Supporting comprehension of unfamiliar programs by modeling cues

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• 作者：Naveen Kulkarni ; Vasudeva Varma
• 关键词：Program comprehension ; Cues ; Information scent ; Integrated development environment ; Developer interaction ; Summarization
• 刊名：Software Quality Journal
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：25
• 期：1
• 页码：307-340
• 全文大小：
• 刊物主题：Software Engineering/Programming and Operating Systems; Programming Languages, Compilers, Interpreters; Data Structures, Cryptology and Information Theory; Operating Systems;
• 出版者：Springer US
• ISSN：1573-1367
• 卷排序：25

文摘

Developers need to comprehend a program before modifying it. In such cases, developers use cues to establish the relevance of a piece of information with a task. Being familiar with different kinds of cues will help the developers to comprehend a program faster. But unlike the experienced developers, novice developers fail to recognize the relevant cues, because (a) there are many cues and (b) they might be unfamiliar with the artifacts. However, not much is known about the developers’ choice of cue. Hence, we conducted two independent studies to understand the kind of cues used by the developers and how a tool influences their cue selection. First, from our user study on two common comprehension tasks, we found that developers actively seek the cues and their cue source choices are similar but task dependent. In our second exploratory study, we investigated whether an integrated development environment (IDE) influences a developer’s cue choices. By observing their interaction history while fixing bugs in Eclipse IDE, we found that the IDE’s influence on their cue choices was not statistically significant. Finally, as a case in point, we propose a novel task-specific program summarization approach to aid novice developers in comprehending an unfamiliar program. Our approach used developers cue choices to synthesize the summaries. A comparison of the synthesized summaries with the summaries recorded by the developers shows both had similar content. This promising result encourages us to explore task-specific cue models, which can aid novice developers to accomplish complex comprehension tasks faster.