10,088 research outputs found
Visual diagnosis of tree boosting methods
Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms
LLM-based Interaction for Content Generation: A Case Study on the Perception of Employees in an IT department
In the past years, AI has seen many advances in the field of NLP. This has
led to the emergence of LLMs, such as the now famous GPT-3.5, which
revolutionise the way humans can access or generate content. Current studies on
LLM-based generative tools are mainly interested in the performance of such
tools in generating relevant content (code, text or image). However, ethical
concerns related to the design and use of generative tools seem to be growing,
impacting the public acceptability for specific tasks. This paper presents a
questionnaire survey to identify the intention to use generative tools by
employees of an IT company in the context of their work. This survey is based
on empirical models measuring intention to use (TAM by Davis, 1989, and UTAUT2
by Venkatesh and al., 2008). Our results indicate a rather average
acceptability of generative tools, although the more useful the tool is
perceived to be, the higher the intention to use seems to be. Furthermore, our
analyses suggest that the frequency of use of generative tools is likely to be
a key factor in understanding how employees perceive these tools in the context
of their work. Following on from this work, we plan to investigate the nature
of the requests that may be made to these tools by specific audiences.Comment: 14 pages (bibliography inclued), 6 figures, preprint submitted to
Work-In-Progress session of ACM IMX'23 Interactive Media Experienc
Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability
Artificial Intelligence (AI) is rapidly integrating into various aspects of
our daily lives, influencing decision-making processes in areas such as
targeted advertising and matchmaking algorithms. As AI systems become
increasingly sophisticated, ensuring their transparency and explainability
becomes crucial. Functional transparency is a fundamental aspect of algorithmic
decision-making systems, allowing stakeholders to comprehend the inner workings
of these systems and enabling them to evaluate their fairness and accuracy.
However, achieving functional transparency poses significant challenges that
need to be addressed. In this paper, we propose a design for user-centered
compliant-by-design transparency in transparent systems. We emphasize that the
development of transparent and explainable AI systems is a complex and
multidisciplinary endeavor, necessitating collaboration among researchers from
diverse fields such as computer science, artificial intelligence, ethics, law,
and social science. By providing a comprehensive understanding of the
challenges associated with transparency in AI systems and proposing a
user-centered design framework, we aim to facilitate the development of AI
systems that are accountable, trustworthy, and aligned with societal values.Comment: Hosain, M. T. , Anik, M. H. , Rafi, S. , Tabassum, R. , Insia, K. &
S{\i}dd{\i}ky, M. M. (). Path To Gain Functional Transparency In Artificial
Intelligence With Meaningful Explainability . Journal of Metaverse , 3 (2) ,
166-180 . DOI: 10.57019/jmv.130668
A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges
The software engineering community recently has witnessed widespread
deployment of AI programming assistants, such as GitHub Copilot. However, in
practice, developers do not accept AI programming assistants' initial
suggestions at a high frequency. This leaves a number of open questions related
to the usability of these tools. To understand developers' practices while
using these tools and the important usability challenges they face, we
administered a survey to a large population of developers and received
responses from a diverse set of 410 developers. Through a mix of qualitative
and quantitative analyses, we found that developers are most motivated to use
AI programming assistants because they help developers reduce key-strokes,
finish programming tasks quickly, and recall syntax, but resonate less with
using them to help brainstorm potential solutions. We also found the most
important reasons why developers do not use these tools are because these tools
do not output code that addresses certain functional or non-functional
requirements and because developers have trouble controlling the tool to
generate the desired output. Our findings have implications for both creators
and users of AI programming assistants, such as designing minimal cognitive
effort interactions with these tools to reduce distractions for users while
they are programming.Comment: Accepted to ICSE'2
Self-Adaptation in Industry: A Survey
Computing systems form the backbone of many areas in our society, from
manufacturing to traffic control, healthcare, and financial systems. When
software plays a vital role in the design, construction, and operation, these
systems are referred as software-intensive systems. Self-adaptation equips a
software-intensive system with a feedback loop that either automates tasks that
otherwise need to be performed by human operators or deals with uncertain
conditions. Such feedback loops have found their way to a variety of practical
applications; typical examples are an elastic cloud to adapt computing
resources and automated server management to respond quickly to business needs.
To gain insight into the motivations for applying self-adaptation in practice,
the problems solved using self-adaptation and how these problems are solved,
and the difficulties and risks that industry faces in adopting self-adaptation,
we performed a large-scale survey. We received 184 valid responses from
practitioners spread over 21 countries. Based on the analysis of the survey
data, we provide an empirically grounded overview of state-of-the-practice in
the application of self-adaptation. From that, we derive insights for
researchers to check their current research with industrial needs, and for
practitioners to compare their current practice in applying self-adaptation.
These insights also provide opportunities for the application of
self-adaptation in practice and pave the way for future industry-research
collaborations.Comment: 43 page
Insights into Software Development Approaches: Mining Q&A Repositories
Context: Software practitioners adopt approaches like DevOps, Scrum, and
Waterfall for high-quality software development. However, limited research has
been conducted on exploring software development approaches concerning
practitioners discussions on Q&A forums. Objective: We conducted an empirical
study to analyze developers discussions on Q&A forums to gain insights into
software development approaches in practice. Method: We analyzed 13,903
developers posts across Stack Overflow (SO), Software Engineering Stack
Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed
method approach, consisting of the topic modeling technique (i.e., Latent
Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify
frequently discussed topics of software development approaches, trends
(popular, difficult topics), and the challenges faced by practitioners in
adopting different software development approaches. Findings: We identified 15
frequently mentioned software development approaches topics on Q&A sites and
observed an increase in trends for the top-3 most difficult topics requiring
more attention. Finally, our study identified 49 challenges faced by
practitioners while deploying various software development approaches, and we
subsequently created a thematic map to represent these findings. Conclusions:
The study findings serve as a useful resource for practitioners to overcome
challenges, stay informed about current trends, and ultimately improve the
quality of software products they develop
Insights into software development approaches: mining Q &A repositories
© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Context: Software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q &A forums. Objective: We conducted an empirical study to analyze developers’ discussions on Q &A forums to gain insights into software development approaches in practice. Method: We analyzed 13,903 developers’ posts across Stack Overflow (SO), Software Engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings: We identified 15 frequently mentioned software development approaches topics on Q &A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions: The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop.Peer reviewe
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