Proceedings ArticleDOI
Security and emotion: sentiment analysis of security discussions on GitHub
Daniel Pletea,Bogdan Vasilescu,Alexander Serebrenik +2 more
- pp 348-351
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TLDR
The findings confirm the importance of properly training developers to address security concerns in their applications as well as the need to test applications thoroughly for security vulnerabilities in order to reduce frustration and improve overall project atmosphere.Abstract:
Application security is becoming increasingly prevalent during software and especially web application development. Consequently, countermeasures are continuously being discussed and built into applications, with the goal of reducing the risk that unauthorized code will be able to access, steal, modify, or delete sensitive data. In this paper we gauged the presence and atmosphere surrounding security-related discussions on GitHub, as mined from discussions around commits and pull requests. First, we found that security related discussions account for approximately 10% of all discussions on GitHub. Second, we found that more negative emotions are expressed in security-related discussions than in other discussions. These findings confirm the importance of properly training developers to address security concerns in their applications as well as the need to test applications thoroughly for security vulnerabilities in order to reduce frustration and improve overall project atmosphere.read more
Citations
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Journal ArticleDOI
What are mobile developers asking about? A large scale study using stack overflow
Christoffer Rosen,Emad Shihab +1 more
TL;DR: This paper uses data from the popular online Q&A site, Stack Overflow, and analyze 13,232,821 posts to examine what mobile developers ask about, and establishes a novel approach for analyzing questions asked onQ&A forums.
Proceedings ArticleDOI
Sentiment analysis for software engineering: how far can we go?
TL;DR: This work retrained—on a set of 40k manually labeled sentences/words extracted from Stack Overflow—a state-of-the-art sentiment analysis tool exploiting deep learning, and found the results were negative.
Journal ArticleDOI
Sentiment Polarity Detection for Software Development
TL;DR: Senti4SD as mentioned in this paper is a classifier specifically trained to support sentiment analysis in developers' communication channels, which is trained and validated using a gold standard of Stack Overflow questions, answers, and comments manually annotated for sentiment polarity.
Journal ArticleDOI
On negative results when using sentiment analysis tools for software engineering research
TL;DR: Whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other is studied.
Proceedings ArticleDOI
Are bullies more productive?: empirical study of affectiveness vs. issue fixing time
TL;DR: It is found that the happier developers are (expressing emotions such as JOY and LOVE in their comments), the shorter the issue fixing time is likely to be, and negative emotions, such as SADNESS, are linked with longerissue fixing time.
References
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Proceedings ArticleDOI
The GHTorent dataset and tool suite
TL;DR: The GHTorent project has been collecting data for all public projects available on Github for more than a year, and the dataset details and construction process are presented.
Book
The Security Development Lifecycle
Michael Howard,Steve Lipner +1 more
TL;DR: In this article, an introduction to the security development lifecycle (SDL) provides a history of the methodology and guides you through each stage of a proven process-from design to release-that helps minimize security defects.
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Glossary of Key Information Security Terms
TL;DR: This glossary of common security terms has been extracted from NIST Federal Information Processing Standards (FIPS), the Special Publication 800 series, NIST Interagency Reports (NISTIRs), and from the Committee for National Security Systems Instruction 4009 (CNSSI-4009).
Proceedings ArticleDOI
StackOverflow and GitHub: Associations between Software Development and Crowdsourced Knowledge
TL;DR: This paper investigates the interplay between Stack Overflow activities and the development process, reflected by code changes committed to the largest social coding repository, GitHub, and shows that active GitHub committers ask fewer questions and provide more answers than others.
Proceedings ArticleDOI
Topic Detection by Clustering Keywords
Christian Wartena,Rogier Brussee +1 more
TL;DR: Evaluation on Wikipedia articles shows that clusters of keywords correlate strongly with the Wikipedia categories of the articles, and a newly proposed term distribution taking co-occurrence of terms into account gives best results.