13,170 research outputs found
Analysis and Detection of Information Types of Open Source Software Issue Discussions
Most modern Issue Tracking Systems (ITSs) for open source software (OSS)
projects allow users to add comments to issues. Over time, these comments
accumulate into discussion threads embedded with rich information about the
software project, which can potentially satisfy the diverse needs of OSS
stakeholders. However, discovering and retrieving relevant information from the
discussion threads is a challenging task, especially when the discussions are
lengthy and the number of issues in ITSs are vast. In this paper, we address
this challenge by identifying the information types presented in OSS issue
discussions. Through qualitative content analysis of 15 complex issue threads
across three projects hosted on GitHub, we uncovered 16 information types and
created a labeled corpus containing 4656 sentences. Our investigation of
supervised, automated classification techniques indicated that, when prior
knowledge about the issue is available, Random Forest can effectively detect
most sentence types using conversational features such as the sentence length
and its position. When classifying sentences from new issues, Logistic
Regression can yield satisfactory performance using textual features for
certain information types, while falling short on others. Our work represents a
nontrivial first step towards tools and techniques for identifying and
obtaining the rich information recorded in the ITSs to support various software
engineering activities and to satisfy the diverse needs of OSS stakeholders.Comment: 41st ACM/IEEE International Conference on Software Engineering
(ICSE2019
Activity-Based Analysis of Open Source Software Contributors: Roles and Dynamics
Contributors to open source software (OSS) communities assume diverse roles
to take different responsibilities. One major limitation of the current OSS
tools and platforms is that they provide a uniform user interface regardless of
the activities performed by the various types of contributors. This paper
serves as a non-trivial first step towards resolving this challenge by
demonstrating a methodology and establishing knowledge to understand how the
contributors' roles and their dynamics, reflected in the activities
contributors perform, are exhibited in OSS communities. Based on an analysis of
user action data from 29 GitHub projects, we extracted six activities that
distinguished four Active roles and five Supporting roles of OSS contributors,
as well as patterns in role changes. Through the lens of the Activity Theory,
these findings provided rich design guidelines for OSS tools to support diverse
contributor roles.Comment: 12th International Workshop on Cooperative and Human Aspects of
Software Engineering (CHASE 2019
Approach Intelligent Writing Assistants Usability with Seven Stages of Action
Despite the potential of Large Language Models (LLMs) as writing assistants,
they are plagued by issues like coherence and fluency of the model output,
trustworthiness, ownership of the generated content, and predictability of
model performance, thereby limiting their usability. In this position paper, we
propose to adopt Norman's seven stages of action as a framework to approach the
interaction design of intelligent writing assistants. We illustrate the
framework's applicability to writing tasks by providing an example of software
tutorial authoring. The paper also discusses the framework as a tool to
synthesize research on the interaction design of LLM-based tools and presents
examples of tools that support the stages of action. Finally, we briefly
outline the potential of a framework for human-LLM interaction research.Comment: The Second Workshop on Intelligent and Interactive Writing Assistants
co-located with The ACM CHI Conference on Human Factors in Computing Systems
(CHI 2023
GUILGET: GUI Layout GEneration with Transformer
Sketching out Graphical User Interface (GUI) layout is part of the pipeline
of designing a GUI and a crucial task for the success of a software
application. Arranging all components inside a GUI layout manually is a
time-consuming task. In order to assist designers, we developed a method named
GUILGET to automatically generate GUI layouts from positional constraints
represented as GUI arrangement graphs (GUI-AGs). The goal is to support the
initial step of GUI design by producing realistic and diverse GUI layouts. The
existing image layout generation techniques often cannot incorporate GUI design
constraints. Thus, GUILGET needs to adapt existing techniques to generate GUI
layouts that obey to constraints specific to GUI designs. GUILGET is based on
transformers in order to capture the semantic in relationships between elements
from GUI-AG. Moreover, the model learns constraints through the minimization of
losses responsible for placing each component inside its parent layout, for not
letting components overlap if they are inside the same parent, and for
component alignment. Our experiments, which are conducted on the CLAY dataset,
reveal that our model has the best understanding of relationships from GUI-AG
and has the best performances in most of evaluation metrics. Therefore, our
work contributes to improved GUI layout generation by proposing a novel method
that effectively accounts for the constraints on GUI elements and paves the
road for a more efficient GUI design pipeline.Comment: 12 pages, 5 figures, Canadian AI Conference 202
How to Sustain a Scientific Open-Source Software Ecosystem: Learning from the Astropy Project
Scientific open-source software (OSS) has greatly benefited research
communities through its transparent and collaborative nature. Given its
critical role in scientific research, ensuring the sustainability of such
software has become vital. Earlier studies have proposed sustainability
strategies for conventional scientific software and open-source communities.
However, it remains unclear whether these solutions can be easily adapted to
the integrated framework of scientific OSS and its larger ecosystem. This study
examines the challenges and opportunities to enhance the sustainability of
scientific OSS in the context of interdisciplinary collaboration, open-source
community, and multi-project ecosystem. We conducted a case study on a
widely-used software ecosystem in the astrophysics domain, the Astropy Project,
using a mixed-methods design approach. This approach includes an interview with
core contributors regarding their participation in an interdisciplinary team, a
survey of disengaged contributors about their motivations for contribution,
reasons for disengagement, and suggestions for sustaining the communities, and
finally, an analysis of cross-referenced issues and pull requests to understand
best practices for collaboration on the ecosystem level. Our study reveals the
implications of major challenges for sustaining scientific OSS and proposes
concrete suggestions for tackling these challenges
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