39,574 research outputs found
Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Efforts
This Innovative Practice Full Paper presents an approach of using software
development artifacts to gauge student behavior and the effectiveness of
changes to curriculum design. There is an ongoing need to adapt university
courses to changing requirements and shifts in industry. As an educator it is
therefore vital to have access to methods, with which to ascertain the effects
of curriculum design changes. In this paper, we present our approach of
analyzing software repositories in order to gauge student behavior during
project work. We evaluate this approach in a case study of a university
undergraduate software development course teaching agile development
methodologies. Surveys revealed positive attitudes towards the course and the
change of employed development methodology from Scrum to Kanban. However,
surveys were not usable to ascertain the degree to which students had adapted
their workflows and whether they had done so in accordance with course goals.
Therefore, we analyzed students' software repository data, which represents
information that can be collected by educators to reveal insights into learning
successes and detailed student behavior. We analyze the software repositories
created during the last five courses, and evaluate differences in workflows
between Kanban and Scrum usage
A Case Study Exploring Organizational Development and Performance Management in the Operational Infrastructure of a Professional Working Organization, Using Academic Constructs
Curriculum, as a concept, has been historically associated with traditional schooling, but the reality is that its application extends to many arenas beyond academia. Through the case study lens, this dissertation utilized the ideologies of curricular theorists John Dewey, John Franklin Bobbitt, and Ralph Tyler to explore how intended, enacted, and assessed curricula phases can integrate into a professional working organization’s comprehensive functionality and materialize into the planning and implementation of its operational infrastructure. Following content analysis of a selected institution’s operational system, using closed codes, a descriptive comprehensive curriculum was designed to address the research purpose of understanding employee performance and organizational outcomes. Findings indicated that curricular phases are inherently embedded into the organizational development and performance management of nonacademic spaces; moreover, the framework of an organization’s operational infrastructure consists largely of curriculum elements. The primary research implication invokes being able to manage the efficiency and effectiveness levels of (a) personnel unit performance and (b) the workplace environment, through curriculum analysis and prescription
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
Lightweight and static verification of UML executable models
Executable models play a key role in many software development methods by facilitating the (semi)automatic implementation/execution of the software system under development. This is possible because executable models promote a complete and fine-grained specification of the system behaviour. In this context, where models are the basis of the whole development process, the quality of the models has a high impact on the final quality of software systems derived from them. Therefore, the existence of methods to verify the correctness of executable models is crucial. Otherwise, the quality of the executable models (and in turn the quality of the final system generated from them) will be compromised. In this paper a lightweight and static verification method to assess the correctness of executable models is proposed. This method allows us to check whether the operations defined as part of the behavioural model are able to be executed without breaking the integrity of the structural model and returns a meaningful feedback that helps repairing the detected inconsistencies.Peer ReviewedPostprint (author's final draft
Exploring AI Tool's Versatile Responses: An In-depth Analysis Across Different Industries and Its Performance Evaluation
AI Tool is a large language model (LLM) designed to generate human-like
responses in natural language conversations. It is trained on a massive corpus
of text from the internet, which allows it to leverage a broad understanding of
language, general knowledge, and various domains. AI Tool can provide
information, engage in conversations, assist with tasks, and even offer
creative suggestions. The underlying technology behind AI Tool is a transformer
neural network. Transformers excel at capturing long-range dependencies in
text, making them well-suited for language-related tasks. AI Tool has 175
billion parameters, making it one of the largest and most powerful LLMs to
date. This work presents an overview of AI Tool's responses on various sectors
of industry. Further, the responses of AI Tool have been cross-verified with
human experts in the corresponding fields. To validate the performance of AI
Tool, a few explicit parameters have been considered and the evaluation has
been done. This study will help the research community and other users to
understand the uses of AI Tool and its interaction pattern. The results of this
study show that AI Tool is able to generate human-like responses that are both
informative and engaging. However, it is important to note that AI Tool can
occasionally produce incorrect or nonsensical answers. It is therefore
important to critically evaluate the information that AI Tool provides and to
verify it from reliable sources when necessary. Overall, this study suggests
that AI Tool is a promising new tool for natural language processing, and that
it has the potential to be used in a wide variety of applications
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
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