118 research outputs found
How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow
Context: The success of Stack Overflow and other community-based
question-and-answer (Q&A) sites depends mainly on the will of their members to
answer others' questions. In fact, when formulating requests on Q&A sites, we
are not simply seeking for information. Instead, we are also asking for other
people's help and feedback. Understanding the dynamics of the participation in
Q&A communities is essential to improve the value of crowdsourced knowledge.
Objective: In this paper, we investigate how information seekers can increase
the chance of eliciting a successful answer to their questions on Stack
Overflow by focusing on the following actionable factors: affect, presentation
quality, and time.
Method: We develop a conceptual framework of factors potentially influencing
the success of questions in Stack Overflow. We quantitatively analyze a set of
over 87K questions from the official Stack Overflow dump to assess the impact
of actionable factors on the success of technical requests. The information
seeker reputation is included as a control factor. Furthermore, to understand
the role played by affective states in the success of questions, we
qualitatively analyze questions containing positive and negative emotions.
Finally, a survey is conducted to understand how Stack Overflow users perceive
the guideline suggestions for writing questions.
Results: We found that regardless of user reputation, successful questions
are short, contain code snippets, and do not abuse with uppercase characters.
As regards affect, successful questions adopt a neutral emotional style.
Conclusion: We provide evidence-based guidelines for writing effective
questions on Stack Overflow that software engineers can follow to increase the
chance of getting technical help. As for the role of affect, we empirically
confirmed community guidelines that suggest avoiding rudeness in question
writing.Comment: Preprint, to appear in Information and Software Technolog
A Revision Control System for Image Editing in Collaborative Multimedia Design
Revision control is a vital component in the collaborative development of
artifacts such as software code and multimedia. While revision control has been
widely deployed for text files, very few attempts to control the versioning of
binary files can be found in the literature. This can be inconvenient for
graphics applications that use a significant amount of binary data, such as
images, videos, meshes, and animations. Existing strategies such as storing
whole files for individual revisions or simple binary deltas, respectively
consume significant storage and obscure semantic information. To overcome these
limitations, in this paper we present a revision control system for digital
images that stores revisions in form of graphs. Besides, being integrated with
Git, our revision control system also facilitates artistic creation processes
in common image editing and digital painting workflows. A preliminary user
study demonstrates the usability of the proposed system.Comment: pp. 512-517 (6 pages
EmoTxt: A Toolkit for Emotion Recognition from Text
We present EmoTxt, a toolkit for emotion recognition from text, trained and
tested on a gold standard of about 9K question, answers, and comments from
online interactions. We provide empirical evidence of the performance of
EmoTxt. To the best of our knowledge, EmoTxt is the first open-source toolkit
supporting both emotion recognition from text and training of custom emotion
classification models.Comment: In Proc. 7th Affective Computing and Intelligent Interaction
(ACII'17), San Antonio, TX, USA, Oct. 23-26, 2017, p. 79-80, ISBN:
978-1-5386-0563-
Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?
Assessing the personality of software engineers may help to match individual traits with the characteristics of development activities such as code review and testing, as well as support managers in team composition. However, self-assessment questionnaires are not a practical solution for collecting multiple observations on a large scale. Instead, automatic personality detection, while overcoming these limitations, is based on off-the-shelf solutions trained on non-technical corpora, which might not be readily applicable to technical domains like software engineering. In this paper, we first assess the performance of general-purpose personality detection tools when applied to a technical corpus of developers’ emails retrieved from the public archives of the Apache Software Foundation. We observe a general low accuracy of predictions and an overall disagreement among the tools. Second, we replicate two previous research studies in software engineering by replacing the personality detection tool used to infer developers’ personalities from pull-request discussions and emails. We observe that the original results are not confirmed, i.e., changing the tool used in the original study leads to diverging conclusions. Our results suggest a need for personality detection tools specially targeted for the software engineering domain
Adopting the eclipse communication framework: The case of eConference
eConference is a text-based conferencing tool, built upon the Eclipse Rich Client Platform (RCP), which has evolved over four versions since its first release in 2002. In the latest version, our tool has reached communication protocol independency thanks to the adoption of the Eclipse Communication Framework (ECF). This paper describes how the development of this new release of eConference has unexpectedly evolved due to the underestimated impact of adopting ECF as a network layer. The problems encountered have been tackled by developing an aspect-based framework, which promises to be applicable to other distributed applications built upon Eclipse RCP and with an emphasis on communication. Future improvements to both our tool and framework are also discussed
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