CodeDeviant: Helping Programmers Detect Edits That Accidentally Alter Program Behavior

Abstract

In this paper, we present CodeDeviant, a novel tool for visual dataflow programming environments that assists programmers by helping them ensure that their code-restructuring changes did not accidentally alter the behavior of the application. CodeDeviant aims to integrate seamlessly into a programmer's workflow, requiring little or no additional effort or planning. Key features of CodeDeviant include transparently recording program execution data, enabling programmers to efficiently compare program outputs, and allowing only apt comparisons between executions. We report a formative qualitative-shadowing study of LabViewprogrammers, which motivated CodeDeviant's design, revealing that the programmers had considerable difficulty determining whether code changes they made resulted in unintended program behavior. To evaluate Code-Deviant, we implemented a prototype CodeDeviant extension for LabViewand used it to conduct a laboratory user study. Key results included that programmers using CodeDeviant discovered behavior-altering changes more accurately and in less time than programmers using standard LabView.