHuman preferenceLanguage modelsMeaning-preserving transformationsSource code expressionsSurprisal

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Software Engineering

Background:

  • Source code serves as human communication, constrained by execution requirements.
  • Programming languages are expressive yet predictable, enabling tools that use language model surprisal.
  • The relationship between surprisal and cognitive load in code remains under-explored.

Purpose of the Study:

  • To investigate the link between surprisal and programmer preference in code expressions.
  • To determine if programmers favor more predictable code variants.

Main Methods:

  • Generated meaning-preserving code transformations for Java and Python.
  • Conducted a corpus study analyzing language model surprisal of developer-written code.
  • Performed human subject studies comparing programmer preferences for code snippets with varying surprisal scores.

Main Results:

  • Developer-written code expressions were generally rated as more predictable by language models.
  • Programmers consistently preferred code variants with lower surprisal scores (more predictable).
  • Stronger language models, like transformers, better aligned with these programmer preferences.

Conclusions:

  • Programmer preference in code aligns with predictability as measured by language model surprisal.
  • Predictability is a key factor in how programmers evaluate and choose code expressions.
  • Language models can effectively capture and predict programmer preferences for code clarity and simplicity.