一句话：通过日记与访谈研究程序员如何利用线索筛选在线学习资源，并原型验证基于资源风格属性的线索辅助搜索。
程序员学习软件技术时面对风格与需求各异的在线资源，搜索引擎虽能过滤相关资源，但用户仍需逐个浏览才能判断其是否契合需求。既有工作多关注在资源内部定位相关信息，本文转而帮助程序员评估资源适用性、区分不同资源。作者先通过日记与访谈研究了解程序员在线寻找学习资源的方式，观察到他们会借助某些线索决定是否访问资源；随后基于发现实现原型工具，探索在多大程度上支持这种线索跟随过程。研究旨在支持学习资源搜索行为，并为资源创作者揭示程序员搜索时关注的重要因素。
When learning a software technology, programmers face a large variety of resources in different styles and catering to different requirements. Although search engines are helpful to filter relevant resources, programmers are still required to manually go through a number of resources before they find one pertinent to their needs. Prior work has largely concentrated on helping programmers find the precise location of relevant information within a resource. Our work focuses on helping programmers assess the pertinence of resources to differentiate between resources. We investigated how programmers find learning resources online via a diary and interview study, and observed that programmers use certain cues to determine whether to access a resource. Based on our findings, we investigate the extent to which we can support the cue-following process via a prototype tool. Our research supports programmers’ search behaviour for software technology learning resources to inform resource creators on important factors that programmers look for during their search.
DOI 原文 ·
@inproceedings{paperbot7001,
title = {This is your cue! assisting search behaviour with resource style properties},
author = {Deeksha M. Arya},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3558909}
}