926 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 Gold Standard for Emotion Annotation in Stack Overflow
Software developers experience and share a wide range of emotions throughout
a rich ecosystem of communication channels. A recent trend that has emerged in
empirical software engineering studies is leveraging sentiment analysis of
developers' communication traces. We release a dataset of 4,800 questions,
answers, and comments from Stack Overflow, manually annotated for emotions. Our
dataset contributes to the building of a shared corpus of annotated resources
to support research on emotion awareness in software development.Comment: To appear in Proceedings of the 15th International Conference on
Mining Software Repositories (MSR '18) Data Showcase Track, 28-29 May,
Gothenburg, Swede
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
Consequences of Unhappiness While Developing Software
The growing literature on affect among software developers mostly reports on
the linkage between happiness, software quality, and developer productivity.
Understanding the positive side of happiness -- positive emotions and moods --
is an attractive and important endeavor. Scholars in industrial and
organizational psychology have suggested that also studying the negative side
-- unhappiness -- could lead to cost-effective ways of enhancing working
conditions, job performance, and to limiting the occurrence of psychological
disorders. Our comprehension of the consequences of (un)happiness among
developers is still too shallow, and is mainly expressed in terms of
development productivity and software quality. In this paper, we attempt to
uncover the experienced consequences of unhappiness among software developers.
Using qualitative data analysis of the responses given by 181 questionnaire
participants, we identified 49 consequences of unhappiness while doing software
development. We found detrimental consequences on developers' mental
well-being, the software development process, and the produced artifacts. Our
classification scheme, available as open data, will spawn new happiness
research opportunities of cause-effect type, and it can act as a guideline for
practitioners for identifying damaging effects of unhappiness and for fostering
happiness on the job.Comment: 6 pages. To be presented at the Second International Workshop on
Emotion Awareness in Software Engineering, colocated with the 39th
International Conference on Software Engineering (ICSE'17). Extended version
of arXiv:1701.02952v2 [cs.SE
A Benchmark Study on Sentiment Analysis for Software Engineering Research
A recent research trend has emerged to identify developers' emotions, by
applying sentiment analysis to the content of communication traces left in
collaborative development environments. Trying to overcome the limitations
posed by using off-the-shelf sentiment analysis tools, researchers recently
started to develop their own tools for the software engineering domain. In this
paper, we report a benchmark study to assess the performance and reliability of
three sentiment analysis tools specifically customized for software
engineering. Furthermore, we offer a reflection on the open challenges, as they
emerge from a qualitative analysis of misclassified texts.Comment: Proceedings of 15th International Conference on Mining Software
Repositories (MSR 2018
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