114,505 research outputs found
Ethical Issues in Empirical Studies of Software Engineering
The popularity of empirical methods in software engineering research is on the rise. Surveys,
experiments, metrics, case studies, and field studies are examples of empirical methods used to
investigate both software engineering processes and products. The increased application of
empirical methods has also brought about an increase in discussions about adapting these
methods to the peculiarities of software engineering. In contrast, the ethical issues raised by
empirical methods have received little, if any, attention in the software engineering literature. This
article is intended to introduce the ethical issues raised by empirical research to the software
engineering research community, and to stimulate discussion of how best to deal with these ethical
issues. Through a review of the ethical codes of several fields that commonly employ humans and
artifacts as research subjects, we have identified major ethical issues relevant to empirical studies
of software engineering. These issues are illustrated with real empirical studies of software
engineering
Worse Than Spam: Issues In Sampling Software Developers
Background: Reaching out to professional software developers is a crucial
part of empirical software engineering research. One important method to
investigate the state of practice is survey research. As drawing a random
sample of professional software developers for a survey is rarely possible,
researchers rely on various sampling strategies. Objective: In this paper, we
report on our experience with different sampling strategies we employed,
highlight ethical issues, and motivate the need to maintain a collection of key
demographics about software developers to ease the assessment of the external
validity of studies. Method: Our report is based on data from two studies we
conducted in the past. Results: Contacting developers over public media proved
to be the most effective and efficient sampling strategy. However, we not only
describe the perspective of researchers who are interested in reaching goals
like a large number of participants or a high response rate, but we also shed
light onto ethical implications of different sampling strategies. We present
one specific ethical guideline and point to debates in other research
communities to start a discussion in the software engineering research
community about which sampling strategies should be considered ethical.Comment: 6 pages, 2 figures, Proceedings of the 2016 ACM/IEEE International
Symposium on Empirical Software Engineering and Measurement (ESEM 2016), ACM,
201
Motivation, Design, and Ubiquity: A Discussion of Research Ethics and Computer Science
Modern society is permeated with computers, and the software that controls
them can have latent, long-term, and immediate effects that reach far beyond
the actual users of these systems. This places researchers in Computer Science
and Software Engineering in a critical position of influence and
responsibility, more than any other field because computer systems are vital
research tools for other disciplines. This essay presents several key ethical
concerns and responsibilities relating to research in computing. The goal is to
promote awareness and discussion of ethical issues among computer science
researchers. A hypothetical case study is provided, along with questions for
reflection and discussion.Comment: Written as central essay for the Computer Science module of the
LANGURE model curriculum in Research Ethic
A Unified Checklist for Observational and Experimental Research in Software Engineering (Version 1)
Current checklists for empirical software engineering cover either experimental research or case study research but ignore the many commonalities that exist across all kinds of empirical research. Identifying these commonalities, and explaining why they exist, would enhance our understanding of empirical research in general and of the differences between experimental and case study research in particular. In this report we design a unified checklist for empirical research, and identify commonalities and differences between experimental and case study research. We design the unified checklist as a specialization of the general engineering cycle, which itself is a special case of the rational choice cycle. We then compare the resulting empirical research cycle with two checklists for experimental research, and with one checklist for case study research. The resulting checklist identifies important questions to be answered in experimental and case study research design and reports. The checklist provides insights in two different types of empirical research design and their relationships. Its limitations are that it ignores other research methods such as meta-research or surveys. It has been tested so far only in our own research designs and in teaching empirical methods. Future work includes expanding the comparison with other methods and application in more cases, by others than ourselves
Ethically Aligned Design: An empirical evaluation of the RESOLVEDD-strategy in Software and Systems development context
Use of artificial intelligence (AI) in human contexts calls for ethical
considerations for the design and development of AI-based systems. However,
little knowledge currently exists on how to provide useful and tangible tools
that could help software developers and designers implement ethical
considerations into practice. In this paper, we empirically evaluate a method
that enables ethically aligned design in a decision-making process. Though this
method, titled the RESOLVEDD-strategy, originates from the field of business
ethics, it is being applied in other fields as well. We tested the
RESOLVEDD-strategy in a multiple case study of five student projects where the
use of ethical tools was given as one of the design requirements. A key finding
from the study indicates that simply the presence of an ethical tool has an
effect on ethical consideration, creating more responsibility even in instances
where the use of the tool is not intrinsically motivated.Comment: This is the author's version of the work. The copyright holder's
version can be found at https://doi.org/10.1109/SEAA.2019.0001
A Value-Sensitive Design Approach to Intelligent Agents
This chapter proposed a novel design methodology called Value-Sensitive Design and its potential application to the field of artificial intelligence research and design. It discusses the imperatives in adopting a design philosophy that embeds values into the design of artificial agents at the early stages of AI development. Because of the high risk stakes in the unmitigated design of artificial agents, this chapter proposes that even though VSD may turn out to be a less-than-optimal design methodology, it currently provides a framework that has the potential to embed stakeholder values and incorporate current design methods. The reader should begin to take away the importance of a proactive design approach to intelligent agents
Teaching psychology to computing students
The aim of this paper is twofold. The first aim is to discuss some observations gained from teaching Psychology to Computing students, highlighting both the wide range of areas where Psychology is relevant to Computing education and the topics that are relevant at different stages of students’ education. The second aim is to consider findings from research investigating the characteristics of Computing and Psychology students. It is proposed that this information could be considered in the design and use of Psychology materials for Computing students.
The format for the paper is as follows. Section one will illustrate the many links between the disciplines of Psychology & Computing; highlighting these links helps to answer the question that many Computing students ask, what can Psychology offer to Computing? Section two will then review some of the ways that I have been involved in teaching Psychology to Computing students, from A/AS level to undergraduate and postgraduate level. Section three will compare the profiles of Computing and Psychology students (e.g. on age, gender and motivation to study), to highlight how an understanding of these factors can be used to adapt Psychology teaching materials for Computing students. The conclusions which cover some practical suggestions are presented in section four
Preliminary Survey on Empirical Research Practices in Requirements Engineering
Context and Motivation:\ud
Based on published output in the premium RE conferences and journals, we observe a growing body of research using both quantitative and qualitative research methods to help understand which RE technique, process or tool work better in which context. Also, more and more empirical studies in RE aim at comparing and evaluating alternative techniques that are solutions to common problems. However, until now there have been few meta studies of the current state of knowledge about common practices carried out by researchers and practitioners in empirical RE. Also, surprisingly little has been published on how RE researchers perceive the usefulness of these best practices.\ud
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Objective:\ud
The goal of our study is to improve our understanding of what empirical practices are performed by researchers and practitioners in RE, for the purpose of understanding the extent to which the research methods of empirical software engineering are adopted in the RE community.\ud
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Method:\ud
We surveyed the practices that participants of the REFSQ conference have been using in their empirical research projects. The survey was part of the REFSQ 2012 Empirical Track.\ud
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Conclusions:\ud
We found that there are 15 commonly used practices out of a set of 27. The study has two implications: first it presents a list of practices that are commonly used in the RE community, and a list of practices that still remain to be practiced. Researchers may now make an informed decision on how to extend the practices they use in producing and executing their research designs, so that their designs get better. Second, we found that senior researchers and PhD students do not always converge in their perceptions about the usefulness of research practices. Whether this is all right and whether something needs to be done in the face of this finding remains an open question
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