Journal article
Finding help with programming errors: An exploratory study of novice software engineers' focus in stack overflow posts
The Journal of systems and software, v 159, p110454
01 Jan 2020
Abstract
Monthly, 50 million users visit Stack Overflow, a popular Q&A forum used by software developers, to share and gather knowledge and help with coding problems. Although Q&A forums serve as a good resource for seeking help from developers beyond the local team, the abundance of information can cause developers, especially novice software engineers, to spend considerable time in identifying relevant answers and suitable suggested fixes.
This exploratory study aims to understand how novice software engineers direct their efforts and what kinds of information they focus on within a post selected from the results returned in response to a search query on Stack Overflow. The results can be leveraged to improve the Q&A forum interface, guide tools for mining forums, and potentially improve granularity of traceability mappings involving forum posts. We qualitatively analyze the novice software engineers' perceptions from a survey as well as their annotations of a set of Stack Overflow posts. Our results indicate that novice software engineers pay attention to only 27% of code and 15-21% of text in a Stack Overflow post to understand and determine how to apply the relevant information to their context. Our results also discern the kinds of information prominent in that focus. (C) 2019 Elsevier Inc. All rights reserved.
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Details
- Title
- Finding help with programming errors: An exploratory study of novice software engineers' focus in stack overflow posts
- Creators
- Preetha Chatterjee - University of DelawareMinji Kong - University of DelawareLori Pollock - University of Delaware
- Publication Details
- The Journal of systems and software, v 159, p110454
- Publisher
- Elsevier
- Number of pages
- 13
- Grant note
- 1813253; 1422184 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000502883100016
- Scopus ID
- 2-s2.0-85074504797
- Other Identifier
- 991021883914404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Software Engineering
- Computer Science, Theory & Methods